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Open-Domain Question Answering

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평가표: 15
보존된 평가 기록: 109

Open-domain question answering is the task of question answering on open-domain datasets such as Wikipedia.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/wenhuchen/OTT-QA

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Open-domain Question Answering via Chain of Reasoning over Heterogeneous Knowledge

https://github.com/wenzhengzhang/entqa

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EntQA: Entity Linking as Question Answering

https://github.com/wissam-sib/dilbert

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Delaying Interaction Layers in Transformer-based Encoders for Efficient Open Domain Question Answering

https://github.com/wjdghks950/discern-and-answer

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Why So Gullible? Enhancing the Robustness of Retrieval-Augmented Models against Counterfactual Noise

https://github.com/wyu97/GenRead

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Generate rather than Retrieve: Large Language Models are Strong Context Generators

https://github.com/xfactlab/emnlp2023-damaging-retrieval

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Detrimental Contexts in Open-Domain Question Answering

https://github.com/xfactlab/emnlp2023-knowledge-corpus-error

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Knowledge Corpus Error in Question Answering

https://github.com/xiangyue9607/c-more

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C-MORE: Pretraining to Answer Open-Domain Questions by Consulting Millions of References

https://github.com/xnhyacinth/iag

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Awakening Augmented Generation: Learning to Awaken Internal Knowledge of Large Language Models for Question Answering

https://github.com/xsc1234/nir-prompt

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NIR-Prompt: A Multi-task Generalized Neural Information Retrieval Training Framework

https://github.com/xwhan/ProQA

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Progressively Pretrained Dense Corpus Index for Open-Domain Question Answering

https://github.com/yairf11/MUPPET

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Multi-Hop Paragraph Retrieval for Open-Domain Question Answering

https://github.com/yeliu918/dhr

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Dense Hierarchical Retrieval for Open-Domain Question Answering

https://github.com/ytyz1307zzh/minimally_edited_questions

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Exploring Contrast Consistency of Open-Domain Question Answering Systems on Minimally Edited Questions

https://github.com/yunx-z/combo

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Merging Generated and Retrieved Knowledge for Open-Domain QA

https://github.com/yuwvandy/kg-llm-mdqa

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Knowledge Graph Prompting for Multi-Document Question Answering

https://github.com/yuzhaouoe/sae-based-representation-engineering

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Steering Knowledge Selection Behaviours in LLMs via SAE-Based Representation Engineering

https://github.com/yxh-y/tablerag

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TableRAG: A Retrieval Augmented Generation Framework for Heterogeneous Document Reasoning

https://github.com/zhxlia/murre

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MURRE: Multi-Hop Table Retrieval with Removal for Open-Domain Text-to-SQL

https://github.com/ziems/llm-url

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Large Language Models are Built-in Autoregressive Search Engines

https://github.com/zli12321/qa_metrics

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PEDANTS: Cheap but Effective and Interpretable Answer Equivalence

https://github.com/zorazrw/filco

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Learning to Filter Context for Retrieval-Augmented Generation

https://github.com/zycdev/aiso

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Adaptive Information Seeking for Open-Domain Question Answering

https://worksheets.codalab.org/worksheets/0x39ba5559790b4099a7ff75f916ce19a4

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On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks

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