Papers with extractive QA
INDUS: Effective and Efficient Language Models for Scientific Applications (2024.emnlp-industry)
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Bishwaranjan Bhattacharjee, Aashka Trivedi, Masayasu Muraoka, Muthukumaran Ramasubramanian, Takuma Udagawa, Iksha Gurung, Nishan Pantha, Rong Zhang, Bharath Dandala, Rahul Ramachandran, Manil Maskey, Kaylin Bugbee, Michael Little, Elizabeth Fancher, Irina Gerasimov, Armin Mehrabian, Lauren Sanders, Sylvain Costes, Sergi Blanco-Cuaresma, Kelly Lockhart, Thomas Allen, Felix Grezes, Megan Ansdell, Alberto Accomazzi, Yousef El-Kurdi, Davis Wertheimer, Birgit Pfitzmann, Cesar Berrospi Ramis, Michele Dolfi, Rafael Lima, Panagiotis Vagenas, S. Mukkavilli, Peter Staar, Sanaz Vahidinia, Ryan McGranaghan, Tsengdar Lee
| Challenge: | Large language models trained on general domain corpora showed remarkable results on natural language processing tasks. |
| Approach: | They develop a suite of large language models trained on general domain corpora that address NLP tasks and smaller versions of them created using knowledge distillation. |
| Outcome: | The proposed models outperform general-purpose and domain-specific encoders on new and existing tasks and in industrial settings. |
Detecting (Un)answerability in Large Language Models with Linear Directions (2026.eacl-long)
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| Challenge: | Large language models (LLMs) often respond confidently to questions even when they lack the necessary information, leading to inaccurate responses or hallucinations. |
| Approach: | They propose an approach for identifying a direction in the model’s activation space that captures unanswerability and uses it for classification. |
| Outcome: | The proposed method detects unanswerable questions and generalizes better across datasets than existing prompt-based and classifier-based approaches. |
Using contradictions improves question answering systems (2023.acl-short)
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| Challenge: | Existing systems that use contradiction to determine if a question is supported by background contexts do better than those that use entailment. |
| Approach: | They propose a method that incorporates contradiction in natural language inference (NLI) they propose to reformulate answers from QA systems as hypotheses and then select the best one based on the results. |
| Outcome: | The proposed method improves on multiple choice and extractive QA in two settings. |
Hyperdecoders: Instance-specific decoders for multi-task NLP (2022.findings-emnlp)
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| Challenge: | Recent work in NLP has examined the performance of large pretrained transformer-based models in multi-task settings, where a single model is evaluated on multiple tasks simultaneously. |
| Approach: | They propose a method for multi-tasking using a hypernetwork conditioned on the output of an encoder to generate a unique decoder adaptation for every input instance. |
| Outcome: | The proposed method outperforms previous methods for efficient multi-task fine-tuning and maps from encoder representations to output labels. |
From Multiple-Choice to Extractive QA: A Case Study for English and Arabic (2025.coling-main)
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Teresa Lynn, Malik H. Altakrori, Samar M. Magdy, Rocktim Jyoti Das, Chenyang Lyu, Mohamed Nasr, Younes Samih, Kirill Chirkunov, Alham Fikri Aji, Preslav Nakov, Shantanu Godbole, Salim Roukos, Radu Florian, Nizar Habash
| Challenge: | Recent years have brought about very fast developments in Natural Language Processing (NLP), but many other languages are overlooked due to limited resources. |
| Approach: | They propose to repurpose a multilingual BELEBELE dataset for a task of extractive QA in the style of machine reading comprehension. |
| Outcome: | The proposed approach could be used to extract QA in the style of machine reading comprehension. |
Learning What to Remember: Adaptive Probabilistic Memory Retention for Memory-Efficient Language Models (2025.findings-emnlp)
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| Challenge: | Adaptive Retention is a probabilistic, layer-wise token selection mechanism that learns which representations to keep under a strict global budget M. |
| Approach: | They propose a probabilistic token selection mechanism that learns which representations to keep under a strict global budget M. |
| Outcome: | The proposed method reduces memory usage by 35–45% while improving throughput by 1.8. |
Decomposition-Enhanced Training for Post-Hoc Attributions in Language Models (2026.eacl-long)
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Sriram Balasubramanian, Samyadeep Basu, Koustava Goswami, Ryan A. Rossi, Varun Manjunatha, Roshan Santhosh, Ruiyi Zhang, Soheil Feizi, Nedim Lipka
| Challenge: | Existing methods for extractive QA struggle in multi-hop, abstractive, and semi-extractive settings. |
| Approach: | They propose a method that prompts models to produce answer decompositions as intermediate reasoning steps. |
| Outcome: | The proposed method outperforms existing methods and matches or exceeds state-of-the-art frontier models. |
Momentum Contrastive Pre-training for Question Answering (2022.emnlp-main)
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| Challenge: | Existing methods for extractive Question Answering generate cloze-like queries different from natural questions in syntax structure, which could overfit pre-trained models to simple keyword matching. |
| Approach: | They propose a method to align the answer probability between cloze-like and natural query-passage sample pairs. |
| Outcome: | The proposed method improves on three benchmarking QA datasets on supervised and zero-shot scenarios. |
Relation-Guided Pre-Training for Open-Domain Question Answering (2021.findings-emnlp)
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| Challenge: | Existing QA datasets are imbalanced in some types of relations, which hurts generalization performance over long-tail questions. |
| Approach: | They propose a relation-guided pre-training framework to infer latent relations from a QA dataset . they then propose RGPT-QA to conduct extractive QA to get the target answer entity . |
| Outcome: | The proposed framework improves Exact Match accuracy on natural questions, TriviaQA, and WebQuestions. |
Free Lunch: Robust Cross-Lingual Transfer via Model Checkpoint Averaging (2023.acl-long)
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| Challenge: | Massively multilingual language models have shown strong performance in zero-shot (ZS-XLT) and few-shot cross-lingual transfer setups where models are fine-tuned on task data in a source language are transferred without any or with only a few annotated instances to the target language(s). |
| Approach: | They propose a method that averages different checkpoints during task fine-tuning to improve model robustness. |
| Outcome: | The proposed method overestimates model performance in cross-lingual transfer setups where models are evaluated at checkpoints that generalize best to validation instances in the target languages. |
Enhancing Extractive Question Answering in Multiparty Dialogues with Logical Inference Memory Network (2025.coling-main)
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| Challenge: | Existing models for multiparty dialogue question answering (QA) do not consider logical inference relations in multiparty dialogs, leading to suboptimal performance. |
| Approach: | They propose a memory network with logical inference for extractive QA in multiparty dialogues. |
| Outcome: | The proposed model achieves state-of-the-art on Molweni and FriendsQA benchmarks. |
Zero-Shot Dialogue State Tracking via Cross-Task Transfer (2021.emnlp-main)
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Zhaojiang Lin, Bing Liu, Andrea Madotto, Seungwhan Moon, Zhenpeng Zhou, Paul Crook, Zhiguang Wang, Zhou Yu, Eunjoon Cho, Rajen Subba, Pascale Fung
| Challenge: | Existing approaches to training a dialogue state tracking model require extensive annotated dialogue data. |
| Approach: | They propose to transfer cross-task knowledge from general question answering corpora to QA model that can handle zero-shot DST. |
| Outcome: | The proposed model improves existing zero-shot and few-shot results on MultiWoz and shows better generalization ability in unseen domains. |
LAVa: Layer-wise KV Cache Eviction with Dynamic Budget Allocation (2025.findings-emnlp)
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| Challenge: | Existing methods for cache compression are heuristic and lack dynamic budget allocation . cnn's john mccartney and johnny mccain present a new approach for cache eviction and dynamic budgets . |
| Approach: | They propose a unified framework for cache compression that minimizes information loss in transformer residual streams. |
| Outcome: | The proposed method consistently maintains top performance across task types. |
One For All & All For One: Bypassing Hyperparameter Tuning with Model Averaging for Cross-Lingual Transfer (2023.findings-emnlp)
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| Challenge: | Existing methods for zero-shot cross-lingual transfer are unreliable due to the lack of pretraining data. |
| Approach: | They propose to accumulatively average model snapshots from different runs into a single model. |
| Outcome: | The proposed protocol decouples performance maximization from hyperparameter tuning. |
Tokenization Consistency Matters for Generative Models on Extractive NLP Tasks (2023.findings-emnlp)
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| Challenge: | Pretrained sequence-to-sequence (seq2sequ) models have been widely used to solve extractive tasks, where parts of the input are extracted to form the desired output. |
| Approach: | They propose a simple fix to tokenization inconsistency that damages extractive nature of generative models by causing performance drop and hallucination. |
| Outcome: | The proposed model performs better in both in-domain and out-of-domain datasets with a notable average of +1.7 F1 gain when a BART model is trained on SQuAD and evaluated on 8 QA datasets. |
Tokenization and Representation Biases in Multilingual Models on Dialectal NLP Tasks (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) pre-trained on massive text data in many languages are preferred solution for various Natural Language processing tasks. |
| Approach: | They compare tokenization parity and information parity as representational biases in pre-trained models . they find TP is better predictor of performance on tasks reliant on syntactic and morphological cues . |
| Outcome: | The proposed model improves on dialect classification, topic classification, and extractive question answering tasks. |