# Context Convergence Improves Answering Inferential Questions

arXiv: https://arxiv.org/abs/2605.12370 · HTML: https://mlsanity.com/paper/2605.12370/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 34 references that are in the map.
- First posted on arXiv 12 May 2026.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning](https://mlsanity.com/paper/2503.09516/) (2025)
- [ReasonIR: Training Retrievers for Reasoning Tasks](https://mlsanity.com/paper/2504.20595/) (2025)
- [ReasonRank: Empowering Passage Ranking with Strong Reasoning Ability](https://mlsanity.com/paper/2508.07050/) (2025)
- [DIVER: A Multi-Stage Approach for Reasoning-intensive Information Retrieval](https://mlsanity.com/paper/2508.07995/) (2025)
- [RaDeR: Reasoning-aware Dense Retrieval Models](https://mlsanity.com/paper/2505.18405/) (2025)
- [Choose Your QA Model Wisely: A Systematic Study of Generative and Extractive Readers for Question Answering](https://mlsanity.com/paper/2203.07522/) (2022)
- [TriviaHG: A Dataset for Automatic Hint Generation from Factoid Questions](https://mlsanity.com/paper/2403.18426/) (2024)
- [Exploring Hint Generation Approaches in Open-Domain Question Answering](https://mlsanity.com/paper/2409.16096/) (2024)
- [Navigating the Landscape of Hint Generation Research: From the Past to the Future](https://mlsanity.com/paper/2404.04728/) (2024)
- [BoolQuestions: Does Dense Retrieval Understand Boolean Logic in Language?](https://mlsanity.com/paper/2411.12235/) (2024)
- [Wrong Answers Can Also Be Useful: PlausibleQA -- A Large-Scale QA Dataset with Answer Plausibility Scores](https://mlsanity.com/paper/2502.16358/) (2025)
- [WikiHint: A Human-Annotated Dataset for Hint Ranking and Generation](https://mlsanity.com/paper/2412.01626/) (2024)

## Abstract (arXiv)

While Large Language Models (LLMs) are widely used in open-domain Question Answering (QA), their ability to handle inferential questions-where answers must be derived rather than directly retrieved-remains still underexplored. This study investigates how the structure and quality of passages influence LLM performance on such questions. We focus on convergence, a measure of how effectively sentences (hints) eliminate incorrect answers, as a criterion for constructing passages. Using subsets of the TriviaHG dataset, we form passages by combining sentences with varying convergence levels and evaluate six LLMs of different sizes and architectures. Our results show that passages built from higher convergence sentences lead to substantially better answer accuracy than those selected by cosine similarity, indicating that convergence captures meaningful relevance for inferential reasoning. Additionally, ordering sentences by descending convergence slightly improves performance, suggesting that LLMs tend to prioritize earlier, information-rich cues. These findings highlight convergence as a practical signal for guiding passage construction and analyzing inferential reasoning behavior in LLMs.