From 5b3bf89f1a2f00a86f19c19054ef13eaeec5c110 Mon Sep 17 00:00:00 2001 From: Lisa Dunlap Date: Thu, 2 May 2024 00:51:33 -0700 Subject: [PATCH 01/17] started llama3 blog post --- blog/2024-05-01-llama3.md | 134 ++++++++++++++++++ .../llama3/llama_sentiment_distribution.png | Bin 0 -> 1171201 bytes .../images/blog/llama3/sentiment_win_rate.png | Bin 0 -> 1292448 bytes public/images/blog/llama3/topic_winrate.png | Bin 0 -> 1371513 bytes 4 files changed, 134 insertions(+) create mode 100644 blog/2024-05-01-llama3.md create mode 100644 public/images/blog/llama3/llama_sentiment_distribution.png create mode 100644 public/images/blog/llama3/sentiment_win_rate.png create mode 100644 public/images/blog/llama3/topic_winrate.png diff --git a/blog/2024-05-01-llama3.md b/blog/2024-05-01-llama3.md new file mode 100644 index 00000000..2265e0c7 --- /dev/null +++ b/blog/2024-05-01-llama3.md @@ -0,0 +1,134 @@ +# Why is Llama 3 so good? Analyzing model performance and behavioral differences +On April 18th, Meta released Llama 3, their newest open-source large language model. Since then, the Chatbot Arena has seen over 15,000 battles against Llama 3-70B alone, and it has quickly risen to the top of the English leaderboard. This remarkable achievement by Meta is excellent news for the open-source community, and we believe this remarkable result deserves further analysis. In this blog post, we aim to provide more insight into why users often prefer Llama 3-70b to top-ranked models like GPT-4-Turbo, Gemini 1.5 Pro, and Claude 3 Opus. + +We investigate the following: +1. What types of questions are users asking? Do users prefer Llama 3 on certain types of questions? +2. How hard are these questions? Does the ranking change if the questions are easier/harder? +3. Are certain users or questions overrepresented? Do duplicate questions or rankings from a small number of users affect the win-rate? +4. Does Llama 3 have qualitative differences which make users like it more? + +We focus on battles consisting of llama-3-70b against 5 top-ranked models and reach the following conclusions: +1. Llama 3 beats other top-ranking models on open-ended writing problems and loses on more objective math and coding problems +2. As prompts get harder*, Llama 3’s win-rate drops significantly. +3, Deduplication or outliers do not significantly affect the win-rate +4. Qualitatively, llama 3’s outputs are pore positive, friendlier and more conversational than other models +5. There is a correlation between llama being more conversational and llama winning + + +

Figure 1: Llama 3-70b win-rate against top 5 models across topic clusters.

+ + + +## Analyzing win-rate across different types of questions + +**Topic Analysis.** We perform topic clustering on 10k user prompts from battles with Llama 3-70b to get a further understanding of the types of questions being asked (see arena-hard for further explanation of the topic clustering pipeline). In Figure X we see the win rate of Llama 3 against the other top5 models across different topic clusters of user prompts. We see that llamas win-rate is highest for clusters which contain open-ended writing questions while its lowest win rate happens for more objective tasks like C++ programming, as well as long context writing prompts. We see this conclusion proved further in the following section. + +**Win-rate VS prompt difficulty.** We employ our recently released pipeline which scores the difficulty of prompts to determine how Llama 3 compares to the other top models as prompts get harder. We define a set of ``hardness' ' criteria and use GPT-4-turbo to annotate each prompt from 0 to 7 to indicate how many of these criteria are satisfied (higher score indicates a harder prompt). Our 7 criteria are: + + + + + + + + + + + + + + + + + + + + + + + + + + +
Table 2: 7 Key "Hardness" Criteria
1. Specificity: Does the prompt ask for a specific output?
2. Domain Knowledge: Does the prompt cover one or more specific domains?
3. Complexity: Does the prompt have multiple levels of reasoning, components, or variables?
4. Problem-Solving: Does the prompt directly involve the AI to demonstrate active problem-solving skills?
5. Creativity: Does the prompt involve a level of creativity in approaching the problem?
6. Technical Accuracy: Does the prompt require technical accuracy in the response?
7. Real-world Application: Does the prompt relate to real-world applications?
+ +We score 1000 battles against the top 3 models on the leaderboard and plot win-rate VS prompt score in Figure X. We observe a significant drop in Llama 3's performance compared to the other top models, from high 50% win-rate to low 40%. We conclude that as prompts get harder, users prefer Llama 3 outputs much less than other models[^1]. + +[^1]: this scoring system may not capture all prompts which the average user the hardness criteria we define are not exhaustive and often allocate high scores to more objective problems like math and coding questions. + + +## The effect of overrepresented questions and judges + +**Effect of duplicate questions.** An interesting finding from our topic clustering was the existence of a "Sentences Ending with “Apple” cluster. From manual inspection we confirm that this cluster does indeed contain many variations on the question "write 10 sentences that end with the word “apple". This raises questions on if this repeated question is largely contributing to the overall ranking, which we explore in the following section. Using fuzzy string matching, we find that 88% (1730/1975) of the user prompts in battles between Llama 3 and the other top models are unique, and show in Table X that deduplication does not significantly affect Llama 3 win-rate, and only changes + + + +
+

Table X: Llama 3-70b battle counts and win rates against the top 5 models in the arena with and without duplication.

+ + + + + + + + + + + + + + + + + + + + + +
Model # battles # battles (deduplicated) Llama 3 win rate Llama 3 win rate (deduplicated)
GPT-4-Turbo 634 559 47.31% 46.35%
GPT-4 1106 116 112 50.60% 51.39%
GPT-4 0125 548 480 50.21% 50.39%
Claude 3 Opus 478 416 52.82% 52.80%
Gemini 1.5 199 163 58.46% 57.12%
+ + +**User analysis.** First we consider some basic judging statistics to check that judging behavior is similar between Claude-3-opus-20240229 and Llama 3-70b. + +Visually, there is no significant strange behavior as the number of votes per judge increases. In fact, it seems that as the number of votes per judge increases, the judge’s own battles roughly approach the actual global win rate. + +In order to limit the impact of user’s that vote many times we can take the mean of each judge’s win rate, thereby bounding the impact of each individual judge. In this case, we find this stratified win rate is still very similar to the original winrate, suggesting that very active judges are not skewing the result. + + +
+

Table 4. Detailed Engagement Metrics for LLMs (Timeframe: April 24 - May 1, 2023). The latest and detailed version here.

+ + + + + + + + + + + + + + + +
Model Battles Unique Judges Mean Votes per Judge Median Votes per Judge Max Votes per Judge Mean Judge Age Median Judge Age Max Judge Age
Llama3-70B-Instruct 12,719 7,591 1.68 1 65 1 hr 10 min 2 hr 12 min 2 days
Claude-3-opus-20240229 68,656 48,570 1.41 1 73 1 hr 57 min 1 hr 55 min 3 days
All Models All Time 749,205 316,372 2.37 1 591 8 hr 27 min 2 hr 23 min 295 days
+

*Judge age is defined as rating_date - min_rating_date(ip_address)

+ +## Qualitative differences between Llama3 outputs VS other models +From qualitative analysis of outputs between Llama3 and other models, we observe that Llama3 outputs are often more excited, positive, conversational, and friendly than other models. + +**Measuring sentiment.** To measure excitement, we assign a binary label to each output based on the presence of an exclamation point. For positivity, friendliness, and conversationality, we use GPT-3.5 as a judge to rate each output on a scale of 1-5. In a given battle, Llama 3 outputs are labeled as more excited, positive, conversational, or friendly if their score is higher than the opponent's. Figure X displays the distribution of these qualities across models, revealing that Llama 3 outputs generally exhibit higher levels of excitement, positivity, friendliness, and conversationality compared to their opponents. + + +

Figure X: Sentiment Distribution.

+ +**Is sentiment related to win-rate?** Figure X compares the sentiment qualities of Llama 3's outputs in battles it wins versus those it loses. The results suggest that winning outputs tend to be more conversational and slightly friendlier, indicating that engaging and positive communication may contribute to success in these competitive scenarios. +To further investigate this correlation, we used logistic regression to analyze how differences in exclamation, positivity, friendliness, and conversationality between outputs affect user preference. Conversationality had the greatest impact, increasing a model's odds of winning by 27.5% when it was more conversational than its opponent. Friendliness and excitement also positively influenced preferences, raising the odds by 15.9% and 22.0%, respectively. Positivity showed no statistically significant correlation with win-rate. However, the low pseudo R-squared values across all attributes suggest that while these factors are statistically significant, they only partially explain the variance in model selection, implying that other unmeasured factors also play important roles in shaping user choices. + + +

Figure X: Llama 3-sentiment VS win-rate.

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zn?D^~2XN}GMHqcfZMLu$uKCpxJCkzs*#c}p;svqFPT~;CI_!c?mI;vPNrpz?aMrHa zx3A4M%hL%Uqz)i!b!c@%vl(H22>wB8ik9HB4HwCUVDYF31yyY9%(f_-!$@BzlAu9F zR2uZ;VbAw)d^bELY5l@8`pqQ|dnX3Z+Uj8_cE}m$xP6f8rMU)@9-GB6=2G<^4pAn2 zPvVW~oDIUrCwnxcq=EHzj>;RxSU9yD6<48x4-b^q+u(XW;XhrWEnFz zqzMtxDv>C$)n+uEjzSnG1nmWksV0(bg_#R4Jx~4wt4kmiR-|Pwpiw3_*hJ7YY3AOM zG32`Nwkl&cikOevhBLk2eCjj#6TmJ2PM$^^2J#*?OagOxQo;KRC)HRiAnZ_eYz((1 z0k{f^`U#FxIm)oVrDqkX$Ssky>q#`tg!~C`l=PoY^w`SN`Uo-+uPzTl4llyuA-YH_6-e@OC}C zT@OR=z}xjO^lrS34{z7Q+x0N?4!m6tL+{4h`0#c;{C}^9*zEDAB!L?a@8NGST4B08 JaoJ~I{y*fcfF%F` literal 0 HcmV?d00001 From 168a3f6554035bfd71aac225a598564f3659c2c4 Mon Sep 17 00:00:00 2001 From: Lisa Dunlap Date: Thu, 2 May 2024 00:57:49 -0700 Subject: [PATCH 02/17] added more things --- blog/2024-05-01-llama3.md | 32 +++++++++++++++++++++++++++----- 1 file changed, 27 insertions(+), 5 deletions(-) diff --git a/blog/2024-05-01-llama3.md b/blog/2024-05-01-llama3.md index 2265e0c7..25a54eff 100644 --- a/blog/2024-05-01-llama3.md +++ b/blog/2024-05-01-llama3.md @@ -67,7 +67,7 @@ td {text-align: left}
-

Table X: Llama 3-70b battle counts and win rates against the top 5 models in the arena with and without duplication.

+

Table X: Llama 3-70b battle counts and win rates (excluding ties) against the top 5 models in the arena with and without duplication.

@@ -96,8 +96,7 @@ td {text-align: left} Visually, there is no significant strange behavior as the number of votes per judge increases. In fact, it seems that as the number of votes per judge increases, the judge’s own battles roughly approach the actual global win rate. -In order to limit the impact of user’s that vote many times we can take the mean of each judge’s win rate, thereby bounding the impact of each individual judge. In this case, we find this stratified win rate is still very similar to the original winrate, suggesting that very active judges are not skewing the result. - +[ADD IN EVAN FIGS HERE]

Table 4. Detailed Engagement Metrics for LLMs (Timeframe: April 24 - May 1, 2023). The latest and detailed version here.

@@ -119,6 +118,25 @@ In order to limit the impact of user’s that vote many times we can take the me

*Judge age is defined as rating_date - min_rating_date(ip_address)

+In order to limit the impact of user’s that vote many times we can take the mean of each judge’s win rate, thereby bounding the impact of each individual judge. In this case, we find this stratified win rate in Table X is still very similar to the original winrate, suggesting that very active judges are not skewing the result. + + +
+

Table 5. Model Win Rates (Timeframe: April 24 - May 1, 2023). The latest and detailed version here. Note that ties are counted as 0.5, with wins and losses as 1 and 0, respectively.

+ + + + + + + + + + + + +
Model Win rate Stratified Winrate
Llama3-70B-Instruct 0.541 0.543
Claude-3-opus-20240229 0.619 0.621
+ ## Qualitative differences between Llama3 outputs VS other models From qualitative analysis of outputs between Llama3 and other models, we observe that Llama3 outputs are often more excited, positive, conversational, and friendly than other models. @@ -128,7 +146,11 @@ From qualitative analysis of outputs between Llama3 and other models, we observe

Figure X: Sentiment Distribution.

**Is sentiment related to win-rate?** Figure X compares the sentiment qualities of Llama 3's outputs in battles it wins versus those it loses. The results suggest that winning outputs tend to be more conversational and slightly friendlier, indicating that engaging and positive communication may contribute to success in these competitive scenarios. -To further investigate this correlation, we used logistic regression to analyze how differences in exclamation, positivity, friendliness, and conversationality between outputs affect user preference. Conversationality had the greatest impact, increasing a model's odds of winning by 27.5% when it was more conversational than its opponent. Friendliness and excitement also positively influenced preferences, raising the odds by 15.9% and 22.0%, respectively. Positivity showed no statistically significant correlation with win-rate. However, the low pseudo R-squared values across all attributes suggest that while these factors are statistically significant, they only partially explain the variance in model selection, implying that other unmeasured factors also play important roles in shaping user choices. +To further investigate this correlation, we used logistic regression to analyze how differences in exclamation, positivity, friendliness, and conversationality between outputs affect user preference. Conversationality had the greatest impact, increasing a model's odds of winning by 27.5% when it was more conversational than its opponent. Friendliness and excitement also positively influenced preferences, raising the odds by 15.9% and 22.0%, respectively. Positivity showed no statistically significant correlation with win-rate[^2]. + +[^2]: We observe low pseudo R-squared values across all attributes suggest that while these factors are statistically significant, they only partially explain the variance in model selection, implying that other unmeasured factors also play important roles in shaping user choices. -

Figure X: Llama 3-sentiment VS win-rate.

\ No newline at end of file +

Figure X: Llama 3-sentiment VS win-rate.

+ +## Conclusion \ No newline at end of file From 522068e2eff429c5dcc1dbb57fba01a5daed285c Mon Sep 17 00:00:00 2001 From: Lisa Dunlap Date: Thu, 2 May 2024 01:24:22 -0700 Subject: [PATCH 03/17] it doesnt crash now --- blog/2024-05-01-llama3.md | 14 ++++++++++---- content/about.md | 2 +- 2 files changed, 11 insertions(+), 5 deletions(-) diff --git a/blog/2024-05-01-llama3.md b/blog/2024-05-01-llama3.md index 25a54eff..44b809f4 100644 --- a/blog/2024-05-01-llama3.md +++ b/blog/2024-05-01-llama3.md @@ -1,4 +1,10 @@ -# Why is Llama 3 so good? Analyzing model performance and behavioral differences +--- +title: "Why is Llama 3 so good? Analyzing when and why users prefer llama 3" +author: "Lisa Dunlap, Evan Frick, Tianle Li, Isaac Ong, Wei-Lin Chiang" +date: "May 2, 2024" +previewImg: /images/blog/llama3/llama3_blog_cover.png +--- + On April 18th, Meta released Llama 3, their newest open-source large language model. Since then, the Chatbot Arena has seen over 15,000 battles against Llama 3-70B alone, and it has quickly risen to the top of the English leaderboard. This remarkable achievement by Meta is excellent news for the open-source community, and we believe this remarkable result deserves further analysis. In this blog post, we aim to provide more insight into why users often prefer Llama 3-70b to top-ranked models like GPT-4-Turbo, Gemini 1.5 Pro, and Claude 3 Opus. We investigate the following: @@ -68,7 +74,7 @@ td {text-align: left}

Table X: Llama 3-70b battle counts and win rates (excluding ties) against the top 5 models in the arena with and without duplication.

- +
@@ -100,7 +106,7 @@ Visually, there is no significant strange behavior as the number of votes per ju

Table 4. Detailed Engagement Metrics for LLMs (Timeframe: April 24 - May 1, 2023). The latest and detailed version here.

-
Model # battles # battles (deduplicated) Llama 3 win rate Llama 3 win rate (deduplicated)
+
@@ -123,7 +129,7 @@ In order to limit the impact of user’s that vote many times we can take the me

Table 5. Model Win Rates (Timeframe: April 24 - May 1, 2023). The latest and detailed version here. Note that ties are counted as 0.5, with wins and losses as 1 and 0, respectively.

-
Model Battles Unique Judges Mean Votes per Judge Median Votes per Judge Max Votes per Judge Mean Judge Age Median Judge Age Max Judge Age
+
diff --git a/content/about.md b/content/about.md index 7b441d44..93ad0e40 100644 --- a/content/about.md +++ b/content/about.md @@ -8,7 +8,7 @@ We aim to make large models accessible to everyone by co-development of open mod ### Members **Student Team** -[Lianmin Zheng](https://lmzheng.net/), [Ying Sheng](https://sites.google.com/view/yingsheng/home), [Wei-Lin Chiang](https://infwinston.github.io/), [Shiyi Cao](https://shiyicao.com/), [Tianle Li](https://codingwithtim.github.io/), [Christopher Chou](https://github.com/BabyChouSr), [Dacheng Li](https://dachengli1.github.io/), [Zhuohan Li](https://people.eecs.berkeley.edu/~zhuohan/), [Zi Lin](https://zi-lin.com/), [Zhanghao Wu](https://zhanghaowu.me/), [Shuo Yang](https://github.com/andy-yang-1), [Siyuan Zhuang](https://github.com/suquark), [Yonghao Zhuang](https://github.com/ZYHowell) +[Lianmin Zheng](https://lmzheng.net/), [Ying Sheng](https://sites.google.com/view/yingsheng/home), [Wei-Lin Chiang](https://infwinston.github.io/), [Shiyi Cao](https://shiyicao.com/), [Tianle Li](https://codingwithtim.github.io/), [Christopher Chou](https://github.com/BabyChouSr), [Dacheng Li](https://dachengli1.github.io/), [Zhuohan Li](https://people.eecs.berkeley.edu/~zhuohan/), [Zi Lin](https://zi-lin.com/), [Zhanghao Wu](https://zhanghaowu.me/), [Shuo Yang](https://github.com/andy-yang-1), [Siyuan Zhuang](https://github.com/suquark), [Yonghao Zhuang](https://github.com/ZYHowell), [Lisa Dunlap](https://lisabdunlap.com) **Faculty Team** [Joseph E. Gonzalez](https://people.eecs.berkeley.edu/~jegonzal/), [Ion Stoica](https://people.eecs.berkeley.edu/~istoica/), [Eric P. Xing](http://www.cs.cmu.edu/~epxing/), [Hao Zhang](https://people.eecs.berkeley.edu/~hao/) From 245d3abb6307a58b1d7d194c22a6cb7e3c3c62c6 Mon Sep 17 00:00:00 2001 From: Lisa Dunlap Date: Thu, 2 May 2024 12:23:36 -0700 Subject: [PATCH 04/17] added more pics --- .../images/blog/llama3/llama3_blog_cover.png | Bin 0 -> 361032 bytes public/images/blog/llama3/topic_winrate.png | Bin 1371513 -> 1369163 bytes 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 public/images/blog/llama3/llama3_blog_cover.png diff --git a/public/images/blog/llama3/llama3_blog_cover.png b/public/images/blog/llama3/llama3_blog_cover.png new file mode 100644 index 0000000000000000000000000000000000000000..fe3875844430e45713a19e39f27c858f2dcd4ef8 GIT binary patch literal 361032 zcmafaV{~Rgvu-l6ZQHgnv5kps+n!8pJDJ$FZQHh;yvcj#JLlYUf8IZ}cJJO*wW^-3 zUQhS#R*{yF5Dta_(G(X|(oo_i#;yeeVcv(%0i^{22LTsk!HoDxNqphyf}C}kng(@ z$aM?cR6Gkf7P<(?2Z{j0zYKst05E@d8rT@n0z~=_`A&QPr}7zq88GqH2TZu90$2cc 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z1Lf73_*+}b04DwUFY$&Z1Q+W7$3GTL&$;TE-{AefV~}&hK4;48FO>0L3@{t;o27Ef w`d3Vqe_uVo_|HH5`40YE2mi14g6VIm1ta$aA6mA}kDhn1iG2Ne(1l>h($ From 8cc10f8912d70beb6ccf814665ef5aa0454d8f7d Mon Sep 17 00:00:00 2001 From: efrick2002 Date: Tue, 7 May 2024 12:10:48 -0700 Subject: [PATCH 05/17] added images and such --- blog/2024-05-01-llama3.md | 18 +- .../images/blog/llama3/claude_num_judge.png | Bin 0 -> 1073871 bytes public/images/blog/llama3/dtree.svg | 217 ++++++++++++++++++ public/images/blog/llama3/llama_num_judge.png | Bin 0 -> 841563 bytes 4 files changed, 233 insertions(+), 2 deletions(-) create mode 100644 public/images/blog/llama3/claude_num_judge.png create mode 100644 public/images/blog/llama3/dtree.svg create mode 100644 public/images/blog/llama3/llama_num_judge.png diff --git a/blog/2024-05-01-llama3.md b/blog/2024-05-01-llama3.md index 44b809f4..935c92a0 100644 --- a/blog/2024-05-01-llama3.md +++ b/blog/2024-05-01-llama3.md @@ -62,6 +62,13 @@ We score 1000 battles against the top 3 models on the leaderboard and plot win-r [^1]: this scoring system may not capture all prompts which the average user the hardness criteria we define are not exhaustive and often allocate high scores to more objective problems like math and coding questions. +We can further analyze to a more granular level which types of questions affect win-rate by fitting a decision tree on the 7 binary columns representing if a given prompt has satisfied each of the criteria above. From this decision tree we can segment prompts into criteria subsets such that Llama3-70b-Instruct performs very well or very poorly. The tree shown in figure X shows us which subsets change the model’s win-rate the most when conditioned on. + + +

Llama3-70b-Instruct win-rate conditioned on hierarchical prompt criteria subsets as fitted using a standard decision tree algorithm. *English battles against top models (claude-3-opus-20240229, gpt-4-0125-preview, gpt-4-1106-preview, gpt-4-turbo-2024-04-09, gemini-1.5-pro-api-0409-preview).

+ +The first thing to notice is that “Specificity” is the root node of the tree, suggesting that this criteria already divides Llama3-70b-Instruct’s performance into its strengths and weaknesses. It supports our initial findings above that Llama3-70b-Instruct is stronger on open-ended questions (not specific) rather than more objective tasks. We can traverse further down the tree and see that Llama3-70b-Instruct is quite strong on open-ended creative questions (see the blue path), reaching around a 60% win-rate against these top models. Following the orange path, we can notice that Llama3-70b-Instruct has a much lower win-rate against top models when answering specific reasoning-based questions. + ## The effect of overrepresented questions and judges @@ -102,7 +109,14 @@ td {text-align: left} Visually, there is no significant strange behavior as the number of votes per judge increases. In fact, it seems that as the number of votes per judge increases, the judge’s own battles roughly approach the actual global win rate. -[ADD IN EVAN FIGS HERE] +
+
+ Image 1 + Image 2 +

Fig X: Average Winrate for each judge vs Number of judgments made by each judge.

+
+
+

Table 4. Detailed Engagement Metrics for LLMs (Timeframe: April 24 - May 1, 2023). The latest and detailed version here.

@@ -156,7 +170,7 @@ To further investigate this correlation, we used logistic regression to analyze [^2]: We observe low pseudo R-squared values across all attributes suggest that while these factors are statistically significant, they only partially explain the variance in model selection, implying that other unmeasured factors also play important roles in shaping user choices. - +

Figure X: Llama 3-sentiment VS win-rate.

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z{hu5%q~FaVyFlLWZNJm!kth2F;qU(8{T@KSuTSouxb*wlhy4$w?AI2(+wY0Uen~FK z`|Zp1TM*&@yj{%yyC!e9${W^1PVVhidBd{2jVfuO^fn&tGsDd-bR%-tjgQ1 z@`gou8&%%4C~vpQn^xs*RC&Xyyxl5qSd_O>}Frt@4IN`TrhO bdUS^kRR7c1e) Date: Tue, 7 May 2024 12:12:59 -0700 Subject: [PATCH 06/17] oops, fixed path --- blog/2024-05-01-llama3.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/blog/2024-05-01-llama3.md b/blog/2024-05-01-llama3.md index 935c92a0..6e312b09 100644 --- a/blog/2024-05-01-llama3.md +++ b/blog/2024-05-01-llama3.md @@ -170,7 +170,7 @@ To further investigate this correlation, we used logistic regression to analyze [^2]: We observe low pseudo R-squared values across all attributes suggest that while these factors are statistically significant, they only partially explain the variance in model selection, implying that other unmeasured factors also play important roles in shaping user choices. - +

Figure X: Llama 3-sentiment VS win-rate.

## Conclusion \ No newline at end of file From 7e9d70f65345e01bcadf08946fce616b83ef6a34 Mon Sep 17 00:00:00 2001 From: efrick2002 Date: Tue, 7 May 2024 14:05:35 -0700 Subject: [PATCH 07/17] update text --- blog/2024-05-01-llama3.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/blog/2024-05-01-llama3.md b/blog/2024-05-01-llama3.md index 6e312b09..0495a3a7 100644 --- a/blog/2024-05-01-llama3.md +++ b/blog/2024-05-01-llama3.md @@ -67,8 +67,7 @@ We can further analyze to a more granular level which types of questions affect

Llama3-70b-Instruct win-rate conditioned on hierarchical prompt criteria subsets as fitted using a standard decision tree algorithm. *English battles against top models (claude-3-opus-20240229, gpt-4-0125-preview, gpt-4-1106-preview, gpt-4-turbo-2024-04-09, gemini-1.5-pro-api-0409-preview).

-The first thing to notice is that “Specificity” is the root node of the tree, suggesting that this criteria already divides Llama3-70b-Instruct’s performance into its strengths and weaknesses. It supports our initial findings above that Llama3-70b-Instruct is stronger on open-ended questions (not specific) rather than more objective tasks. We can traverse further down the tree and see that Llama3-70b-Instruct is quite strong on open-ended creative questions (see the blue path), reaching around a 60% win-rate against these top models. Following the orange path, we can notice that Llama3-70b-Instruct has a much lower win-rate against top models when answering specific reasoning-based questions. - +The first thing to notice is that “Specificity” is the root node of the tree, suggesting that this criteria most immediately divides Llama3-70b-Instruct’s performance into its strengths and weaknesses. It supports our initial findings above that Llama3-70b-Instruct is stronger on open-ended tasks rather than more closed-ended tasks. We can traverse further down the tree and see that Llama3-70b-Instruct is quite strong on open-ended creative questions (see the blue path), reaching around a 60% win-rate against these top models. Emperically, these types of questions are often writing and brainstorming style questions. For example two prompts where Llama-3-70B-Instruct won are: "Write the first chapter of a novel." and "Could you provide two story suggestions for children that promote altruism? ". On the other hand, following the orange path, we can notice that Llama3-70b-Instruct has a lower win-rate against top models when answering close-ended, non-real-world, reasoning-based questions. These questions are often logic puzzles and math word word problems. Two examples where Llama-3-70B-Instruct won are: "123x = -4x * 2 - 65" and "There are two ducks in front of a duck, two ducks behind a duck and a duck in the middle. How many ducks are there?" ## The effect of overrepresented questions and judges From 999ba13749d6f994454a67b2c408681d67820d4b Mon Sep 17 00:00:00 2001 From: Lisa Dunlap Date: Tue, 7 May 2024 17:13:35 -0700 Subject: [PATCH 08/17] upadted topic cluster results --- blog/2024-05-01-llama3.md | 70 +++++++++--------- .../llama3/llama_sentiment_distribution.png | Bin 1171201 -> 593363 bytes .../images/blog/llama3/sentiment_win_rate.png | Bin 1292448 -> 528579 bytes public/images/blog/llama3/topic_win_rate.png | Bin 0 -> 619649 bytes public/images/blog/llama3/topic_winrate.png | Bin 1369163 -> 0 bytes 5 files changed, 34 insertions(+), 36 deletions(-) create mode 100644 public/images/blog/llama3/topic_win_rate.png delete mode 100644 public/images/blog/llama3/topic_winrate.png diff --git a/blog/2024-05-01-llama3.md b/blog/2024-05-01-llama3.md index 0495a3a7..583b6ba2 100644 --- a/blog/2024-05-01-llama3.md +++ b/blog/2024-05-01-llama3.md @@ -14,26 +14,26 @@ We investigate the following: 4. Does Llama 3 have qualitative differences which make users like it more? We focus on battles consisting of llama-3-70b against 5 top-ranked models and reach the following conclusions: -1. Llama 3 beats other top-ranking models on open-ended writing problems and loses on more objective math and coding problems -2. As prompts get harder*, Llama 3’s win-rate drops significantly. +1. Llama 3 beats other top-ranking models on open-ended writing and create problems and loses on more close-ended math and coding problems +2. As prompts get harder, Llama 3’s win-rate against top-tier models drops significantly 3, Deduplication or outliers do not significantly affect the win-rate -4. Qualitatively, llama 3’s outputs are pore positive, friendlier and more conversational than other models -5. There is a correlation between llama being more conversational and llama winning +4. Qualitatively, Llama 3’s outputs are friendlier and more conversational than other models, and these traits appear more often in battles that llama 3 wins - -

Figure 1: Llama 3-70b win-rate against top 5 models across topic clusters.

+ + +

Figure 1. LLama3-70b win-rate(excluding ties) against top 5 models across prompt topics. * denotes that the category contains less than 50 battles.

## Analyzing win-rate across different types of questions -**Topic Analysis.** We perform topic clustering on 10k user prompts from battles with Llama 3-70b to get a further understanding of the types of questions being asked (see arena-hard for further explanation of the topic clustering pipeline). In Figure X we see the win rate of Llama 3 against the other top5 models across different topic clusters of user prompts. We see that llamas win-rate is highest for clusters which contain open-ended writing questions while its lowest win rate happens for more objective tasks like C++ programming, as well as long context writing prompts. We see this conclusion proved further in the following section. +**Topic Analysis.** We utilize an LLM labeler (Llama 3-70b) to categorize user prompts into a pre-established taxonomy of topics and visualize the win-rate of Llama3-70b against the other top models across. We see that Llama’s win rate is highest for open-ended and creative tasks like brainstorming and writing, and lowest for more close-ended technical tasks like math and translation. Interestingly Llama 3 achieves the highest win-rate over data processing tasks which mainly consist of parsing and dataframe operations, but as this category has only 19 examples this remains inconclusive. -**Win-rate VS prompt difficulty.** We employ our recently released pipeline which scores the difficulty of prompts to determine how Llama 3 compares to the other top models as prompts get harder. We define a set of ``hardness' ' criteria and use GPT-4-turbo to annotate each prompt from 0 to 7 to indicate how many of these criteria are satisfied (higher score indicates a harder prompt). Our 7 criteria are: +**Win-rate VS prompt difficulty.** We employ our [recently released pipeline](https://lmsys.org/blog/2024-04-19-arena-hard/) which scores the difficulty of prompts to determine how Llama 3 compares to the other top models as prompts get harder. We define a set of ``hardness' ' criteria and use GPT-4-turbo to annotate each prompt from 0 to 7 to indicate how many of these criteria are satisfied (higher score indicates a harder prompt). Our 7 criteria are: - + @@ -58,47 +58,46 @@ We focus on battles consisting of llama-3-70b against 5 top-ranked models and re
Table 2: 7 Key "Hardness" Criteria
1. Specificity: Does the prompt ask for a specific output?
-We score 1000 battles against the top 3 models on the leaderboard and plot win-rate VS prompt score in Figure X. We observe a significant drop in Llama 3's performance compared to the other top models, from high 50% win-rate to low 40%. We conclude that as prompts get harder, users prefer Llama 3 outputs much less than other models[^1]. - -[^1]: this scoring system may not capture all prompts which the average user the hardness criteria we define are not exhaustive and often allocate high scores to more objective problems like math and coding questions. +We score 1000 battles against the top 3 models on the leaderboard and plot win-rate VS prompt score in Figure X. We observe a significant drop in Llama 3's performance compared to the other top models, from high 50% win-rate to low 40%. We conclude that as more of these ``hardness'' criteria are met, Llama 3's win rate drop rapidly compared to other models. Notw that these criteria may not be exhaustive, see [the blog](https://lmsys.org/blog/2024-04-19-arena-hard/) for further discussion. We can further analyze to a more granular level which types of questions affect win-rate by fitting a decision tree on the 7 binary columns representing if a given prompt has satisfied each of the criteria above. From this decision tree we can segment prompts into criteria subsets such that Llama3-70b-Instruct performs very well or very poorly. The tree shown in figure X shows us which subsets change the model’s win-rate the most when conditioned on. -

Llama3-70b-Instruct win-rate conditioned on hierarchical prompt criteria subsets as fitted using a standard decision tree algorithm. *English battles against top models (claude-3-opus-20240229, gpt-4-0125-preview, gpt-4-1106-preview, gpt-4-turbo-2024-04-09, gemini-1.5-pro-api-0409-preview).

+

Figure 3. Llama3-70b-Instruct win-rate conditioned on hierarchical prompt criteria subsets as fitted using a standard decision tree algorithm. *English battles against top models (claude-3-opus-20240229, gpt-4-0125-preview, gpt-4-1106-preview, gpt-4-turbo-2024-04-09, gemini-1.5-pro-api-0409-preview).

The first thing to notice is that “Specificity” is the root node of the tree, suggesting that this criteria most immediately divides Llama3-70b-Instruct’s performance into its strengths and weaknesses. It supports our initial findings above that Llama3-70b-Instruct is stronger on open-ended tasks rather than more closed-ended tasks. We can traverse further down the tree and see that Llama3-70b-Instruct is quite strong on open-ended creative questions (see the blue path), reaching around a 60% win-rate against these top models. Emperically, these types of questions are often writing and brainstorming style questions. For example two prompts where Llama-3-70B-Instruct won are: "Write the first chapter of a novel." and "Could you provide two story suggestions for children that promote altruism? ". On the other hand, following the orange path, we can notice that Llama3-70b-Instruct has a lower win-rate against top models when answering close-ended, non-real-world, reasoning-based questions. These questions are often logic puzzles and math word word problems. Two examples where Llama-3-70B-Instruct won are: "123x = -4x * 2 - 65" and "There are two ducks in front of a duck, two ducks behind a duck and a duck in the middle. How many ducks are there?" ## The effect of overrepresented questions and judges -**Effect of duplicate questions.** An interesting finding from our topic clustering was the existence of a "Sentences Ending with “Apple” cluster. From manual inspection we confirm that this cluster does indeed contain many variations on the question "write 10 sentences that end with the word “apple". This raises questions on if this repeated question is largely contributing to the overall ranking, which we explore in the following section. Using fuzzy string matching, we find that 88% (1730/1975) of the user prompts in battles between Llama 3 and the other top models are unique, and show in Table X that deduplication does not significantly affect Llama 3 win-rate, and only changes +**Effect of duplicate questions.** Using fuzzy string matching, we find that ~9% (6658/7327) of the user prompts in battles between Llama 3 and the other top models are unique, and show in Table 1 that deduplication does not significantly affect Llama3 win-rate, and only changes +
-

Table X: Llama 3-70b battle counts and win rates (excluding ties) against the top 5 models in the arena with and without duplication.

+

Table 1: Llama 3-70b battle stats.

- + - + - + - + - + - +
Model # battles # battles (deduplicated) Llama 3 win rate Llama 3 win rate (deduplicated)Model # battles # battles no tie # battles (dedup, no tie) Llama 3 win rate Llama 3 win rate (dedup, no tie)
GPT-4-Turbo 634 559 47.31% 46.35%Claude 3 Opus 1959 1328 1171 51.28% 51.58%
GPT-4 1106 116 112 50.60% 51.39%Gemini 1.5 2413 1620 1437 50.06% 49.48%
GPT-4 0125 548 480 50.21% 50.39%GPT-4 0125 1271 881 779 48.58% 49.04%
Claude 3 Opus 478 416 52.82% 52.80%GPT-4 1106 526 349 307 50.72% 52.12%
Gemini 1.5 199 163 58.46% 57.12%GPT-4-Turbo 2097 1437 1287 47.74% 47.73%
@@ -106,19 +105,19 @@ td {text-align: left} **User analysis.** First we consider some basic judging statistics to check that judging behavior is similar between Claude-3-opus-20240229 and Llama 3-70b. -Visually, there is no significant strange behavior as the number of votes per judge increases. In fact, it seems that as the number of votes per judge increases, the judge’s own battles roughly approach the actual global win rate. +As seem in Figure 4, there is no significant strange behavior as the number of votes per judge increases. In fact, it seems that as the number of votes per judge increases, the judge’s own battles roughly approach the actual global win rate.
Image 1 Image 2 -

Fig X: Average Winrate for each judge vs Number of judgments made by each judge.

+

Fig 4: Average Winrate for each judge vs Number of judgments made by each judge.


-

Table 4. Detailed Engagement Metrics for LLMs (Timeframe: April 24 - May 1, 2023). The latest and detailed version here.

+

Table 2. Detailed Engagement Metrics for LLMs (Timeframe: April 24 - May 1, 2023). The latest and detailed version here.

@@ -137,11 +136,11 @@ Visually, there is no significant strange behavior as the number of votes per ju

*Judge age is defined as rating_date - min_rating_date(ip_address)

-In order to limit the impact of user’s that vote many times we can take the mean of each judge’s win rate, thereby bounding the impact of each individual judge. In this case, we find this stratified win rate in Table X is still very similar to the original winrate, suggesting that very active judges are not skewing the result. +In order to limit the impact of user’s that vote many times we can take the mean of each judge’s win rate, thereby bounding the impact of each individual judge. In this case, we find this stratified win rate in Table 2 is still very similar to the original winrate, suggesting that very active judges are not skewing the result.
-

Table 5. Model Win Rates (Timeframe: April 24 - May 1, 2023). The latest and detailed version here. Note that ties are counted as 0.5, with wins and losses as 1 and 0, respectively.

+

Table 2. Model Win Rates (Timeframe: April 24 - May 1, 2023). The latest and detailed version here. Note that ties are counted as 0.5, with wins and losses as 1 and 0, respectively.

@@ -159,17 +158,16 @@ In order to limit the impact of user’s that vote many times we can take the me ## Qualitative differences between Llama3 outputs VS other models From qualitative analysis of outputs between Llama3 and other models, we observe that Llama3 outputs are often more excited, positive, conversational, and friendly than other models. -**Measuring sentiment.** To measure excitement, we assign a binary label to each output based on the presence of an exclamation point. For positivity, friendliness, and conversationality, we use GPT-3.5 as a judge to rate each output on a scale of 1-5. In a given battle, Llama 3 outputs are labeled as more excited, positive, conversational, or friendly if their score is higher than the opponent's. Figure X displays the distribution of these qualities across models, revealing that Llama 3 outputs generally exhibit higher levels of excitement, positivity, friendliness, and conversationality compared to their opponents. +**Measuring sentiment.** To measure excitement, we assign a binary label to each output based on the presence of an exclamation point. For positivity, friendliness, and conversationality, we use GPT-3.5 as a judge to rate each output on a scale of 1-5. In a given battle, Llama 3 outputs are labeled as more excited, positive, conversational, or friendly if their score is higher than the opponent's. Figure 5 displays the distribution of these qualities across models, revealing that Llama 3 outputs generally exhibit higher levels of excitement, positivity, friendliness, and conversationality compared to their opponents. - -

Figure X: Sentiment Distribution.

+ +

Figure 5: Sentiment Distribution.

-**Is sentiment related to win-rate?** Figure X compares the sentiment qualities of Llama 3's outputs in battles it wins versus those it loses. The results suggest that winning outputs tend to be more conversational and slightly friendlier, indicating that engaging and positive communication may contribute to success in these competitive scenarios. -To further investigate this correlation, we used logistic regression to analyze how differences in exclamation, positivity, friendliness, and conversationality between outputs affect user preference. Conversationality had the greatest impact, increasing a model's odds of winning by 27.5% when it was more conversational than its opponent. Friendliness and excitement also positively influenced preferences, raising the odds by 15.9% and 22.0%, respectively. Positivity showed no statistically significant correlation with win-rate[^2]. +**Is sentiment related to win-rate?** Figure 6 compares the sentiment qualities of Llama 3's outputs in battles it wins versus those it loses. The results suggest that winning outputs tend to be more conversational and slightly friendlier, indicating that engaging and positive communication may contribute to success in these competitive scenarios. -[^2]: We observe low pseudo R-squared values across all attributes suggest that while these factors are statistically significant, they only partially explain the variance in model selection, implying that other unmeasured factors also play important roles in shaping user choices. + +

Figure 6: Llama 3-sentiment VS win-rate.

- -

Figure X: Llama 3-sentiment VS win-rate.

+## Conclusion -## Conclusion \ No newline at end of file +From the beginning, our mission has been to advance LLM development and understanding. As such, we believe that utilizing the data we have to perform more in-depth analysis into these models behaviors as well as use preferences is invaluable to both model users and model developers. While we use Llama 3 as a case study, we are excited to explore analyzing other models in the future to provide more insight to the community. \ No newline at end of file diff --git a/public/images/blog/llama3/llama_sentiment_distribution.png b/public/images/blog/llama3/llama_sentiment_distribution.png index df293b6e569f8d0d9e6cb3174fe1d51465f9ea78..08cb51e07f4800356e88bb082ef2f77b1db21e64 100644 GIT binary patch literal 593363 zcmeFad03C>`#x^Q3^T?uC`($5trU@jc6$mXZQ3ccsib{pjHMlEK?&`PR3 zDcbk6@AW;e=8Z8Q^ErIc*cxhTz;B`ucU2^nZR%SCi{+=$QYhBF+Xwc)QrO?OwOJ$G&!TAvC{0+ zIb$6|177YO+*>!S)HX3WYb>y7lm6##;5Iy`wTW%*YExX~=d%YCjc3eI*-!s{$8dAc zt{LCWm?6G*m#l?P$2&`t(njCm9w~0+ogwE}9b0wp!Bu7Doz>yrb8^gC6|%B!(74rE zxwGV{l2)8V>0~zNK3}7ak18bg?JQjQodUz#A9l(9`qTH)tCerAx%S7J4ACZU=Zvx? zH&&$ooT}Db{rrM^vt`Zp-Af8=dtY*F+O%l_?&tsXOZSp&5$^i`_Dfb`EzAGyCuhvq z8Tj81{ugWa-=+Q+i}l~&`ZtR;W5$1j>)))`f066otk!>n>tC(be}n5^E!TgM>)$Nb ze}n7atk!>#>tC(be}n5^E!Y2l$Q9;i^65?=TDKPZi#0Sf$m(?F-Qr!%#l`h{Uq^^( zd)dB%yTajdkM*k__f+ad2PR!j&aVCK(~ten-_Hx@_7#Y-CDd5yd6eBXudy`hP8$kJ z`ssMVR;_%WW!ugi^C&axt`W|5Wx3PzfJJNFmM_=nNWZ**jn#eK;jp4g>#ijBS#y_N zYg#pKAt zrB3r%1Ts%tVOes8#q1cfWyAHtz=K{d^75)%jlZoeocEOxZT;J)edDj`;Q;|_nhN~b zqBAC~^6m-?icItuL++>zoEI1v7;~=i)v=^=r&9Zi4lZ0+h}&u@3Owj@_Sw%y&wggl zTqwr8FjQ98%k0~?@Z|12yHCo;lPEr$;g8;1b&eJ-w+}q={BmK4w7+&qi1gMo6`Qfh zntrx}4z@$@whz8DuhlLMlZ(@@+9qJuv1~Sr$r1XV)|213`#-;QK8|zaMi=v1+wE!= zA1+x;43ul;dTtbFpYW)wtFu6s)TIs8IMq%~4i$#Uh3^>ai1jzDO*bl$7F+B4^%m{i zJC{vFXx-u4H%<2YZn5lbXsJ#zE?WNSlWV0vFX{QKQ_Ih&sHlwl-p>t}j~u;Ys2r`9 zrd=MTYSLD+-j%<}d&gjfroU#6>-Fo`W5UmUwL)QeN8V(-elPXuy7c?tNp-uCQ{$~t z_KpsV*|SFn+THWtu5T}ob_0Cmv=sXL@3}JHD4)MM^T~yo{)g{u6A%9CnxC(=(^vk> zUHZx?DKWi$`}Vr8FH3Z4uf*QHacACcaTT2$-q9WrTNf-kp+k%NLE`g||NRf=GrIlV zL>$G~Y`31y^InMbkP-;4l#f26 zr3C)-Pb*or=~psrO6xyLaTPOEW1VwL>+bT4v*tPCqXb&Fe)|&m>iO|WEw~RA%SI2Qw$R{pzCV8ZBBl}(0W<&ePeq;W(HX8kFJ71V z$IpxF66;-^!lTm#B!zl117goBb=^3~?T}>LEU(k)&7)aYq3L$mc;Q1Xd{F2sJ+F<* zp4p4O{Sh;cn-5|+vb*i4#t@H7AEc%Vwfd-PbX6zun0HmTltn66pUd6Y!-wifk?hqie#>+#8rSO20(_mz}Er%*E_11>EY>_ zEh8n&m}WiXx?Uz|ucf7Bk$6h=_()&<@4s+gU16BYQx>779S*E>v&Mo&3TPK@O|yY!2D{1j z|FEWVZS0?PT!Ut{rE*!|EJr*mG8M)wMQn@Wv?)Fe)jCy-uznosfqgc7Ze{7i8_4&BB1#JXN&*+iQof?qul$)5)Q|22Dt5NOg90-qHJFwZC3@ zR8KwI)Q)76)

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From 957d3406e37d11fc154feddfa94fb29d3ed759dd Mon Sep 17 00:00:00 2001 From: efrick2002 Date: Tue, 7 May 2024 19:03:56 -0700 Subject: [PATCH 09/17] small fixes --- blog/2024-05-01-llama3.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/blog/2024-05-01-llama3.md b/blog/2024-05-01-llama3.md index 583b6ba2..10dd6e20 100644 --- a/blog/2024-05-01-llama3.md +++ b/blog/2024-05-01-llama3.md @@ -65,7 +65,7 @@ We can further analyze to a more granular level which types of questions affect

Figure 3. Llama3-70b-Instruct win-rate conditioned on hierarchical prompt criteria subsets as fitted using a standard decision tree algorithm. *English battles against top models (claude-3-opus-20240229, gpt-4-0125-preview, gpt-4-1106-preview, gpt-4-turbo-2024-04-09, gemini-1.5-pro-api-0409-preview).

-The first thing to notice is that “Specificity” is the root node of the tree, suggesting that this criteria most immediately divides Llama3-70b-Instruct’s performance into its strengths and weaknesses. It supports our initial findings above that Llama3-70b-Instruct is stronger on open-ended tasks rather than more closed-ended tasks. We can traverse further down the tree and see that Llama3-70b-Instruct is quite strong on open-ended creative questions (see the blue path), reaching around a 60% win-rate against these top models. Emperically, these types of questions are often writing and brainstorming style questions. For example two prompts where Llama-3-70B-Instruct won are: "Write the first chapter of a novel." and "Could you provide two story suggestions for children that promote altruism? ". On the other hand, following the orange path, we can notice that Llama3-70b-Instruct has a lower win-rate against top models when answering close-ended, non-real-world, reasoning-based questions. These questions are often logic puzzles and math word word problems. Two examples where Llama-3-70B-Instruct won are: "123x = -4x * 2 - 65" and "There are two ducks in front of a duck, two ducks behind a duck and a duck in the middle. How many ducks are there?" +Shown in Figure 3, the first thing to notice is that “Specificity” is the root node of the tree, suggesting that this criteria most immediately divides Llama3-70b-Instruct’s performance into its strengths and weaknesses. It supports our initial findings above that Llama3-70b-Instruct is stronger on open-ended tasks rather than more closed-ended tasks. We can traverse further down the tree in Figure 3 and see that Llama3-70b-Instruct is quite strong on open-ended creative questions (see the blue path), reaching around a 60% win-rate against these top models. Emperically, these types of questions are often writing and brainstorming style questions. For example two prompts where Llama-3-70B-Instruct won are: "Write the first chapter of a novel." and "Could you provide two story suggestions for children that promote altruism? ". On the other hand, following the orange path in Figure 3, we can notice that Llama3-70b-Instruct has a lower win-rate against top models when answering close-ended, non-real-world, reasoning-based questions. These questions are often logic puzzles and math word word problems. Two examples where Llama-3-70B-Instruct won are: "123x = -4x * 2 - 65" and "There are two ducks in front of a duck, two ducks behind a duck and a duck in the middle. How many ducks are there?" ## The effect of overrepresented questions and judges @@ -105,12 +105,12 @@ td {text-align: left} **User analysis.** First we consider some basic judging statistics to check that judging behavior is similar between Claude-3-opus-20240229 and Llama 3-70b. -As seem in Figure 4, there is no significant strange behavior as the number of votes per judge increases. In fact, it seems that as the number of votes per judge increases, the judge’s own battles roughly approach the actual global win rate. +As seen in Figure 4, there is no significant strange behavior as the number of votes per judge increases. In fact, it seems that as the number of votes per judge increases, the judge’s own battles roughly approach the actual global win rate.
- Image 1 - Image 2 + Image 1 + Image 2

Fig 4: Average Winrate for each judge vs Number of judgments made by each judge.

From f8ee10f9d71e9b4b184aa29aa8c82e27678bf0b4 Mon Sep 17 00:00:00 2001 From: efrick2002 Date: Tue, 7 May 2024 19:06:21 -0700 Subject: [PATCH 10/17] small fixes --- blog/2024-05-01-llama3.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/blog/2024-05-01-llama3.md b/blog/2024-05-01-llama3.md index 10dd6e20..54bb0984 100644 --- a/blog/2024-05-01-llama3.md +++ b/blog/2024-05-01-llama3.md @@ -60,7 +60,7 @@ We focus on battles consisting of llama-3-70b against 5 top-ranked models and re We score 1000 battles against the top 3 models on the leaderboard and plot win-rate VS prompt score in Figure X. We observe a significant drop in Llama 3's performance compared to the other top models, from high 50% win-rate to low 40%. We conclude that as more of these ``hardness'' criteria are met, Llama 3's win rate drop rapidly compared to other models. Notw that these criteria may not be exhaustive, see [the blog](https://lmsys.org/blog/2024-04-19-arena-hard/) for further discussion. -We can further analyze to a more granular level which types of questions affect win-rate by fitting a decision tree on the 7 binary columns representing if a given prompt has satisfied each of the criteria above. From this decision tree we can segment prompts into criteria subsets such that Llama3-70b-Instruct performs very well or very poorly. The tree shown in figure X shows us which subsets change the model’s win-rate the most when conditioned on. +We can further analyze to a more granular level which types of questions affect win-rate by fitting a decision tree on the 7 binary columns representing if a given prompt has satisfied each of the criteria above. From this decision tree we can segment prompts into criteria subsets such that Llama3-70b-Instruct performs very well or very poorly. The tree shown in Figure 3 shows us which subsets change the model’s win-rate the most when conditioned on.

Figure 3. Llama3-70b-Instruct win-rate conditioned on hierarchical prompt criteria subsets as fitted using a standard decision tree algorithm. *English battles against top models (claude-3-opus-20240229, gpt-4-0125-preview, gpt-4-1106-preview, gpt-4-turbo-2024-04-09, gemini-1.5-pro-api-0409-preview).

From 9975eb14521e637ba44bd5f87f963231ae28657b Mon Sep 17 00:00:00 2001 From: Lisa Dunlap Date: Wed, 8 May 2024 11:34:55 -0700 Subject: [PATCH 11/17] updated plots --- blog/2024-05-01-llama3.md | 12 ++++++------ .../llama3/llama_sentiment_distribution.png | Bin 593363 -> 619960 bytes .../images/blog/llama3/sentiment_win_rate.png | Bin 528579 -> 528538 bytes 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/blog/2024-05-01-llama3.md b/blog/2024-05-01-llama3.md index 54bb0984..6c661458 100644 --- a/blog/2024-05-01-llama3.md +++ b/blog/2024-05-01-llama3.md @@ -1,6 +1,6 @@ --- -title: "Why is Llama 3 so good? Analyzing when and why users prefer llama 3" -author: "Lisa Dunlap, Evan Frick, Tianle Li, Isaac Ong, Wei-Lin Chiang" +title: "What’s up with Llama 3? Arena battle analysis" +author: "Lisa Dunlap, Evan Frick, Tianle Li, Isaac Ong, Joseph E. Gonzalez Wei-Lin Chiang" date: "May 2, 2024" previewImg: /images/blog/llama3/llama3_blog_cover.png --- @@ -27,7 +27,7 @@ We focus on battles consisting of llama-3-70b against 5 top-ranked models and re ## Analyzing win-rate across different types of questions -**Topic Analysis.** We utilize an LLM labeler (Llama 3-70b) to categorize user prompts into a pre-established taxonomy of topics and visualize the win-rate of Llama3-70b against the other top models across. We see that Llama’s win rate is highest for open-ended and creative tasks like brainstorming and writing, and lowest for more close-ended technical tasks like math and translation. Interestingly Llama 3 achieves the highest win-rate over data processing tasks which mainly consist of parsing and dataframe operations, but as this category has only 19 examples this remains inconclusive. +**Topic Analysis.** We utilize an LLM labeler (Llama 3-70b) to categorize user prompts into a pre-established taxonomy of topics and visualize the win-rate of Llama3-70b against the other top models. We see that Llama’s win rate is highest for open-ended and creative tasks like brainstorming and writing, and lowest for more close-ended technical tasks like math and translation. Interestingly, Llama 3 achieves the highest win-rate over data processing tasks which mainly consist of parsing and dataframe operations, but as this category has only 19 examples this remains inconclusive. **Win-rate VS prompt difficulty.** We employ our [recently released pipeline](https://lmsys.org/blog/2024-04-19-arena-hard/) which scores the difficulty of prompts to determine how Llama 3 compares to the other top models as prompts get harder. We define a set of ``hardness' ' criteria and use GPT-4-turbo to annotate each prompt from 0 to 7 to indicate how many of these criteria are satisfied (higher score indicates a harder prompt). Our 7 criteria are: @@ -69,7 +69,7 @@ Shown in Figure 3, the first thing to notice is that “Specificity” is the ro ## The effect of overrepresented questions and judges -**Effect of duplicate questions.** Using fuzzy string matching, we find that ~9% (6658/7327) of the user prompts in battles between Llama 3 and the other top models are unique, and show in Table 1 that deduplication does not significantly affect Llama3 win-rate, and only changes +**Effect of duplicate questions.** Using fuzzy string matching, we find that ~9% (6658/7327) of the user prompts in battles between Llama 3 and the other top models are duplicates, and show in Table X that deduplication does not significantly affect Llama3 win-rate, and only changes Llama 3-70b win rates against the top 5 models in the arena with and without duplication.

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