Papers with open-domain question-answering
Multi-Task Dense Retrieval via Model Uncertainty Fusion for Open-Domain Question Answering (2021.findings-emnlp)
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| Challenge: | Existing approaches to multitask dense retrieval are not effective due to corpus inconsistency. |
| Approach: | They propose to train individual dense passage retrievers for different open-domain question-answering tasks and aggregate their predictions during test time. |
| Outcome: | The proposed method achieves state-of-the-art performance on 5 benchmark QA datasets, with up to 10% improvement in top-100 accuracy compared to a joint-training multi-task DPR on SQuAD. |
Open-Domain Question-Answering for COVID-19 and Other Emergent Domains (2021.emnlp-demo)
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| Challenge: | a system for open-domain question-answering is developed for COVID-19 . small data size allows system to retrieve answers from large corpus of scientific papers . |
| Approach: | They propose an open-domain question-answering system that can retrieve answers from large corpus of COVID-19 papers. |
| Outcome: | The proposed open-domain question-answering system can retrieve answers from large corpus of COVID-19 scientific papers. |
Augmenting Pre-trained Language Models with QA-Memory for Open-Domain Question Answering (2023.eacl-main)
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| Challenge: | Existing methods for open-domain question-answering use an open book approach . a recent alternative is to retrieve from a collection of previously-generated question-annwer pairs . |
| Approach: | They propose a new QA system that augments a text-to-text model with a large memory of question-answer pairs and a task for the latent step of question retrieval. |
| Outcome: | The proposed system outperforms closed-book QA and can answer multi-hop questions. |
Neural Ranking with Weak Supervision for Open-Domain Question Answering : A Survey (2023.findings-eacl)
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| Challenge: | Neural ranking models require substantial amounts of relevance annotations, which is costly to scale. |
| Approach: | They propose to train a NR model with weak supervision instead of annotations . they use a structured overview of standard WS signals used for training a model . |
| Outcome: | The proposed approach reduces the cost of annotations by using weak supervision instead of a parametric model. |
Self-prompted Chain-of-Thought on Large Language Models for Open-domain Multi-hop Reasoning (2023.findings-emnlp)
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| Challenge: | Existing open-domain question-answering methods lack quality assurance . existing methods lack scalability and poor diversity, hindering LLMs' capabilities . |
| Approach: | They propose an open-domain multi-hop reasoning framework to answer multi-choice questions . they propose an adaptive sampler for in-context selection and self-prompted inference . |
| Outcome: | The proposed framework surpasses the existing SOTA methods on large-scale datasets and doubles the zero-shot performance of small-scale LLMs. |
WebWalker: Benchmarking LLMs in Web Traversal (2025.acl-long)
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Jialong Wu, Wenbiao Yin, Yong Jiang, Zhenglin Wang, Zekun Xi, Runnan Fang, Linhai Zhang, Yulan He, Deyu Zhou, Pengjun Xie, Fei Huang
| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of natural language processing tasks. |
| Approach: | They propose a benchmark to assess the ability of LLMs to perform web traversal by using an explore-critic paradigm. |
| Outcome: | The proposed framework mimics human-like web navigation through an explore-critic paradigm and demonstrates the effectiveness of RAG combined with WebWalker in real-world scenarios. |
Referral Augmentation for Zero-Shot Information Retrieval (2024.findings-acl)
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| Challenge: | Referral-augmented retrieval improves zero-shot document retrieval in a variety of tasks . prior work shows sparse models struggle to reconcile with dense models . |
| Approach: | They propose a technique that concatenates document indices with referrals from other documents that cite or link to the given document. |
| Outcome: | The proposed technique outperforms generative text expansion techniques on structured tasks and improves on ACL paper retrieval. |