Papers with FBBS
Rectifying Belief Space via Unlearning to Harness LLMs’ Reasoning (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) exhibit sophisticated reasoning yet still generate incorrect answers. |
| Approach: | They propose a belief space rectification framework that suppresses spurious beliefs and enhances true ones to reduce erroneous reasoning and generalization. |
| Outcome: | The proposed framework reduces erroneous reasoning and improves generalization on three QA datasets and three LLMs. |