The SelfHarmonized Chain of Thought (ECHO) method offers an enhancement in generating reasoning chains for large language models (LLMs) by using an iterative refinement process.

ECHO begins by clustering a given dataset of questions based on their semantic similarity using a sentence transformer, like SentenceBERT, which embeds the questions into a vector space.

Each cluster then has a representative question selected, and the model generates a reasoning chain for that question using zeroshot Chain of Thought (CoT) prompting, breaking down the solution into intermediate steps. What makes ECHO different is its dynamic prompting mechanism. During each iteration, one reasoning chain is randomly chosen for regeneration, while the remaining chains from other clusters are used as incontext examples to guide improvement.

This allows reasoning patterns to crosspollinate, meaning if one chain contains errors or gaps, other chains can help fill in those weaknesses, creating a more harmonized and robust set of reasoning steps.

One of the major issues in previous methods like AutoCoT was the risk of errors spreading when similar but incorrect reasoning chains were generated. ECHO addresses this problem by refining and crossvalidating reasoning chains between clusters, ensuring consistency and accuracy.

The iterative nature of ECHO means that all reasoning chains get a chance to be regenerated and improved multiple times, leading to a harmonized set of solutions where errors are gradually eliminated. Though ECHO's performance improvement over traditional methods like AutoCoT is around 2.3%, the real strength of the approach lies in its ability to refine reasoning for complex tasks like arithmetic, commonsense reasoning, and symbolic logic.

The method’s adaptive, selflearning nature ensures that logical consistency is achieved over time, making it particularly useful for domains where precise, stepbystep reasoning is critical.

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SelfHarmonized Chain of Thought
https://arxiv.org/pdf/2409.04057

Code repo: https://github.com/Xalp/ECHO

00:00 Chain of Thoughts Intro
03:18 Auto CoT's problem
04:12 ECHO SelfHarmonized CoT
10:10 ECHO specific datasets
11:59 A new idea to combine Strategic CoT to ECHO
15:36 Simple ECHO CoT example
18:13 Performance benchmark ECHO CoT
20:34 Ideas how to improve on ECHO CoT


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