Papers with multi-document reasoning
Logic Matters in Lightweight Hallucination Classification for RAG System (2026.acl-long)
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| Challenge: | Existing hallucination detection frameworks for RAGs lack robustness and performance . a compact model may lose track of precise information in retrieved segments or misinterpret a document's entailment score. |
| Approach: | They propose a lightweight, modular framework for hallucination detection in RAG systems . they capture logical relationships among retrieved documents within the vector space . |
| Outcome: | The proposed framework improves hallucination detection in RAG systems without complex architectures or pre-training on datasets. |
Towards Multi-Document Question Answering in Scientific Literature: Pipeline, Dataset, and Evaluation (2025.findings-emnlp)
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| Challenge: | Existing QA systems do not strictly enforce cross-document synthesis or exploit the explicit inter-paper structure that links sources. |
| Approach: | They propose a pipeline methodology for constructing a multi-document academic QA dataset . they detect communities based on citation networks and leverage Large Language Models . |
| Outcome: | The proposed method generates QA pairs related to multi-document content automatically and forms coherent communities based on citation networks and large language models. |
SciDQA: A Deep Reading Comprehension Dataset over Scientific Papers (2024.emnlp-main)
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| Challenge: | SciDQA is a dataset for question-answering that challenges language models to deeply understand scientific articles. |
| Approach: | They propose a new dataset for reading comprehension that challenges language models to deeply understand scientific articles consisting of 2,937 QA pairs. |
| Outcome: | The SciDQA dataset is based on 2,937 QA pairs and decontextualizes the content, tracks the source document across different versions, and incorporates a bibliography for multi-document question-answering. |
MDBench: A Synthetic Multi-Document Reasoning Benchmark Generated with Knowledge Guidance (2025.findings-acl)
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| Challenge: | Multi-document reasoning is an area of increasing relevance given LLM capabilities in handling longer-context inputs, but few benchmarks exist to rigorously examine model behavior in this setting. |
| Approach: | They propose a new dataset for evaluating LLMs on the task of multi-document reasoning that uses condensed structured seed knowledge to modify it through LLM-assisted edits. |
| Outcome: | The proposed method generates document sets and QA examples on a multi-document reasoning task using a synthetic generation process. |
Parallel Context-of-Experts Decoding for Retrieval Augmented Generation (2026.findings-acl)
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| Challenge: | Retrieval Augmented Generation relies on concatenating documents into a long context prompt, causing prefill bottlenecks. |
| Approach: | They propose a training-free framework that shifts evidence aggregation from attention to decoding . they treat retrieved documents as isolated "experts", synchronizing their predictions via a retrieval-aware extension of context-awful decoding. |
| Outcome: | The proposed framework shifts evidence aggregation from attention to decoding . it treats retrieved documents as isolated experts, synchronizing their predictions . |