Auto-Patching: Enhancing Multi-Hop Reasoning in Language Models

Multi-hop questions still stump large language models (LLMs), which struggle to link information across multiple reasoning steps. We introduce Auto-Patch, a novel method that dynamically patches hidden states during inference to enhance multi-hop reasoning in LLMs. Building on the PatchScopes framework, Auto-Patch selectively modifies internal representations using a learned classifier. Evaluated on the MuSiQue dataset, Auto-Patch improves the solve rate from 18.45% (baseline) to 23.63~±~0.7% (3 runs), narrowing the gap to Chain-of-Thought prompting (27.44%). Our results highlight the potential of dynamic hidden state interventions for advancing complex reasoning in LLMs.