Summary of Concept-reversed Winograd Schema Challenge: Evaluating and Improving Robust Reasoning in Large Language Models Via Abstraction, by Kaiqiao Han et al.
Concept-Reversed Winograd Schema Challenge: Evaluating and Improving Robust Reasoning in Large Language Models via Abstraction
by Kaiqiao Han, Tianqing Fang, Zhaowei Wang, Yangqiu Song, Mark Steedman
First submitted to arxiv on: 15 Oct 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 A new evaluation dataset is proposed to assess the robustness of Large Language Models (LLMs) in performing logical reasoning. The Concept-Reversed Winograd Schema Challenge (CR-WSC) is designed to test LLMs’ ability to reason effectively, rather than relying on superficial associations and logical chains. By reversing the concepts associated with correct answers, researchers found that LLMs’ performance dropped significantly despite maintaining the same reasoning rationale. To improve LLMs’ robustness and consistency in reasoning, a novel prompt method called Abstraction-of-Thought (AoT) is introduced, which uses conceptual abstraction to recover adversarial cases to normal cases. Experimental results on CR-WSC demonstrate the effectiveness of AoT. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are really smart at figuring things out. But sometimes they make mistakes because they rely too much on what they already know instead of actually thinking about it. To test how well LLMs can really think, researchers created a new set of questions called the Concept-Reversed Winograd Schema Challenge (CR-WSC). They took a famous set of questions and changed them so that the correct answer was no longer the obvious choice. When they tested this with LLMs, they found that many of them struggled to get the right answers even though the reasoning was still the same. To help LLMs do better, researchers came up with a new way to ask questions called Abstraction-of-Thought (AoT). This helps LLMs to think more deeply and avoid making mistakes. |
Keywords
» Artificial intelligence » Prompt
