Papers with question-answering systems
Improving Vietnamese-English Cross-Lingual Retrieval for Legal and General Domains (2025.naacl-short)
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Toan Ngoc Nguyen, Nam Le Hai, Nguyen Doan Hieu, Dai An Nguyen, Linh Ngo Van, Thien Huu Nguyen, Sang Dinh
| Challenge: | Existing document retrieval systems focus on a single language, targeting resource-rich languages like English or Chinese. |
| Approach: | They propose auxiliary loss function and symmetrical training strategy for cross-lingual retrieval between Vietnamese and English . they propose a dataset that covers the general domain and extends to the legal field . |
| Outcome: | The proposed dataset significantly improves state-of-the-art models on cross-lingual retrieval tasks. |
CAPID: Context-Aware PII Detection for Question-Answering Systems (2026.eacl-srw)
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Mariia Ponomarenko, Sepideh Abedini, Masoumeh Shafieinejad, D. B. Emerson, Shubhankar Mohapatra, Xi He
| Challenge: | Existing approaches mainly redact all PII, disregarding the fact that some may be contextually relevant to the user’s question, resulting in a degradation of response quality. |
| Approach: | They propose a method that fine-tunes a locally owned small language model that filters sensitive information before it is passed to LLMs for QA. |
| Outcome: | The proposed approach outperforms baselines in span, relevance and type accuracy while preserving significantly higher utility under anonymization. |
ViMedAQA: A Vietnamese Medical Abstractive Question-Answering Dataset and Findings of Large Language Model (2024.acl-srw)
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| Challenge: | Existing abstractive question-answering datasets in Vietnamese are lacking . |
| Approach: | They propose to introduce a Vietnamese abstractive question-answering corpus to address this gap . they propose to use Vietnamese abstractives to generate answers to questions . |
| Outcome: | The proposed dataset examines the capability of large language models in the Vietnamese medical domain, including reasoning, memorizing and awareness of essential information. |
Unifying AI Tutor Evaluation: An Evaluation Taxonomy for Pedagogical Ability Assessment of LLM-Powered AI Tutors (2025.naacl-long)
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| Challenge: | Existing evaluations of large language models have been limited to subjective protocols and benchmarks. |
| Approach: | They propose a unified evaluation taxonomy with eight pedagogical dimensions based on key learning sciences principles to assess the pedagical value of LLM-powered AI tutor responses grounded in student mistakes or confusions in the mathematical domain. |
| Outcome: | The proposed taxonomy, benchmark, and human-annotated labels will streamline the evaluation process and help track the progress in AI tutors’ development. |
GOVERN: Gradient Orientation Vote Ensemble for Multi-Teacher Reinforced Distillation (2024.emnlp-industry)
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| Challenge: | Pre-trained language models have achieved remarkable performance in OpenQA, but for practical deployment, knowledge distillation is crucial to maintain high performance while operating under computational constraints. |
| Approach: | They propose an algorithm to perform unsupervised knowledge distillation without the guidance of labels to achieve 99.5% of performance. |
| Outcome: | The proposed algorithm achieves 99.5% of performance in a commercial question-answering system. |
A dataset and baselines for sequential open-domain question answering (D18-1)
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| Challenge: | Existing question-answering systems focus on answering individual questions, assuming they are devoid of context. |
| Approach: | They propose to ask multiple related questions in a dataset that includes human-authored questions. |
| Outcome: | The proposed system can answer human-authored questions better than existing systems. |
MRAG: A Modular Retrieval Framework for Time-Sensitive Question Answering (2025.findings-emnlp)
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| Challenge: | Existing methods for answering time-sensitive questions lack temporal reasoning . existing methods struggle with these time-intensive questions, authors say . |
| Approach: | They propose a temporal-based question-answering framework that integrates temporal perturbations and gold evidence labels into a question processing framework. |
| Outcome: | The proposed framework outperforms baseline retrieval methods in retrieval performance. |
When and How to Augment Your Input: Question Routing Helps Balance the Accuracy and Efficiency of Large Language Models (2025.findings-naacl)
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| Challenge: | augmented generation of knowledge-based long-tail questions can be useful for large language models, but can cause significant latency. |
| Approach: | They propose an adaptive question routing framework that uses a query router to augment input to the right time. |
| Outcome: | The proposed framework surpasses existing approaches in accuracy and efficiency on benchmarks such as AmbigNQ, HotpotQA, MMLU-STEM, and PopQA. |
Knowing What’s Missing: Assessing Information Sufficiency in Question Answering (2026.findings-eacl)
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| Challenge: | Existing approaches to assess whether a given context contains sufficient information fail on factual questions. |
| Approach: | They propose a framework that asks a model to reason about what information is missing . this framework generates more accurate sufficiency judgments while articulating any information gaps . |
| Outcome: | The proposed framework produces more accurate sufficiency judgments while clearly articulating any information gaps. |
Closed-book Question Generation via Contrastive Learning (2023.eacl-main)
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| Challenge: | Recent studies on open-book QG have achieved promising progress, but generating natural questions under a more practical closed-book setting remains a challenge. |
| Approach: | They propose a QG model that stores more information in its parameters through contrastive learning and an answer reconstruction module. |
| Outcome: | The proposed model outperforms baselines in automatic evaluation and human evaluation on a public dataset and a new WikiCQA dataset. |
DeepREF: A Framework for Optimized Deep Learning-based Relation Classification (2022.lrec-1)
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| Challenge: | Existing frameworks for relation extraction (RE) are limited due to lack of implementation details. |
| Approach: | They propose to use deep learning to develop relation extraction systems using deep learning models. |
| Outcome: | The proposed framework is inspired by the OpenNRE and REflex existing frameworks. |
Are Red Roses Red? Evaluating Consistency of Question-Answering Models (P19-1)
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| Challenge: | Existing question-answering systems are limited in their ability to test reasoning and comprehension. |
| Approach: | They propose a method to automatically extract implications from QA datasets to evaluate models' consistency . they use a heuristic to generate such questions and retrain models with implication-augmented data . |
| Outcome: | The proposed method shows that generated implications are well formed and valid . retraining with implication-augmented data improves consistency on both synthetic and human-generated implications. |
Thread: A Logic-Based Data Organization Paradigm for How-To Question Answering with Retrieval Augmented Generation (2025.emnlp-main)
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Kaikai An, Fangkai Yang, Liqun Li, Junting Lu, Sitao Cheng, Shuzheng Si, Lu Wang, Pu Zhao, Lele Cao, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Baobao Chang
| Challenge: | Recent advances in retrieval-augmented generation (RAG) have substantially improved question-answering systems, particularly for factoid ‘5Ws’ questions. |
| Approach: | They propose a data organization paradigm where large language models transform documents into more structured and loosely interconnected LUs. |
| Outcome: | Experiments in open-domain and industrial settings show that the proposed paradigm outperforms existing paradigms and shows high adaptability across diverse document formats. |
Dr ChatGPT tell me what I want to hear: How different prompts impact health answer correctness (2023.emnlp-main)
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| Challenge: | Using the TREC Misinformation dataset, we empirically evaluate ChatGPT to show not just its effectiveness but reveal that knowledge passed in the prompt can bias the model to the detriment of answer correctness. |
| Approach: | They empirically evaluate ChatGPT to find out whether a prompt can bias the model to the detriment of answer correctness. |
| Outcome: | The proposed model can be biased to the detriment of answer correctness by using retrieved-then-generate pipelines and how a user phrases their question as well as the question type. |