Concept-Reversed Winograd Schema Challenge: Evaluating and Improving Robust Reasoning in Large Language Models via Abstraction (2025.naacl-short)
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| Challenge: | Recent research has revealed that Large Language Models (LLMs) often experience issues with hallucinations and unreliable reasoning due to semantic associations and superficial logical chains. |
| Approach: | They propose a concept-reversed Winograd Schema Challenge dataset to evaluate the robustness of Large Language Models (LLMs) they propose Abstraction-of-Thought (AoT) method for recovering adversarial cases to normal cases using conceptual abstraction to improve LLMs’ robustness and consistency in reasoning. |
| Outcome: | The proposed method improves LLMs’ robustness and consistency in reasoning under adversarial and long-tail scenarios. |
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