Siyi Liu, Qiang Ning, Kishaloy Halder, Zheng Qi, Wei Xiao, Phu Mon Htut, Yi Zhang, Neha Anna John, Bonan Min, Yassine Benajiba, Dan Roth
| Challenge: | Open domain question answering systems often rely on information retrieved from large collections of text to answer questions. |
| Approach: | They evaluate and benchmark three powerful Large Language Models with a dataset . they find that 25% of unambiguous open domain questions can lead to conflicting contexts . |
| Outcome: | The proposed model can't be used to answer questions with conflicting contexts . it can be fine tuned to provide richer information into the model's training . |
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| Challenge: | Existing work on citation generation has focused on unambiguous settings with single answers, failing to address the complexity of real-world scenarios. |
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DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text (2024.findings-naacl)
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| Challenge: | Retrievalaugmented LLMs have been used to ground LLM in external knowledge . a gap exists in the current landscape regarding the effectiveness of grounding LLM on heterogeneous knowledge sources. |
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Blinded by Generated Contexts: How Language Models Merge Generated and Retrieved Contexts When Knowledge Conflicts? (2024.acl-long)
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| Challenge: | Recent advances in augmenting Large Language Models (LLMs) with auxiliary information have significantly revolutionized their efficacy in knowledge-intensive tasks. |
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| Challenge: | Existing QA benchmarks that provide fixed answers to debatable questions are inadequate for evaluating their performance. |
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Open-Domain Question Answering (2020.acl-tutorials)
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| Challenge: | tutorial provides a comprehensive overview of cutting-edge research in open-domain question answering (QA) |
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Desiderata For The Context Use Of Question Answering Systems (2024.eacl-long)
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| Challenge: | Prior work has uncovered a set of common problems in state-of-the-art context-based question answering systems, such as a lack of attention to the context when it conflicts with a model’s parametric knowledge and a loss of consistency with their answers. |
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AmbigQA: Answering Ambiguous Open-domain Questions (2020.emnlp-main)
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| Challenge: | Existing open-domain question answering systems assume questions have a single welldefined answer. |
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QuAC: Question Answering in Context (D18-1)
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Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, Luke Zettlemoyer
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Conflicting Needles in a Haystack: How LLMs behave when faced with contradictory information (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities in retrieving and analyzing complex information, but their reliability in conflicting contexts remains poorly understood. |
| Approach: | They propose an adversarial extension of the Needle-in-a-Haystack framework in which three mutually exclusive “needles” are embedded within long documents. |
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Benchmarking LLM’s Capability in Reasoning over Conflicting Web References (2026.acl-long)
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| Challenge: | Large language models (LLMs) integrated with retrieval-augmented generation (RAG) are a dominant framework for building intelligent assistants. |
| Approach: | They propose a benchmark to evaluate LLMs' reasoning capability over real-world conflicting documents retrieved from the web. |
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