https://github.com/deokhk/dotter
공식 코드Denoising Table-Text Retrieval for Open-Domain Question Answering
Papers with Code 아카이브
Open-Domain Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 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/disi-unibo-nlp/medgenie
공식 코드https://github.com/dmis-lab/gener
공식 코드https://github.com/dmis-lab/tour
공식 코드Optimizing Test-Time Query Representations for Dense Retrieval
https://github.com/dongryeollee96/askcq
공식 코드Asking Clarification Questions to Handle Ambiguity in Open-Domain QA
https://github.com/dongxiangjue/closed-book-question-generation-via-contrastive-learning
공식 코드https://github.com/edchengg/infoseek_eval
공식 코드Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?
https://github.com/ehsk/openqa-eval
공식 코드Evaluating Open-Domain Question Answering in the Era of Large Language Models
https://github.com/ellenmellon/inscit
공식 코드INSCIT: Information-Seeking Conversations with Mixed-Initiative Interactions
https://github.com/elviswxy/Gen-IR_PSPT
공식 코드https://github.com/elviswxy/rag_fairness
공식 코드Does RAG Introduce Unfairness in LLMs? Evaluating Fairness in Retrieval-Augmented Generation Systems
https://github.com/ethanleo-lyx/llmqa
공식 코드Harnessing Multi-Role Capabilities of Large Language Models for Open-Domain Question Answering
https://github.com/eunseongc/mgfid
공식 코드https://github.com/facebookresearch/CCQA
공식 코드CCQA: A New Web-Scale Question Answering Dataset for Model Pre-Training
https://github.com/facebookresearch/DPR
공식 코드https://github.com/facebookresearch/DrQA
공식 코드https://github.com/facebookresearch/ELI5
공식 코드https://github.com/facebookresearch/FiD
공식 코드Distilling Knowledge from Reader to Retriever for Question Answering
https://github.com/facebookresearch/GENRE
공식 코드https://github.com/facebookresearch/KILT
공식 코드https://github.com/facebookresearch/LAMA
공식 코드https://github.com/facebookresearch/PAQ
공식 코드PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them
https://github.com/facebookresearch/QA-Overlap
공식 코드Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets
https://github.com/facebookresearch/SpanBERT
공식 코드SpanBERT: Improving Pre-training by Representing and Predicting Spans
https://github.com/facebookresearch/UniK-QA
공식 코드https://github.com/facebookresearch/atlas
공식 코드Atlas: Few-shot Learning with Retrieval Augmented Language Models
https://github.com/facebookresearch/dpr-scale
공식 코드Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One?
https://github.com/facebookresearch/dpr-scale/tree/main/spar
공식 코드Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One?
https://github.com/fantabulous-j/class
공식 코드Pre-training Cross-lingual Open Domain Question Answering with Large-scale Synthetic Supervision
https://github.com/findzebra/fz-openqa
공식 코드https://github.com/gankim/tree-of-clarifications
공식 코드Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models
https://github.com/google-deepmind/tanq
공식 코드https://github.com/google-research/google-research
공식 코드Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval
https://github.com/google-research/language/tree/master/language/mentionmemory
공식 코드Mention Memory: incorporating textual knowledge into Transformers through entity mention attention
https://github.com/google-research/language/tree/master/language/multivec
공식 코드Sparse, Dense, and Attentional Representations for Text Retrieval
https://github.com/google-research/language/tree/master/language/orqa
공식 코드Latent Retrieval for Weakly Supervised Open Domain Question Answering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.