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ACL Findings2023

RobustQA: Benchmarking the Robustness of Domain Adaptation for Open-Domain Question Answering

Rujun Han, Peng Qi, Yuhao Zhang, Lan Liu, Juliette Burger, William Yang Wang, Zhiheng Huang, Bing Xiang, Dan Roth

摘要

Open-domain question answering (ODQA) is a crucial task in natural language processing. A typical ODQA system relies on a retriever module to select relevant contexts from a large corpus for a downstream reading comprehension model. Existing ODQA datasets consist mainly of Wikipedia corpus, and are insufficient to study models' generalizability across diverse domains, as models are trained and evaluated on the same genre of data. We propose RobustQA 1 , a novel benchmark consisting of datasets from 8 different domains, which facilitates the evaluation of ODQA's domain robustness. To build RobustQA, we annotate QA pairs in retrieval datasets with rigorous quality control. We further examine improving QA performances by incorporating unsupervised learning methods with target-domain corpus and adopting large generative language models. These methods can effectively improve model performances on RobustQA. However, experimental results demonstrate a significant gap from in-domain training, suggesting that Ro-bustQA is a challenging benchmark to evaluate ODQA domain robustness.

1 Datasets and their processing code can be found here: https://github.com/rujunhan/RobustQA-data FiQA-Finance Question: Why do investors buy stock that had appreciated? Document: Imagine how foolish the people that bought Apple at 100musthavefelt.Itwasuptenfoldforthe100 must have felt. It was up tenfold for the 10 it traded at just years prior, how could it go any higher? Stocks have no memory. A stock's earnings may grow and justify the new higher price people are willing to pay. When FB came public, I remarked how I'd analyze the price and felt it was overvalued until its earnings came up. Just because it's gone down ever since, doesn't make it a buy, yet.

Question: What techniques, tricks or otherwise have you used to get upgrades on flights? Document: ... but I think the best way to get upgraded is to fly a lot with the airline. Generally when the flight's overbooked in one class, and they're trying to pick which person to upgrade, frequent flyer status is the first metric they use. The higher your status, the higher up the list you go! Having a high status with a partner airline can work too, high tiers with a partner airline usually comes below the airline's own frequent flyers, but above everyone else. Otherwise, if you're flying on your own that'll help in the event that there aren't enough frequent flyer to upgrade to free the required number of seats! Offering to pay may be an option too -if they're pretty full they may offer you a low price to upgrade.

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引用它的顶会 Paper6

  1. RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question AnsweringRujun Han, Yuhao Zhang, Peng Qi, Yumo Xu 等EMNLP 2024 · 10 次引用
  2. Designing Staged Evaluation Workflows for LLMs: Integrating Domain Experts, Lay Users, and Model-Generated Evaluation CriteriaAnnalisa Szymanski, Simret Araya Gebreegziabher, Oghenemaro Anuyah, Ronald A. Metoyer 等CHI 2026 · 1 次引用
  3. Completing Missing Annotation: Multi-Agent Debate for Accurate and Scalable Relevant Assessment for IR BenchmarksMinjeong Ban, Jeonghwan Choi, Hyangsuk Min, Nicole Hee-Yeon Kim 等ICLR 2026 · 1 次引用
  4. RAG-Zeval: Enhancing RAG Responses Evaluator through End-to-End Reasoning and Ranking-Based Reinforcement LearningKun Li, Yunxiang Li, Tianhua Zhang, Hongyin Luo 等EMNLP 2025 · 1 次引用
  5. Dancing in Chains: Reconciling Instruction Following and Faithfulness in Language ModelsZhengxuan Wu, Yuhao Zhang, Peng Qi, Yumo Xu 等EMNLP 2024

它引用的 Paper5

  1. In Search of Lost Domain GeneralizationIshaan Gulrajani, David Lopez-PazICLR 2021 · 1,416 次引用
  2. Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware SamplingSebastian Hofstätter, Sheng-Chieh Lin, Jheng-Hong Yang, Jimmy Lin 等SIGIR 2021 · 297 次引用
  3. TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence SelectionSiddhant Garg, Thuy Vu, Alessandro MoschittiAAAI 2020 · 229 次引用
  4. Task-aware Retrieval with InstructionsAkari Asai, Timo Schick, Patrick Lewis, Xilun Chen 等ACL Findings 2023 · 45 次引用
  5. ECONET: Effective Continual Pretraining of Language Models for Event Temporal ReasoningRujun Han, Xiang Ren, Nanyun PengEMNLP 2021 · 31 次引用

相关 Paper

  1. Robust Domain Adaptation for Machine Reading ComprehensionLiang Jiang, Zhenyu Huang, Jia Liu, Zujie Wen 等AAAI 2023 · 1 次引用
  2. Exploiting Abstract Meaning Representation for Open-Domain Question AnsweringCunxiang Wang, Zhikun Xu, Qipeng Guo, Xiangkun Hu 等ACL Findings 2023 · 5 次引用
  3. Grape: Knowledge Graph Enhanced Passage Reader for Open-domain Question AnsweringMingxuan Ju, Wenhao Yu, Tong Zhao, Chuxu Zhang 等EMNLP Findings 2022 · 21 次引用
  4. Open-domain Question Answering via Chain of Reasoning over Heterogeneous KnowledgeKaixin Ma, Hao Cheng, Xiaodong Liu, Eric Nyberg 等EMNLP Findings 2022 · 16 次引用
  5. One Question Answering Model for Many Languages with Cross-lingual Dense Passage RetrievalAkari Asai, Xinyan Yu, Jungo Kasai, Hanna HajishirziNeurIPS 2021 · 86 次引用
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