Stepwise Informativeness Search for Efficient and Effective LLM Reasoning
This addresses a specific bottleneck in LLM reasoning for tasks requiring long-context processing, but it is incremental as it builds on existing inference-time methods.
The paper tackles the problem of LLMs losing focus and generating unreliable rationales in multi-step reasoning by proposing a stepwise informativeness search framework, which improves reasoning accuracy on four datasets by generating higher-quality rationales with reduced errors and redundancy.
Advances in Large Language Models (LLMs) have significantly improved multi-step reasoning through generating free-text rationales. However, recent studies show that LLMs tend to lose focus over the middle of long contexts. This raises concerns that as reasoning progresses, LLMs may overlook information in earlier steps when decoding subsequent steps, leading to generate unreliable and redundant rationales. To address this, we propose guiding LLMs to generate more accurate and concise step-by-step rationales by (1) proactively referencing information from underutilized prior steps, and (2) minimizing redundant information between new and existing steps. We introduce stepwise informativeness search, an inference-time tree search framework incorporating two selection heuristics: grounding-guided selection which prioritizes steps paying higher attention over underutilized steps; and novelty-guided selection which encourages steps with novel conclusions. During rationale generation, we use a self-grounding strategy that prompts LLMs to explicitly reference relevant prior steps to provide premises before deduction at each step. Experimental results on four reasoning datasets demonstrate that our approach improves reasoning accuracy by generating higher-quality rationales with reduced errors and redundancy.