Summary of Reasoning Aware Self-consistency: Leveraging Reasoning Paths For Efficient Llm Sampling, by Guangya Wan et al.
Reasoning Aware Self-Consistency: Leveraging Reasoning Paths for Efficient LLM Sampling
by Guangya Wan, Yuqi Wu, Jie Chen, Sheng Li
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed Reasoning-Aware Self-Consistency (RASC) framework enhances the sampling efficiency and faithfulness of Large Language Models (LLMs) by dynamically evaluating both outputs and rationales. RASC assesses the quality of reasoning and the consistency of answers for each generated sample, guiding early stopping decisions and rationale selection. The framework employs criteria-based stopping and weighted majority voting to make informed choices on when to halt sampling and which rationale to select. Experimental results demonstrate that RASC outperforms existing methods, reducing sample usage by approximately 70% while maintaining accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make Large Language Models (LLMs) more reliable is introduced in this research. The model, called Reasoning-Aware Self-Consistency (RASC), helps LLMs provide better answers and be more efficient. RASC checks the quality of each answer and the reasoning behind it, then uses that information to decide when to stop generating answers and which one to choose. This approach works well on different types of questions and reduces the number of answers generated by about 70% without sacrificing accuracy. |
Keywords
» Artificial intelligence » Early stopping
