From e9cb0e8975c60ba9aa9fe6615048a1b1fe4eed47 Mon Sep 17 00:00:00 2001 From: Isaac Ong Date: Sat, 29 Jun 2024 06:01:20 -0700 Subject: [PATCH 01/10] Add RouteLLM blog post --- blog/2024-06-27-routellm.md | 132 ++++++++++++++++++ .../images/blog/routellm/augmented-mmlu.png | Bin 0 -> 54568 bytes .../blog/routellm/augmented-mt-bench.png | Bin 0 -> 61474 bytes public/images/blog/routellm/cover.png | Bin 0 -> 976983 bytes .../images/blog/routellm/indep-benchmarks.png | Bin 0 -> 48184 bytes public/images/blog/routellm/main.png | Bin 0 -> 118500 bytes public/images/blog/routellm/metrics.png | Bin 0 -> 30034 bytes .../blog/routellm/mt-bench-claude-llama.png | Bin 0 -> 65713 bytes .../images/blog/routellm/unaugmented-mmlu.png | Bin 0 -> 50257 bytes .../blog/routellm/unaugmented-mt-bench.png | Bin 0 -> 59282 bytes 10 files changed, 132 insertions(+) create mode 100644 blog/2024-06-27-routellm.md create mode 100644 public/images/blog/routellm/augmented-mmlu.png create mode 100644 public/images/blog/routellm/augmented-mt-bench.png create mode 100644 public/images/blog/routellm/cover.png create mode 100644 public/images/blog/routellm/indep-benchmarks.png create mode 100644 public/images/blog/routellm/main.png create mode 100644 public/images/blog/routellm/metrics.png create mode 100644 public/images/blog/routellm/mt-bench-claude-llama.png create mode 100644 public/images/blog/routellm/unaugmented-mmlu.png create mode 100644 public/images/blog/routellm/unaugmented-mt-bench.png diff --git a/blog/2024-06-27-routellm.md b/blog/2024-06-27-routellm.md new file mode 100644 index 00000000..ed4099e1 --- /dev/null +++ b/blog/2024-06-27-routellm.md @@ -0,0 +1,132 @@ +--- +title: "Cutting LLM Costs Without Compromising Quality with RouteLLM" +author: "Isaac Ong*, Amjad Almahairi*, Vincent Wu, Wei-Lin Chiang, Tianhao Wu, Joseph E. Gonzalez, M Waleed Kadous, Ion Stoica" +date: "July 1, 2024" +previewImg: /images/blog/routellm/cover.png +--- + +LLMs have demonstrated remarkable capabilities across a wide range of tasks, but there exists wide variation in the costs and capabilities of different LLMs, as seen from the plot of performance against cost of various LLMs in Figure 1. Very broadly, more capable models tend to be more expensive than less capable models. This leads to a dilemma when deploying LLMs in the real-world - routing all queries to the largest, most capable model leads to the highest-quality responses but can be expensive, while routing queries to smaller models can save costs but may result in lower-quality responses. + + + +

Figure 1: Plot of performance against cost of various LLMs. Performance is measured by Elo on Chatbot Arena, and cost per million tokens assuming a 1:1 input / output ratio. Through routing between two models, we ideally achieve a better performance:cost ratio than can be achieved with either model.

+ +LLM routing offers a potential solution to this problem, whereby each user query is first processed by a system that decides which LLM to route it to. Ideally, the system should route all queries that can be sufficiently handled by weaker models to such models, and all other queries to stronger models, minimizing cost while maintaining response quality. However, this turns out to be a challenging problem because the routing system has to infer both the characteristics of an incoming query and different models’ capabilities before routing. + +To tackle this, we present **RouteLLM**, a principled framework for LLM routing using preference data. We formalize the problem of LLM routing and explore techniques to both understand and improve router performance via data augmentation. Using these methods, we trained four different types of routers on using data from Chatbot Arena. We demonstrate that our routers can significantly reduce costs without compromising quality on widely-used benchmarks, with cost reductions of up to 70% on MT Bench, 30% on MMLU, and 40% on GSM8K for the same performance relative to our baselines. We also release our code and datasets publicly, including a new [open-source library](https://github.com/lm-sys/RouteLLM) for serving and evaluating LLM routers. + +## Routing Setup + +In our routing setup, we focus on the case where there are two models: a stronger, more expensive model, and a weaker but cheaper model. Given this setup, our objective is to minimize costs while achieving high quality by routing between both models. + + + +

Figure 2: Causal LLM router performance on MMLU

+ +This is best understood through Figure 2, which plots the performance of our causal LLM router (detailed later in this post) on the MMLU benchmark when routing between GPT-4 and Mixtral 8x7B, with their performance denoted by the red and grey dotted lines respectively. Given this, we can plot a graph of the router performance against the number of the calls made to GPT-4 (which is representative of the cost incurred). + +To compare two different routers on a given set of queries, we consider two main metrics: +The percentage of GPT-4 calls required to achieve a specific performance, denoted by the green dotted line in Figure 2. A lower percentage of calls to the strong model directly translates into lower cost incurred. +The average performance improvement that the router is able to achieve across different costs, denoted by the blue shaded area under the curve in Figure 2. A higher value indicates that the router is better able to route queries to the appropriate model across different costs. + +To train our routers, we use *preference data*, which each consists of a prompt and a comparison between the response quality of two models on that prompt i.e. this could be a win for the first model, a win for the second model, or a tie. Using preference data allows us to learn about the strengths and weaknesses of different models and how they relate to user queries, which we believe is effective for training routers. For our base dataset, we utilize data obtained from [Chatbot Arena](http://chat.lmsys.org) of approximately 80,000 battles between different models. We also investigate *data augmentation* to further improve routing performance by expanding the pool of data using both golden-label datasets and a LLM judge. + +We trained four routers using a mix of preference data obtained from Chatbot Arena and data augmentation: +- A similarity-weighted (SW) ranking router that performs a “weighted Elo calculation” drawing inspiration from Chatbot Arena +- A matrix factorization model that learns a scoring function for how well a model can answer a prompt +- A BERT classifier that predicts which model can provide a better response for a prompt +- A causal LLM classifier that also predicts which model can provide a better response for a prompt + +*For the full details of how these routers were trained, please refer to our [paper](https://arxiv.org/abs/2406.18665).* + +## Results + +We evaluated these routers on three popular benchmarks: [MT Bench](https://arxiv.org/abs/2306.05685), [MMLU](https://arxiv.org/abs/2009.03300), and [GSM8K](https://arxiv.org/abs/2110.14168), presenting results for MT Bench and MMLU below. For evaluation, we route between `gpt-4-1106-preview` as our strong model and Mixtral 8x7B as our weak model. As the baseline, we used a random router, which randomly routes user queries to either model under a cost constraint. + +
+
+ + +
+ +

Figure 3: Router performance on MT Bench (left) trained only on the Chatbot Arena dataset. (right) trained on Chatbot Arena augmented using GPT-4 as a judge on the Nectar dataset.

+ +Figure 3 displays the performance of our routers on MT Bench. For routers trained only on the Arena dataset, we observe strong performance for both matrix factorization and SW ranking, with both routers performing better than the random router across all metrics. Notably, matrix factorization is able to achieve 50% of the performance gap between GPT-4 and Mixtral with an approximately 50% cost reduction as compared to the baseline. + +We also augmented the Arena data using an LLM judge. Doing so leads to significant improvements across all routers when trained on this data. On this augmented dataset, matrix factorization is again the best-performing router, with the cost required to achieve 50% of the performance gap between both models further halved, meaning that we are able to achieve the same performance as our random baseline with only about 25% of the cost. + +
+
+ + +
+ + +

Figure 4: Router performance on MMLU (left) trained only on the Chatbot Arena dataset. (right) trained on Chatbot Arena augmented using golden-label data from the MMLU validation split.

+ +Conversely, on MMLU in Figure 4, all routers perform poorly at a near-random level when trained only on Arena dataset, which we attribute to most MMLU questions being out-of-distribution. However, augmenting the training dataset using golden-label data from the MMLU validation split leads to significant performance improvements across all routers, with our best router requiring nearly 30% less cost relative to random to achieve 50% of the performance gap between GPT-4 and Mixtral. Importantly, the golden-labeled dataset of approximately 1500 samples represents less than 2% of the overall training data, demonstrating the effectiveness of data augmentation even when the number of samples is small. + +### Industry Benchmarks + +
+ + +

Figure 6: Comparison of our router against existing routing systems on MT Bench.

+ +In Figure 6, we also report the performance of our best-performing routers on MT Bench against [Unify AI](https://unify.ai/) and [Not Diamond](https://www.notdiamond.ai/notdiamond-0001), two existing LLM routing systems released by companies. Our routers demonstrate competitive performance, achieving stronger results than other available routers for the same cost. + +### Generalizing to Other Models + +While we pick GPT-4 and Mixtral as representative strong and weak models for the above results, we also experiment with a different model pair to demonstrate the generalizability of our framework. Figure 7 shows the MT Bench results when we replace our model pair with Claude 3 Opus and Llama 3 8B. Importantly, we use the same routers without any retraining, and responses from Claude 3 Opus and Llama 3 8B are not present in the training data. + +
+ + +

Figure 7: Router performance on MT Bench when routed to Claude 3 Opus and Llama 3 8B.

+ +Even when the model pair is replaced, we observe strong results across all existing routers on MT Bench, with performance comparable to our original model pair. These results suggest that our routers have learned some common characteristics of problems that can distinguish between strong and weak models, which generalize to new strong and weak model pairs without additional training. + +## Conclusion + +Our results demonstrate the ability of these routers to achieve significant cost savings while maintaining a high quality of responses across a variety of benchmarks. Our results also highlight the effectiveness of data augmentation and benchmark-dataset similarity scores in improving routing performance using only a small amount of data, offering a scalable path towards improving routing performance for real-world use cases. + +Based on our learnings from this research, we have released an open-source framework for serving and evaluating routers on [GitHub](https://github.com/lm-sys/RouteLLM), which we believe is the first framework of its kind supporting LLM routers. We are also releasing all the routers and datasets we have trained on [HuggingFace](https://huggingface.co/routellm) for public use. + +We are excited to see what you build on top of this! Please let us know if you face any issues or have any suggestions. For the full details, please refer to our [arXiv](https://arxiv.org/abs/2406.18665) paper. + +## Acknowledgements + +We are grateful to Tyler Griggs for his valuable feedback on this post. + +## Citations + +``` +@misc{ong2024routellmlearningroutellms, + title={RouteLLM: Learning to Route LLMs with Preference Data}, + author={Isaac Ong and Amjad Almahairi and Vincent Wu and Wei-Lin Chiang and Tianhao Wu and Joseph E. Gonzalez and M Waleed Kadous and Ion Stoica}, + year={2024}, + eprint={2406.18665}, + archivePrefix={arXiv}, + primaryClass={cs.LG}, + url={https://arxiv.org/abs/2406.18665}, +} + +@misc{chiang2024chatbot, + title={Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference}, + author={Wei-Lin Chiang and Lianmin Zheng and Ying Sheng and Anastasios Nikolas Angelopoulos and Tianle Li and Dacheng Li and Hao Zhang and Banghua Zhu and Michael Jordan and Joseph E. Gonzalez and Ion Stoica}, + year={2024}, + eprint={2403.04132}, + archivePrefix={arXiv}, + primaryClass={cs.AI} +} + +@misc{ding2024hybridllmcostefficientqualityaware, + title={Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing}, + author={Dujian Ding and Ankur Mallick and Chi Wang and Robert Sim and Subhabrata Mukherjee and Victor Ruhle and Laks V. S. Lakshmanan and Ahmed Hassan Awadallah}, + year={2024}, + eprint={2404.14618}, + archivePrefix={arXiv}, + primaryClass={cs.LG}, + url={https://arxiv.org/abs/2404.14618}, +} +``` diff --git a/public/images/blog/routellm/augmented-mmlu.png b/public/images/blog/routellm/augmented-mmlu.png new file mode 100644 index 0000000000000000000000000000000000000000..f2bc3c497ae4c7d25883d011c2ca9d8223181c55 GIT binary patch literal 54568 zcmbTeWl)@5ur-PWm%IcC9talPU4jP>?(XjH?hxGFbs)Gq1P?BQySwW*L+&}>{dw!A zDwTnnXV324-D~yg-jlGOGGZu*xQGxC5Gdk5gykV1px7WFAh+RRLBF9bF^UBJ!|5oZ z>Zo92?C7FrZv-K&=V)tb<7jE7|HawJ-oebqnwgHBj+y3*siUK<0~bBL)&IVM&c@z^ ze*C-8K4=jH+aGEU5D;j3;J=V1{KaMv5HpG5!UBq}8OJLwju=XKgBNF~MR%wN2<2!e7k%()|j4qBs}^?|l(g(gpC7o2A5{CFH0|6{)rWy&-7) zzgL#(Euv1IpPzqQA|WHQSWla>1O^B5U2?>eOEWcC&6X)8S$W(Z57lJFD_5%L%9pDC zHXQm2yqbJA`NCcDh#ufcB?>Zi2 zH|4DR^}e4(;qZm^NH`{SbxqAi{k-j}VFQn2>Fk`I_Y5{|7qH68%5QENMbhb;XJ-?D&R39U1|C=2JrEEO 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zLt^cL9Ds)at9LqeA6T?h{4kp6DZF9S(jGvW#@quegchRcT3D88`-cx%#Ax4HQ;F*( zfDH#E649Zdpd}_H?FFd9i>Xj3iY}jDhsR277?CA@aUlw8bz#U+T2vIlNbm~q+`YI% z#1)W|#r&WU062RNcY)wKIQtM7*70^~smUR2Ob)BycK;dV=Nc)Ur@4-HHGpE5!%gh< zo>N1f+acI7izuw{ZO|0jr-drr&Y%w9HdItq^$ri~qEC0nnSfP_Hq{c0T-$~ ztb`pyDr0tM_Kv<9$3>8P{he;CbC3cyt5g!B1VV1R+%>V!Z literal 0 HcmV?d00001 From 1a847b8f479ba540042caadc7bacc230737f56ee Mon Sep 17 00:00:00 2001 From: Isaac Ong Date: Sat, 29 Jun 2024 19:07:21 -0700 Subject: [PATCH 02/10] Add to projects --- content/projects.json | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/content/projects.json b/content/projects.json index cc766ace..7734aba6 100644 --- a/content/projects.json +++ b/content/projects.json @@ -55,6 +55,13 @@ "size": "", "desc": "An exact, fast, parallel decoding algorithm without the need for draft models or data stores.", "link": "https://github.com/hao-ai-lab/LookaheadDecoding" + }, + { + "name": "RouteLLM", + "architecture": "", + "size": "", + "desc": "A framework for serving and evaluating large language model routers.", + "link": "https://github.com/lm-sys/RouteLLM" } ] }, From 96d1f81c7063c781bff0879dff4ece49cf40b400 Mon Sep 17 00:00:00 2001 From: Isaac Ong Date: Sat, 29 Jun 2024 19:07:27 -0700 Subject: [PATCH 03/10] Update RouteLLM post --- blog/2024-06-27-routellm.md | 28 +++++++++++----------------- 1 file changed, 11 insertions(+), 17 deletions(-) diff --git a/blog/2024-06-27-routellm.md b/blog/2024-06-27-routellm.md index ed4099e1..74806cd3 100644 --- a/blog/2024-06-27-routellm.md +++ b/blog/2024-06-27-routellm.md @@ -5,15 +5,15 @@ date: "July 1, 2024" previewImg: /images/blog/routellm/cover.png --- -LLMs have demonstrated remarkable capabilities across a wide range of tasks, but there exists wide variation in the costs and capabilities of different LLMs, as seen from the plot of performance against cost of various LLMs in Figure 1. Very broadly, more capable models tend to be more expensive than less capable models. This leads to a dilemma when deploying LLMs in the real-world - routing all queries to the largest, most capable model leads to the highest-quality responses but can be expensive, while routing queries to smaller models can save costs but may result in lower-quality responses. +LLMs have demonstrated remarkable capabilities across a range of tasks, but there exists wide variation in the costs and capabilities of different LLMs, as seen from a plot of performance against cost of LLMs in Figure 1. Very broadly, more capable models tend to be more expensive than less capable models. This leads to a dilemma when deploying LLMs in the real-world - routing all queries to the largest, most capable model leads to the highest-quality responses but can be expensive, while routing queries to smaller models can save costs but may result in lower-quality responses.

Figure 1: Plot of performance against cost of various LLMs. Performance is measured by Elo on Chatbot Arena, and cost per million tokens assuming a 1:1 input / output ratio. Through routing between two models, we ideally achieve a better performance:cost ratio than can be achieved with either model.

-LLM routing offers a potential solution to this problem, whereby each user query is first processed by a system that decides which LLM to route it to. Ideally, the system should route all queries that can be sufficiently handled by weaker models to such models, and all other queries to stronger models, minimizing cost while maintaining response quality. However, this turns out to be a challenging problem because the routing system has to infer both the characteristics of an incoming query and different models’ capabilities before routing. +LLM routing offers a solution to this problem, whereby each query is first processed by a system that decides which LLM to route it to. Ideally, the system should route all queries that can be sufficiently handled by weaker models to such models, and all other queries to stronger models, minimizing cost while maintaining response quality. However, this turns out to be a challenging problem because the routing system has to infer both the characteristics of an incoming query and different models’ capabilities before routing. -To tackle this, we present **RouteLLM**, a principled framework for LLM routing using preference data. We formalize the problem of LLM routing and explore techniques to both understand and improve router performance via data augmentation. Using these methods, we trained four different types of routers on using data from Chatbot Arena. We demonstrate that our routers can significantly reduce costs without compromising quality on widely-used benchmarks, with cost reductions of up to 70% on MT Bench, 30% on MMLU, and 40% on GSM8K for the same performance relative to our baselines. We also release our code and datasets publicly, including a new [open-source library](https://github.com/lm-sys/RouteLLM) for serving and evaluating LLM routers. +To tackle this, we present **RouteLLM**, a principled framework for LLM routing using preference data. We formalize the problem of LLM routing and explore techniques to understand and improve router performance. Using these ideas, we trained four different routers using data from Chatbot Arena and demonstrate that they can significantly reduce costs without compromising quality on widely-used benchmarks, with **cost reductions of up to 70% on MT Bench, 30% on MMLU, and 40% on GSM8K** for the same performance relative to our baselines. We also release our code and datasets publicly, including a new [open-source library](https://github.com/lm-sys/RouteLLM) for serving and evaluating LLM routers. ## Routing Setup @@ -23,11 +23,7 @@ In our routing setup, we focus on the case where there are two models: a stronge

Figure 2: Causal LLM router performance on MMLU

-This is best understood through Figure 2, which plots the performance of our causal LLM router (detailed later in this post) on the MMLU benchmark when routing between GPT-4 and Mixtral 8x7B, with their performance denoted by the red and grey dotted lines respectively. Given this, we can plot a graph of the router performance against the number of the calls made to GPT-4 (which is representative of the cost incurred). - -To compare two different routers on a given set of queries, we consider two main metrics: -The percentage of GPT-4 calls required to achieve a specific performance, denoted by the green dotted line in Figure 2. A lower percentage of calls to the strong model directly translates into lower cost incurred. -The average performance improvement that the router is able to achieve across different costs, denoted by the blue shaded area under the curve in Figure 2. A higher value indicates that the router is better able to route queries to the appropriate model across different costs. +This is best understood through Figure 2, which represents the performance of our causal LLM router (detailed later in this post) on the MMLU benchmark when routing between GPT-4 and Mixtral 8x7B, with the performance of these two models denoted by the red and grey dotted lines. For each router, we can plot a graph of its performance against the number of the calls made to GPT-4 (which is representative of the cost incurred since GPT-4 cost is dominant). To train our routers, we use *preference data*, which each consists of a prompt and a comparison between the response quality of two models on that prompt i.e. this could be a win for the first model, a win for the second model, or a tie. Using preference data allows us to learn about the strengths and weaknesses of different models and how they relate to user queries, which we believe is effective for training routers. For our base dataset, we utilize data obtained from [Chatbot Arena](http://chat.lmsys.org) of approximately 80,000 battles between different models. We also investigate *data augmentation* to further improve routing performance by expanding the pool of data using both golden-label datasets and a LLM judge. @@ -37,8 +33,6 @@ We trained four routers using a mix of preference data obtained from Chatbot Are - A BERT classifier that predicts which model can provide a better response for a prompt - A causal LLM classifier that also predicts which model can provide a better response for a prompt -*For the full details of how these routers were trained, please refer to our [paper](https://arxiv.org/abs/2406.18665).* - ## Results We evaluated these routers on three popular benchmarks: [MT Bench](https://arxiv.org/abs/2306.05685), [MMLU](https://arxiv.org/abs/2009.03300), and [GSM8K](https://arxiv.org/abs/2110.14168), presenting results for MT Bench and MMLU below. For evaluation, we route between `gpt-4-1106-preview` as our strong model and Mixtral 8x7B as our weak model. As the baseline, we used a random router, which randomly routes user queries to either model under a cost constraint. @@ -49,11 +43,11 @@ We evaluated these routers on three popular benchmarks: [MT Bench](https://arxiv -

Figure 3: Router performance on MT Bench (left) trained only on the Chatbot Arena dataset. (right) trained on Chatbot Arena augmented using GPT-4 as a judge on the Nectar dataset.

+

Figure 3: Router performance on MT Bench (left) trained only on Arena data. (right) trained on Arena data augmented using a LLM judge.

Figure 3 displays the performance of our routers on MT Bench. For routers trained only on the Arena dataset, we observe strong performance for both matrix factorization and SW ranking, with both routers performing better than the random router across all metrics. Notably, matrix factorization is able to achieve 50% of the performance gap between GPT-4 and Mixtral with an approximately 50% cost reduction as compared to the baseline. -We also augmented the Arena data using an LLM judge. Doing so leads to significant improvements across all routers when trained on this data. On this augmented dataset, matrix factorization is again the best-performing router, with the cost required to achieve 50% of the performance gap between both models further halved, meaning that we are able to achieve the same performance as our random baseline with only about 25% of the cost. +We also augmented the Arena data using an LLM judge. Doing so leads to significant improvements across all routers when trained on this data. On this augmented dataset, matrix factorization is again the best-performing router, with the cost required to achieve 50% of the performance gap further halved, meaning that we are able to achieve the same performance as our random baseline with only about 25% of the cost.
@@ -62,9 +56,9 @@ We also augmented the Arena data using an LLM judge. Doing so leads to significa
-

Figure 4: Router performance on MMLU (left) trained only on the Chatbot Arena dataset. (right) trained on Chatbot Arena augmented using golden-label data from the MMLU validation split.

+

Figure 4: Router performance on MMLU (left) trained only on Arena data. (right) trained on Arena data augmented using golden-label data from the MMLU validation split.

-Conversely, on MMLU in Figure 4, all routers perform poorly at a near-random level when trained only on Arena dataset, which we attribute to most MMLU questions being out-of-distribution. However, augmenting the training dataset using golden-label data from the MMLU validation split leads to significant performance improvements across all routers, with our best router requiring nearly 30% less cost relative to random to achieve 50% of the performance gap between GPT-4 and Mixtral. Importantly, the golden-labeled dataset of approximately 1500 samples represents less than 2% of the overall training data, demonstrating the effectiveness of data augmentation even when the number of samples is small. +Conversely, on MMLU in Figure 4, all routers perform poorly at a near-random level when trained only on Arena dataset, which we attribute to most MMLU questions being out-of-distribution. However, augmenting the training dataset using golden-label data from the MMLU validation split leads to significant performance improvements across all routers, with our best router now requiring nearly 30% less cost relative to the baseline to achieve 50% of the performance gap between GPT-4 and Mixtral. Importantly, this golden-labeled dataset of approximately 1500 samples represents less than 2% of the overall training data, demonstrating the effectiveness of data augmentation even when the number of samples is small. ### Industry Benchmarks @@ -73,7 +67,7 @@ Conversely, on MMLU in Figure 4, all routers perform poorly at a near-random lev

Figure 6: Comparison of our router against existing routing systems on MT Bench.

-In Figure 6, we also report the performance of our best-performing routers on MT Bench against [Unify AI](https://unify.ai/) and [Not Diamond](https://www.notdiamond.ai/notdiamond-0001), two existing LLM routing systems released by companies. Our routers demonstrate competitive performance, achieving stronger results than other available routers for the same cost. +In Figure 6, we also report the performance of our best-performing routers on MT Bench against [Unify AI](https://unify.ai/) and [Not Diamond](https://www.notdiamond.ai/notdiamond-0001), two existing LLM routing systems released by companies. Our routers demonstrate competitive performance, achieving much stronger results than other available routers for the same cost. ### Generalizing to Other Models @@ -95,9 +89,9 @@ Based on our learnings from this research, we have released an open-source frame We are excited to see what you build on top of this! Please let us know if you face any issues or have any suggestions. For the full details, please refer to our [arXiv](https://arxiv.org/abs/2406.18665) paper. ## Acknowledgements - + We are grateful to Tyler Griggs for his valuable feedback on this post. - + ## Citations ``` From 926380c273c403a81690ba2a20784ac3a90d6943 Mon Sep 17 00:00:00 2001 From: Isaac Ong Date: Sat, 29 Jun 2024 19:25:49 -0700 Subject: [PATCH 04/10] Update RouteLLM blog post --- blog/2024-06-27-routellm.md | 11 ++++++----- public/images/blog/routellm/metrics.png | Bin 30034 -> 26084 bytes 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/blog/2024-06-27-routellm.md b/blog/2024-06-27-routellm.md index 74806cd3..0a286728 100644 --- a/blog/2024-06-27-routellm.md +++ b/blog/2024-06-27-routellm.md @@ -7,7 +7,7 @@ previewImg: /images/blog/routellm/cover.png LLMs have demonstrated remarkable capabilities across a range of tasks, but there exists wide variation in the costs and capabilities of different LLMs, as seen from a plot of performance against cost of LLMs in Figure 1. Very broadly, more capable models tend to be more expensive than less capable models. This leads to a dilemma when deploying LLMs in the real-world - routing all queries to the largest, most capable model leads to the highest-quality responses but can be expensive, while routing queries to smaller models can save costs but may result in lower-quality responses. - +

Figure 1: Plot of performance against cost of various LLMs. Performance is measured by Elo on Chatbot Arena, and cost per million tokens assuming a 1:1 input / output ratio. Through routing between two models, we ideally achieve a better performance:cost ratio than can be achieved with either model.

@@ -21,9 +21,10 @@ In our routing setup, we focus on the case where there are two models: a stronge -

Figure 2: Causal LLM router performance on MMLU

-This is best understood through Figure 2, which represents the performance of our causal LLM router (detailed later in this post) on the MMLU benchmark when routing between GPT-4 and Mixtral 8x7B, with the performance of these two models denoted by the red and grey dotted lines. For each router, we can plot a graph of its performance against the number of the calls made to GPT-4 (which is representative of the cost incurred since GPT-4 cost is dominant). +

Figure 2: Random router performance on MT Bench

+ +This is best understood through Figure 2, which represents the performance of a router that randomly routes between the two models on MT Bench. Specifically, we route between GPT-4 and Mixtral 8x7B here, with the performance of these two models denoted by the red and grey dotted lines. Similar to this, for any router, we can plot a graph of its performance against the number of the calls made to GPT-4 (which is representative of the cost incurred since GPT-4 cost is dominant). To train our routers, we use *preference data*, which each consists of a prompt and a comparison between the response quality of two models on that prompt i.e. this could be a win for the first model, a win for the second model, or a tie. Using preference data allows us to learn about the strengths and weaknesses of different models and how they relate to user queries, which we believe is effective for training routers. For our base dataset, we utilize data obtained from [Chatbot Arena](http://chat.lmsys.org) of approximately 80,000 battles between different models. We also investigate *data augmentation* to further improve routing performance by expanding the pool of data using both golden-label datasets and a LLM judge. @@ -35,7 +36,7 @@ We trained four routers using a mix of preference data obtained from Chatbot Are ## Results -We evaluated these routers on three popular benchmarks: [MT Bench](https://arxiv.org/abs/2306.05685), [MMLU](https://arxiv.org/abs/2009.03300), and [GSM8K](https://arxiv.org/abs/2110.14168), presenting results for MT Bench and MMLU below. For evaluation, we route between `gpt-4-1106-preview` as our strong model and Mixtral 8x7B as our weak model. As the baseline, we used a random router, which randomly routes user queries to either model under a cost constraint. +We evaluated these routers on three popular benchmarks: [MT Bench](https://arxiv.org/abs/2306.05685), [MMLU](https://arxiv.org/abs/2009.03300), and [GSM8K](https://arxiv.org/abs/2110.14168), presenting results for MT Bench and MMLU below. For evaluation, we route between `gpt-4-1106-preview` as our strong model and Mixtral 8x7B as our weak model. We use the random router from before as our baseline.
@@ -67,7 +68,7 @@ Conversely, on MMLU in Figure 4, all routers perform poorly at a near-random lev

Figure 6: Comparison of our router against existing routing systems on MT Bench.

-In Figure 6, we also report the performance of our best-performing routers on MT Bench against [Unify AI](https://unify.ai/) and [Not Diamond](https://www.notdiamond.ai/notdiamond-0001), two existing LLM routing systems released by companies. Our routers demonstrate competitive performance, achieving much stronger results than other available routers for the same cost. +In Figure 6, we also report the performance of our best-performing routers on MT Bench against [Unify AI](https://unify.ai/) and [Not Diamond](https://www.notdiamond.ai/notdiamond-0001), two existing LLM routing systems released by companies. 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zKG=RR6M``B_JtES#j+p1hp$JxUW!lF=3hP!^u`P!HqeuMNnb$@6+?jb7EW@a{)F~V z##;7Gn`oq^rQ@t}47Rg2V=5CyDEa{1M#F1ZcNEGX8;Ln^{>{xN{rvp+w``#RX6KM` zI_I*F<+v~Ukj~L;5;slZI#f~nGagpAys>c;S-XOAF0$@`q|f6kuk8p%%H_(h=IfY9 zb_TeV;AO2mEwB3iNm(MmdKf7Wi7DwCo|8kxyPq;kMz1YpU4|n zcNQys U%T2g2-;_9VP)jZCfccI80>gWN7XSbN From 9be7c3b474e7fcea428fc22fdf7516bdd59ba877 Mon Sep 17 00:00:00 2001 From: Isaac Ong Date: Sat, 29 Jun 2024 19:29:44 -0700 Subject: [PATCH 05/10] Better explain goal --- blog/2024-06-27-routellm.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/blog/2024-06-27-routellm.md b/blog/2024-06-27-routellm.md index 0a286728..a87bc411 100644 --- a/blog/2024-06-27-routellm.md +++ b/blog/2024-06-27-routellm.md @@ -24,7 +24,7 @@ In our routing setup, we focus on the case where there are two models: a stronge

Figure 2: Random router performance on MT Bench

-This is best understood through Figure 2, which represents the performance of a router that randomly routes between the two models on MT Bench. Specifically, we route between GPT-4 and Mixtral 8x7B here, with the performance of these two models denoted by the red and grey dotted lines. Similar to this, for any router, we can plot a graph of its performance against the number of the calls made to GPT-4 (which is representative of the cost incurred since GPT-4 cost is dominant). +This is best understood through Figure 2, which represents the performance of a router that randomly routes between the two models on MT Bench. Specifically here, we pick GPT-4 and Mixtral 8x7B as our strong / weak model pair, with their performance denoted by the red and grey dotted lines respectively. Similar to this, for any router, we can plot a graph of its performance against the number of the calls made to GPT-4 (which is representative of the cost incurred). Given this setup, our objective is then to minimize costs while achieving high quality responses i.e. we want to have a plot that has a greater area under the curve. To train our routers, we use *preference data*, which each consists of a prompt and a comparison between the response quality of two models on that prompt i.e. this could be a win for the first model, a win for the second model, or a tie. Using preference data allows us to learn about the strengths and weaknesses of different models and how they relate to user queries, which we believe is effective for training routers. For our base dataset, we utilize data obtained from [Chatbot Arena](http://chat.lmsys.org) of approximately 80,000 battles between different models. We also investigate *data augmentation* to further improve routing performance by expanding the pool of data using both golden-label datasets and a LLM judge. From 34597dd7fdc41526eddce143516a70668e800cac Mon Sep 17 00:00:00 2001 From: Isaac Ong Date: Sun, 30 Jun 2024 00:29:07 -0700 Subject: [PATCH 06/10] Rename date --- blog/{2024-06-27-routellm.md => 2024-07-01-routellm.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename blog/{2024-06-27-routellm.md => 2024-07-01-routellm.md} (100%) diff --git a/blog/2024-06-27-routellm.md b/blog/2024-07-01-routellm.md similarity index 100% rename from blog/2024-06-27-routellm.md rename to blog/2024-07-01-routellm.md From 26211cdf60aa6458487dd9fda84110d1a5268906 Mon Sep 17 00:00:00 2001 From: Isaac Ong Date: Sun, 30 Jun 2024 15:51:13 -0700 Subject: [PATCH 07/10] More updates --- blog/2024-07-01-routellm.md | 43 ++++++++++-------- .../blog/routellm/indep-benchmarks-llama.png | Bin 0 -> 48976 bytes .../images/blog/routellm/indep-benchmarks.png | Bin 48184 -> 48327 bytes 3 files changed, 23 insertions(+), 20 deletions(-) create mode 100644 public/images/blog/routellm/indep-benchmarks-llama.png diff --git a/blog/2024-07-01-routellm.md b/blog/2024-07-01-routellm.md index a87bc411..59cbe624 100644 --- a/blog/2024-07-01-routellm.md +++ b/blog/2024-07-01-routellm.md @@ -13,7 +13,7 @@ LLMs have demonstrated remarkable capabilities across a range of tasks, but ther LLM routing offers a solution to this problem, whereby each query is first processed by a system that decides which LLM to route it to. Ideally, the system should route all queries that can be sufficiently handled by weaker models to such models, and all other queries to stronger models, minimizing cost while maintaining response quality. However, this turns out to be a challenging problem because the routing system has to infer both the characteristics of an incoming query and different models’ capabilities before routing. -To tackle this, we present **RouteLLM**, a principled framework for LLM routing using preference data. We formalize the problem of LLM routing and explore techniques to understand and improve router performance. Using these ideas, we trained four different routers using data from Chatbot Arena and demonstrate that they can significantly reduce costs without compromising quality on widely-used benchmarks, with **cost reductions of up to 70% on MT Bench, 30% on MMLU, and 40% on GSM8K** for the same performance relative to our baselines. We also release our code and datasets publicly, including a new [open-source library](https://github.com/lm-sys/RouteLLM) for serving and evaluating LLM routers. +To tackle this, we present **RouteLLM**, a principled framework for LLM routing using preference data. We formalize the problem of LLM routing and explore techniques to understand and improve router performance. Using these ideas, we trained four different routers using data from Chatbot Arena and demonstrate that they can significantly reduce costs without compromising quality on widely-used benchmarks, with **cost reductions of up to 70% on MT Bench, 30% on MMLU, and 40% on GSM8K** for the same performance relative to a random baseline. We also release our code and datasets publicly, including a new [open-source library](https://github.com/lm-sys/RouteLLM) for serving and evaluating LLM routers. ## Routing Setup @@ -24,11 +24,11 @@ In our routing setup, we focus on the case where there are two models: a stronge

Figure 2: Random router performance on MT Bench

-This is best understood through Figure 2, which represents the performance of a router that randomly routes between the two models on MT Bench. Specifically here, we pick GPT-4 and Mixtral 8x7B as our strong / weak model pair, with their performance denoted by the red and grey dotted lines respectively. Similar to this, for any router, we can plot a graph of its performance against the number of the calls made to GPT-4 (which is representative of the cost incurred). Given this setup, our objective is then to minimize costs while achieving high quality responses i.e. we want to have a plot that has a greater area under the curve. +This is best understood through Figure 2, which represents the performance of a router that randomly routes between the two models on MT Bench. Specifically, we route between GPT-4 and Mixtral 8x7B here, with the performance of these two models denoted by the red and grey dotted lines. Similar to this, for any router, we can plot a graph of its performance against the number of the calls made to GPT-4 (which is representative of the cost incurred since GPT-4 cost is dominant). -To train our routers, we use *preference data*, which each consists of a prompt and a comparison between the response quality of two models on that prompt i.e. this could be a win for the first model, a win for the second model, or a tie. Using preference data allows us to learn about the strengths and weaknesses of different models and how they relate to user queries, which we believe is effective for training routers. For our base dataset, we utilize data obtained from [Chatbot Arena](http://chat.lmsys.org) of approximately 80,000 battles between different models. We also investigate *data augmentation* to further improve routing performance by expanding the pool of data using both golden-label datasets and a LLM judge. +To train our routers, we use *preference data*, which each consists of a prompt and a comparison between the response quality of two models on that prompt i.e. this could be a win for the first model, a win for the second model, or a tie. Using preference data allows us to learn about the strengths and weaknesses of different models and how they relate to user queries, which we believe is effective for training routers. For our base dataset, we utilize data obtained from [Chatbot Arena](http://chat.lmsys.org). We also investigate *data augmentation* to further improve routing performance by using both golden-label datasets and a LLM judge to expand the pool of preference data. -We trained four routers using a mix of preference data obtained from Chatbot Arena and data augmentation: +We trained four routers using a mix of data from Chatbot Arena and data augmentation: - A similarity-weighted (SW) ranking router that performs a “weighted Elo calculation” drawing inspiration from Chatbot Arena - A matrix factorization model that learns a scoring function for how well a model can answer a prompt - A BERT classifier that predicts which model can provide a better response for a prompt @@ -36,7 +36,7 @@ We trained four routers using a mix of preference data obtained from Chatbot Are ## Results -We evaluated these routers on three popular benchmarks: [MT Bench](https://arxiv.org/abs/2306.05685), [MMLU](https://arxiv.org/abs/2009.03300), and [GSM8K](https://arxiv.org/abs/2110.14168), presenting results for MT Bench and MMLU below. For evaluation, we route between `gpt-4-1106-preview` as our strong model and Mixtral 8x7B as our weak model. We use the random router from before as our baseline. +We evaluated these routers on three popular benchmarks: [MT Bench](https://arxiv.org/abs/2306.05685), [MMLU](https://arxiv.org/abs/2009.03300), and [GSM8K](https://arxiv.org/abs/2110.14168), presenting results for MT Bench and MMLU below. For evaluation, we route between `gpt-4-1106-preview` as our strong model and `mixtral-8x7b-instruct-v0.1` as our weak model. We use the random router from before as our baseline.
@@ -44,11 +44,11 @@ We evaluated these routers on three popular benchmarks: [MT Bench](https://arxiv
-

Figure 3: Router performance on MT Bench (left) trained only on Arena data. (right) trained on Arena data augmented using a LLM judge.

+

Figure 3: Router performance on MT Bench (left) trained only on Arena data (right) trained on Arena data augmented using a LLM judge.

Figure 3 displays the performance of our routers on MT Bench. For routers trained only on the Arena dataset, we observe strong performance for both matrix factorization and SW ranking, with both routers performing better than the random router across all metrics. Notably, matrix factorization is able to achieve 50% of the performance gap between GPT-4 and Mixtral with an approximately 50% cost reduction as compared to the baseline. -We also augmented the Arena data using an LLM judge. Doing so leads to significant improvements across all routers when trained on this data. On this augmented dataset, matrix factorization is again the best-performing router, with the cost required to achieve 50% of the performance gap further halved, meaning that we are able to achieve the same performance as our random baseline with only about 25% of the cost. +Augmenting the Arena data using an LLM judge leads to significant improvements across all routers. When trained on this augmented dataset, matrix factorization is again the best-performing router, with the cost required to achieve 50% of the performance gap further halved, meaning that we are able to achieve the same performance as our random baseline with only about 25% of the cost.
@@ -57,35 +57,38 @@ We also augmented the Arena data using an LLM judge. Doing so leads to significa
-

Figure 4: Router performance on MMLU (left) trained only on Arena data. (right) trained on Arena data augmented using golden-label data from the MMLU validation split.

+

Figure 4: Router performance on MMLU (left) trained only on Arena data (right) trained on Arena data augmented using golden-label data from the MMLU validation split.

-Conversely, on MMLU in Figure 4, all routers perform poorly at a near-random level when trained only on Arena dataset, which we attribute to most MMLU questions being out-of-distribution. However, augmenting the training dataset using golden-label data from the MMLU validation split leads to significant performance improvements across all routers, with our best router now requiring nearly 30% less cost relative to the baseline to achieve 50% of the performance gap between GPT-4 and Mixtral. Importantly, this golden-labeled dataset of approximately 1500 samples represents less than 2% of the overall training data, demonstrating the effectiveness of data augmentation even when the number of samples is small. +Conversely, on MMLU in Figure 4, all routers perform poorly at a near-random level when trained only on Arena dataset, which we attribute to most MMLU questions being out-of-distribution. However, augmenting the training dataset using golden-label data from the MMLU validation split leads to significant performance improvements across all routers, with our best-performing causal LLM router now requiring about 30% less cost as compared to the baseline to achieve 50% of the performance gap between GPT-4 and Mixtral. Importantly, this augmented dataset of approximately 1500 samples represents less than 2% of the overall training data, demonstrating the effectiveness of data augmentation even when the number of samples is small. -### Industry Benchmarks +### Generalizing to Other Models -
- +While we route between GPT-4 and Mixtral for the above evaluations, to demonstrate the generalizability of our framework, we also present MT Bench results when routing between Claude 3 Opus and Llama 3 8B. Importantly, we use the same routers *without any retraining*, and responses from Claude 3 Opus and Llama 3 8B are not present in our training data. -

Figure 6: Comparison of our router against existing routing systems on MT Bench.

+
+ -In Figure 6, we also report the performance of our best-performing routers on MT Bench against [Unify AI](https://unify.ai/) and [Not Diamond](https://www.notdiamond.ai/notdiamond-0001), two existing LLM routing systems released by companies. Our routers demonstrate competitive performance, achieving the same performance as other available routers while being about 40% cheaper. +

Figure 6: Router performance on MT Bench when routed to Claude 3 Opus and Llama 3 8B.

-### Generalizing to Other Models +Even when the model pair is replaced, we observe strong results across all existing routers on MT Bench in Figure 6, with performance comparable to our original model pair. These results suggest that our routers have learned some common characteristics of problems that can distinguish between strong and weak models, which generalize to new strong and weak model pairs without additional training. -While we pick GPT-4 and Mixtral as representative strong and weak models for the above results, we also experiment with a different model pair to demonstrate the generalizability of our framework. Figure 7 shows the MT Bench results when we replace our model pair with Claude 3 Opus and Llama 3 8B. Importantly, we use the same routers without any retraining, and responses from Claude 3 Opus and Llama 3 8B are not present in the training data. +### Indepndent Benchmarking
- +
+ + +
-

Figure 7: Router performance on MT Bench when routed to Claude 3 Opus and Llama 3 8B.

+

Figure 7: Comparison of our router against existing routing systems on MT Bench (left) using gpt-4-turbo-2024-04-09 and llama-2-70b-chat (right) using gpt-4-turbo-2024-04-09 and mixtral-8x7b-instruct-v0.1

-Even when the model pair is replaced, we observe strong results across all existing routers on MT Bench, with performance comparable to our original model pair. These results suggest that our routers have learned some common characteristics of problems that can distinguish between strong and weak models, which generalize to new strong and weak model pairs without additional training. +In Figure 7, we also report the performance of our best-performing routers on MT Bench against [Martian](https://withmartian.com/), [Unify AI](https://unify.ai/) and [Not Diamond](https://www.notdiamond.ai/notdiamond-0001), existing LLM routing systems released by companies. We use `gpt-4-turbo-2024-04-09` as the strong model and `llama-2-70b-chat` or `mixtral-8x7b-instruct-v0.1` as the weak model depending on the models available. Our routers demonstrate very competitive results, achieving the same performance as other routers while being up to 40% cheaper. ## Conclusion Our results demonstrate the ability of these routers to achieve significant cost savings while maintaining a high quality of responses across a variety of benchmarks. Our results also highlight the effectiveness of data augmentation and benchmark-dataset similarity scores in improving routing performance using only a small amount of data, offering a scalable path towards improving routing performance for real-world use cases. -Based on our learnings from this research, we have released an open-source framework for serving and evaluating routers on [GitHub](https://github.com/lm-sys/RouteLLM), which we believe is the first framework of its kind supporting LLM routers. We are also releasing all the routers and datasets we have trained on [HuggingFace](https://huggingface.co/routellm) for public use. +Based on our learnings from this research, we have released an open-source framework for serving and evaluating routers on [GitHub](https://github.com/lm-sys/RouteLLM), which we believe is the first framework of its kind supporting LLM routers. We are also releasing all our routers and datasets on [HuggingFace](https://huggingface.co/routellm) for public use. We are excited to see what you build on top of this! Please let us know if you face any issues or have any suggestions. For the full details, please refer to our [arXiv](https://arxiv.org/abs/2406.18665) paper. diff --git a/public/images/blog/routellm/indep-benchmarks-llama.png b/public/images/blog/routellm/indep-benchmarks-llama.png new file mode 100644 index 0000000000000000000000000000000000000000..99b86a9738a360ec48df5e47f0fb4bf9c0beb156 GIT binary patch literal 48976 zcma&OWmr{T^e(z-Ns;bS2`L4nq$NdCy1S)8I;5nfQKY-O8>G8Iy1Tp2-2Be}+~?d6 z_qiXG{lH#x%{Au8cf4Z-%1Vo&qY$A$AP{s3abbA~1Xd6NfmwTo2!6xGd;1mo!(K$i z{)?57{Wo1(Lx_~Fy|smvy@jbBg`=UZovD>2D-$OZD+7gzy}h*^H#76s|MLYVD_djc zt__thU=d_%aaB7Ae=sNnQxZ~mK`)Fye$@lYd&YZQUt52?Qk zW_T0-D%JA8$Br5*n3`8cV=%t=Ec};#L7ORBn07%{sxSQA7j35Y^6r6iu14yoO;p0M zd$X1n?WxCq&mE_vM*es2A`&>PY>$=%G06G)CkxfDPlxzNOxSWQWu8U2n;IOxr4&{gGhy4Z z=XP^*dxnBSMN9jPo_@?T%Fo|_WqVs|g&yH@aSy>9oS$ z-QA6nL?tJa2jek*I)2da3LB}h(bv<{Q>k}kOyqX>WOu77l|IsN=1MKBMN{1Fi-|W@ zZY(1tBs5+1RlUrdUb*NMC8albU{wm1FO_B!X_9eFkiFUJp|+7{l>H)5VRxKujlhM9 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