Papers with open-domain question answering systems
Reader-Guided Passage Reranking for Open-Domain Question Answering (2021.findings-acl)
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| Challenge: | Current open-domain question answering systems follow a Retriever-Reader architecture . current systems do not use a reranker, which reranked passages based on top predictions of the reader . |
| Approach: | They propose a reader-guIDEd reranking method that reranked passages based on top predictions . they show that RIDER achieves 10 to 20 absolute gains in top-1 retrieval accuracy . |
| Outcome: | The proposed method achieves 10 to 20 gains in top-1 retrieval accuracy and 1 to 4 Exact Match gains without training. |
The Dangers of trusting Stochastic Parrots: Faithfulness and Trust in Open-domain Conversational Question Answering (2023.findings-acl)
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Sabrina Chiesurin, Dimitris Dimakopoulos, Marco Antonio Sobrevilla Cabezudo, Arash Eshghi, Ioannis Papaioannou, Verena Rieser, Ioannis Konstas
| Challenge: | Empirical studies of dialogue have shown that people use different kinds of context-dependent linguistic behavior to indicate grounding, including use of fragments, ellipsis and pronominal reference. |
| Approach: | They propose to use open-domain question answering systems as test-bed for task based dialog generation and compare open- and closed-book models to test their hypothesis. |
| Outcome: | The proposed model parrots user input while providing an unfaithful response. |
Efficient Passage Retrieval with Hashing for Open-domain Question Answering (2021.acl-short)
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| Challenge: | Open-domain question answering systems often require large memory to run because of the massive size of their passage index. |
| Approach: | They propose a memory-efficient neural retrieval model that integrates a learning-to-hash technique into the state-of-the-art Dense Passage Retriever to represent the passage index using compact binary codes. |
| Outcome: | The proposed model significantly reduces memory cost from 65GB to 2GB without loss of accuracy on two open-domain question answering benchmarks. |
Diverse Multi-Answer Retrieval with Determinantal Point Processes (2022.coling-1)
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| Challenge: | Existing open domain question answering systems provide a single answer to ambiguous questions. |
| Approach: | They propose a re-ranking approach that takes query-passage relevance and passage-passance correlation into account to retrieve passages that are query-relevant and diverse. |
| Outcome: | The proposed method outperforms state-of-the-art on the AmbigQA dataset. |
Silver Retriever: Advancing Neural Passage Retrieval for Polish Question Answering (2024.lrec-main)
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| Challenge: | lexical approaches to find passages have outperformed lexicals due to their superior performance . however, for some languages, such as Polish, few models are available . a recent study shows that neural retrievers are more efficient and efficient than lexica. |
| Approach: | They present a neural retriever for Polish trained on a diverse collection of manual and weakly labeled datasets. |
| Outcome: | The proposed model outperforms lexical retrieval models in Polish on three retrieval tasks. |
ConvX: A Lightweight Converter to Bridge Indexed Dense Representations and Large Language Models for Retrieval-Augmented Generation (2026.findings-acl)
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| Challenge: | Existing RAG pipelines suffer from critical efficiency limitations due to their complexity and complexity. |
| Approach: | They propose a compression-based RAG framework that directly leverages indexed dense representations produced by a retriever, substituting to long text contexts. |
| Outcome: | Empirical results show that the proposed model achieves competitive performances compared to the state-of-the-art model that uses a large ad-hoc context compressor while offering substantially improved inference efficiency. |