Context Convergence Improves Answering Inferential Questions

Problem
This preprint addresses the gap in the literature regarding the performance of Large Language Models (LLMs) on inferential questions in open-domain Question Answering (QA). While LLMs excel at retrieving direct answers, their ability to derive answers from context is underexplored. The authors investigate how the structure and quality of textual passages influence LLM performance specifically for inferential reasoning tasks.

Method
The core technical contribution of this work is the introduction of “convergence” as a metric for constructing passages that enhance LLM performance on inferential questions. Convergence measures the effectiveness of sentences in eliminating incorrect answers. The authors utilize subsets of the TriviaHG dataset to create passages by combining sentences with varying levels of convergence. They evaluate six LLMs of different sizes and architectures, although specific architectures and training details are not disclosed. The experiments involve comparing the performance of LLMs on passages constructed based on convergence versus those selected using cosine similarity. Additionally, the authors explore the impact of ordering sentences by descending convergence on model performance.

Results
The results indicate that passages constructed from higher convergence sentences yield significantly improved answer accuracy compared to those selected by cosine similarity. The authors report a notable effect size, although specific numerical results and comparisons to baseline models are not detailed in the summary. Furthermore, the ordering of sentences by convergence level leads to a slight performance enhancement, suggesting that LLMs prioritize earlier, more informative cues in the passage.

Limitations
The authors acknowledge that their study is limited by the specific dataset used (TriviaHG) and the focus on a subset of LLMs, which may not generalize across all architectures or domains. They do not address potential biases in the dataset or the implications of using convergence as a sole criterion for passage construction. Additionally, the lack of detailed performance metrics and comparisons to state-of-the-art models limits the ability to fully assess the significance of their findings.

Why it matters
This work has important implications for the design of QA systems that require inferential reasoning capabilities. By establishing convergence as a practical signal for passage construction, the findings can guide future research in improving LLM performance on complex reasoning tasks. This could lead to more effective applications in domains such as legal reasoning, scientific inquiry, and any area where deriving answers from context is critical. The study also opens avenues for further exploration of passage structuring techniques and their impact on LLM interpretability and reasoning behavior.

Authors: Jamshid Mozafari, Bhawna Piryani, Adam Jatowt
Source: arXiv:2605.12370
URL: https://arxiv.org/abs/2605.12370v1