Papers with answering
Open-Domain Conversational Question Answering with Historical Answers (2022.findings-aacl)
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| Challenge: | Existing approaches to conversational question answering are limited due to the large number of candidate documents. |
| Approach: | They propose a model that leverages historical answers to boost retrieval performance . they propose to use open-domain conversational question answering to solve these problems . |
| Outcome: | The proposed model outperforms baseline models in extractive and generative reader settings on OR-QuAC dataset. |
Generating Complex Question Decompositions in the Face of Distribution Shifts (2025.naacl-long)
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| Challenge: | Question decomposition has been found to improve large language models’ (LLMs) performance on complex question answering (QA) however, performance on the task remains dominated by supervised approaches, suggesting room for making LLMs better decomposers. |
| Approach: | They propose to generate synthetic decomposition data with only five annotated examples by extending recent advances in using LLM-as-judge and for reranking in novel ways. |
| Outcome: | The proposed approach generates synthetic decomposition data with only five examples over two benchmark datasets. |
ReEval: Automatic Hallucination Evaluation for Retrieval-Augmented Large Language Models via Transferable Adversarial Attacks (2024.findings-naacl)
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| Challenge: | Existing static benchmarks do not guarantee that models can use the provided evidence for answering, which is essential to avoid hallucination when the required knowledge is new or private. |
| Approach: | They propose to automatically perturb existing static one for dynamic evaluation by using a chatGPT framework and a set of open-domain QA datasets. |
| Outcome: | The proposed framework generates new test cases on two open-domain QA datasets and is human-readable and useful to trigger hallucination in LLMs. |
Crowdsourcing Question-Answer Meaning Representations (N18-2)
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| Challenge: | Existing datasets for predicate-argument relationships are lacking highly skilled and trained annotators. |
| Approach: | They propose a crowdsourcing scheme to generate question-answer pairs that represent predicate-argument relationships in sentences as a set of question-announcer pairs. |
| Outcome: | The proposed model covers the vast majority of predicate-argument relationships in existing datasets along with many previously under-resourced ones, including implicit arguments and relations. |
Mixture-of-Minds: Multi-Agent Reinforcement Learning for Table Understanding (2026.acl-long)
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Yuhang Zhou, Mingrui Zhang, Ke Li, Mingyi Wang, Qiao Liu, Qifei Wang, Jiayi Liu, Fei Liu, Serena Li, Weiwei LI, Mingze Gao, Abhishek Kumar, Xiangjun Fan, Zhuokai Zhao, Lizhu Zhang
| Challenge: | Large language models (LLMs) have shown promise on understanding and reasoning over tables, but current approaches remain limited. |
| Approach: | They propose a multi-agent framework that decomposes table reasoning into three specialized roles: planning, coding, and answering. |
| Outcome: | The proposed framework decomposes table reasoning into three specialized roles: planning, coding, and answering. |
A Question Answering Framework for Decontextualizing User-facing Snippets from Scientific Documents (2023.emnlp-main)
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| Challenge: | snippets are not meant to be read outside their original document. |
| Approach: | They propose a framework that decomposes the task into three stages: question generation, question answering, and rewriting. |
| Outcome: | The proposed framework decomposes the task into three stages: question generation, question answering, and rewriting. |
WorldTree: A Corpus of Explanation Graphs for Elementary Science Questions supporting Multi-hop Inference (L18-1)
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| Challenge: | Existing methods of automated inference do not provide enough gold explanations to train models . standardized science exams are a challenge task for question answering . |
| Approach: | They propose to manually construct a corpus of explanations for standardized science exams . they also provide an explanation-centered tablestore that contains the knowledge to construct these explanations . |
| Outcome: | The proposed model provides detailed explanations for standardized science exams . the authors show that the proposed model can be trained on the basis of gold explanations . |
Robustifying Multi-hop QA through Pseudo-Evidentiality Training (2021.acl-long)
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| Challenge: | Existing approaches to robustify multi-hop question answering models require expensive annotations. |
| Approach: | They propose a method to supervise answers with right reasoning chains without annotations . they compare answers confidence with and without evidence sentences to generate "pseudo-evidentiality" annotations. |
| Outcome: | The proposed model is accurate and robust in multi-hop reasoning. |
Question Generation and Answering for exploring Digital Humanities collections (2022.lrec-1)
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| Challenge: | Recent advances in representation learning of text have achieved impressive results on benchmark Natural Language Understanding (NLU) tasks. |
| Approach: | They propose a question answering paradigm that uses a BART Transformer based generative model to generate question data. |
| Outcome: | The proposed approach is validated on a new corpus of digitized archive collections of a French Social Science journal. |
Generative Context Pair Selection for Multi-hop Question Answering (2021.emnlp-main)
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Dheeru Dua, Cicero Nogueira dos Santos, Patrick Ng, Ben Athiwaratkun, Bing Xiang, Matt Gardner, Sameer Singh
| Challenge: | Recent studies have shown that discriminative training results in models that exploit these underlying biases to achieve a better held-out performance, without learning the right way to reason. |
| Approach: | They propose a generative context selection model for multi-hop QA that reasons about how the given question could have been generated given a context pair and not just independent contexts. |
| Outcome: | The proposed model outperforms the state-of-the-art model on hotpotQA while being comparable to the state of the art answering performance on adversarial held-out set. |
Probabilistic Tree-of-thought Reasoning for Answering Knowledge-intensive Complex Questions (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) are capable of answering knowledge-intensive complex questions with chain-of-thought reasoning. |
| Approach: | They propose a method to solve complex questions with a tree-of-thought approach using parametric knowledge and retrieved external knowledge to augment CoT reasoning. |
| Outcome: | The proposed approach outperforms SOTA methods on three Complex QA datasets under the open-domain setting. |
RFS-Guard: Detecting Reasoning Hallucinations via Cross-Phase Routing Focus in Large Reasoning Models (2026.acl-long)
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| Challenge: | Large reasoning models (LRMs) generate intermediate reasoning traces before the final answer, yet they remain vulnerable to reasoning hallucinations such as subtle arithmetic errors. |
| Approach: | They propose a Routing Focus Score (RFS) that measures how strongly cross-step attention routing aligns with semantic proximity derived from hidden-state cosine similarity. |
| Outcome: | The proposed framework detects and localizes hallucinations without external tools or repeated sampling. |
Basic Reading Distillation (2025.acl-long)
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| Challenge: | Large language models require high computational resources which limits their deployment in real-world applications. |
| Approach: | They propose to distill large language models into smaller language models by either knowledge distillation or task distillation. |
| Outcome: | The proposed model outperforms or performs comparable to over 20x bigger LLMs on language inference benchmarks and BIG-bench tasks. |