From 95cc14d1505c76fe124f56b233a864a971760893 Mon Sep 17 00:00:00 2001 From: Asankhaya Sharma Date: Thu, 17 Oct 2024 10:03:41 +0800 Subject: [PATCH] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index a89bbfa..0ea3b1c 100644 --- a/README.md +++ b/README.md @@ -162,6 +162,7 @@ or your own code where you want to use the results from optillm. You can use it | Monte Carlo Tree Search | `mcts` | Uses MCTS for decision-making in chat responses | | PV Game | `pvg` | Applies a prover-verifier game approach at inference time | | CoT Decoding | N/A for proxy | Implements chain-of-thought decoding to elicit reasoning without explicit prompting | +| Entropy Decoding | N/A for proxy | Implements adaptive sampling based on the uncertainy of tokens during generation | ## Implemented plugins @@ -273,6 +274,7 @@ called patchflows. We saw huge performance gains across all the supported patchf ## References +- [Entropy Based Sampling and Parallel CoT Decoding](https://github.com/xjdr-alt/entropix) - [Implementation](https://github.com/codelion/optillm/blob/main/optillm/entropy_decoding.py) - [Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation](https://arxiv.org/abs/2409.12941) - [Evaluation script](https://github.com/codelion/optillm/blob/main/scripts/eval_frames_benchmark.py) - [Writing in the Margins: Better Inference Pattern for Long Context Retrieval](https://www.arxiv.org/abs/2408.14906) - [Inspired the implementation of the memory plugin](https://github.com/codelion/optillm/blob/main/optillm/plugins/memory_plugin.py) - [Chain-of-Thought Reasoning Without Prompting](https://arxiv.org/abs/2402.10200) - [Implementation](https://github.com/codelion/optillm/blob/main/optillm/cot_decoding.py)