Summary of Ocean: Offline Chain-of-thought Evaluation and Alignment in Large Language Models, by Junda Wu et al.
OCEAN: Offline Chain-of-thought Evaluation and Alignment in Large Language Models
by Junda Wu, Xintong Li, Ruoyu Wang, Yu Xia, Yuxin Xiong, Jianing Wang, Tong Yu, Xiang Chen, Branislav Kveton, Lina Yao, Jingbo Shang, Julian McAuley
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach, called OCEAN, to evaluate and optimize the offline capabilities of Large Language Models (LLMs) in generating chain-of-thought reasoning paths. The authors use knowledge graphs (e.g., Wikidata5m) to provide feedback on the generated chains of thoughts, addressing the challenge of heterogeneity between LLM reasoning and KG structures. OCEAN models chain-of-thought reasoning as a Markov Decision Process (MDP) and evaluates the policy’s alignment with KG preference modeling using inverse propensity scores (IPS). The proposed estimator is theoretically proven to be unbiased and provides a lower bound on its variance. Empirically, OCEAN can optimize LLMs for generating chain-of-thought reasoning paths with higher estimated values without affecting their general abilities in downstream tasks or internal knowledge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores ways to improve the performance of Large Language Models (LLMs) by evaluating and optimizing their ability to generate chain-of-thought reasoning paths. Researchers use special databases called knowledge graphs to help LLMs learn from mistakes and correct themselves. The new approach, called OCEAN, helps LLMs generate better ideas and thoughts that align with what humans consider correct. This is important because it can improve the overall performance of LLMs without making them worse at other tasks. |
Keywords
» Artificial intelligence » Alignment
