Papers with divergent reasoning

2 papers
Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading Comprehension (P19-1)

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Challenge: Existing models for multi-hop reading comprehension only require a single-hop reasoning, meaning that the evidence needed to answer the question is scattered in a set of supporting documents.
Approach: They propose an interpretable 3-module system called Explore-Propose-Assemble reader (EPAr) that explores and connects relevant information from multiple documents in order to answer a question about the context.
Outcome: The proposed model approximates coarse-to-fine-grained comprehension behavior of human readers when facing multiple long documents.
DiCoRe: Enhancing Zero-shot Event Detection via Divergent-Convergent LLM Reasoning (2025.emnlp-main)

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Challenge: Understanding the complex event ontology, extracting domain-specific triggers from the passage, and structuring them appropriately overloads and limits the utility of Large Language Models (LLMs).
Approach: They propose a divergent-convergent reasoning framework that decouples the task of ED using Dreamer and Grounder.
Outcome: The proposed framework outperforms baselines on six datasets across five domains and nine LLMs, achieving 4–7% average gains over the best baseline.

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