Summary of Soft Self-consistency Improves Language Model Agents, by Han Wang et al.
Soft Self-Consistency Improves Language Model Agents
by Han Wang, Archiki Prasad, Elias Stengel-Eskin, Mohit Bansal
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 This research paper presents a novel approach to improving the performance of large language models (LLMs) in generating multiple answers sequentially. The authors identify limitations in current “sample and select” methods, such as self-consistency (SC), which rely on majority voting to score answers. They demonstrate that SC fails to provide consistent gains when tasks have many distinct and valid answers, making it prohibitively expensive for interactive tasks. To address this issue, the authors introduce Soft Self-Consistency (SOFT-SC), a continuous scoring method that computes model likelihoods to select answers even when they are sparsely distributed. SOFT-SC improves both performance and efficiency on long-horizon interactive tasks, requiring half as many samples as SC for comparable or better performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make language models better by giving them a way to choose the best answer from multiple options. The current method uses majority voting, but this doesn’t work well when there are many right answers. To fix this, the authors came up with a new approach called Soft Self-Consistency (SOFT-SC). It’s like a continuous score that helps the model pick the best answer even when they’re not all the same. This makes it more efficient and effective for tasks that require generating multiple answers in a row. |
