Title: DIVER : Large Language Model Decoding with Span-Level Mutual Information Verification
Primary area: foundation or frontier models, including LLMs
Keywords: Large language model, decoding

Abstract: Large language models (LLMs) have shown impressive capabilities in adapting to various tasks when provided with task-specific instructions. However, LLMs using standard decoding strategies often struggle with deviations from the inputs. Intuitively, compliant LLM outputs should reflect the information present in the input, which can be measured by pointwise mutual information (PMI) scores. Therefore, we propose DIVER, a novel approach that enhances LLM Decoding through span-level PMI VERification. During inference, DIVER first identifies divergence steps that may lead to multiple candidate spans. Subsequently, it calculates the PMI scores by assessing the loglikelihood gains of the input if the candidate spans are generated. Finally, the optimal span is selected based on the PMI re-ranked output distributions. We evaluate our method across various downstream tasks, and empirical results demonstrate that DIVER significantly outperforms existing decoding methods in both performance and versatility