Summary of Mitigating Misleading Chain-of-thought Reasoning with Selective Filtering, by Yexin Wu et al.
Mitigating Misleading Chain-of-Thought Reasoning with Selective Filtering
by Yexin Wu, Zhuosheng Zhang, Hai Zhao
First submitted to arxiv on: 28 Mar 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 SelF-Reasoner model assesses the entailment relationship between a question and candidate reasoning chain to improve large language models’ capabilities in solving intricate questions. The approach leverages chain-of-thought (CoT) reasoning techniques, which have shown remarkable results by breaking down complex problems into step-by-step reasoning chains. However, CoT reasoning is contingent upon the quality of the reasoning process, which can be affected by indecomposable questions and erroneous reasoning chains. To address this challenge, SelF-Reasoner uses a novel filtering mechanism to selectively apply CoT reasoning when confidence in the reasoning chain is high, or predict answers directly when confidence is low. This approach improves fine-tuned T5 model performance on ScienceQA, ECQA, and LastLetter tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The SelF-Reasoner model helps big language models get better at solving tricky questions by thinking step-by-step. Right now, these models are great, but they can make mistakes if the question is too hard or if they’re not confident in their answer. The new approach looks at how well a question matches with the steps it takes to come up with an answer. If it’s a good match, the model uses this thinking approach; otherwise, it just gives an answer. This helps the model do better on science and other questions. |
Keywords
» Artificial intelligence » T5
