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Open-Domain Question Answering
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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/rsvp-ai/bertserini
공식 코드https://github.com/rucaibox/llm-knowledge-boundary
공식 코드Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation
https://github.com/rucaibox/rear
공식 코드REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering
https://github.com/ruochenzhao/verify-and-edit
공식 코드Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Framework
https://github.com/sail-sg/lm-random-memory-access
공식 코드Beyond Memorization: The Challenge of Random Memory Access in Language Models
https://github.com/salesforce/augtriever
공식 코드AugTriever: Unsupervised Dense Retrieval and Domain Adaptation by Scalable Data Augmentation
https://github.com/sayantan11995/context_based_quote_extraction
공식 코드https://github.com/sean0042/open_wikitable
공식 코드Open-WikiTable: Dataset for Open Domain Question Answering with Complex Reasoning over Table
https://github.com/sharonlevy/open_domain_covidqa
공식 코드Open-Domain Question-Answering for COVID-19 and Other Emergent Domains
https://github.com/shuohangwang/mprc
공식 코드R$^3$: Reinforced Reader-Ranker for Open-Domain Question Answering
https://github.com/shuoli90/traq
공식 코드TRAQ: Trustworthy Retrieval Augmented Question Answering via Conformal Prediction
https://github.com/soco-ai/SF-QA
공식 코드SF-QA: Simple and Fair Evaluation Library for Open-domain Question Answering
https://github.com/sony/talkhier
공식 코드Talk Structurally, Act Hierarchically: A Collaborative Framework for LLM Multi-Agent Systems
https://github.com/stanford-futuredata/ColBERT
공식 코드https://github.com/stanfordnlp/ColBERT-QA
공식 코드https://github.com/staoxiao/retromae
공식 코드RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encoder
https://github.com/studio-ousia/bpr
공식 코드Efficient Passage Retrieval with Hashing for Open-domain Question Answering
https://github.com/sunnweiwei/ambigprompt
공식 코드https://github.com/tLabruna/Adapt-LLM
공식 코드When to Retrieve: Teaching LLMs to Utilize Information Retrieval Effectively
https://github.com/teacherpeterpan/contraqa
공식 코드Attacking Open-domain Question Answering by Injecting Misinformation
https://github.com/text-machine-lab/xword_benchmark
공식 코드Down and Across: Introducing Crossword-Solving as a New NLP Benchmark
https://github.com/thinkwee/nover
공식 코드NOVER: Incentive Training for Language Models via Verifier-Free Reinforcement Learning
https://github.com/thunlp/adaptive-note
공식 코드Retriever-and-Memory: Towards Adaptive Note-Enhanced Retrieval-Augmented Generation
https://github.com/thunlp/fastfid
공식 코드FastFiD: Improve Inference Efficiency of Open Domain Question Answering via Sentence Selection
https://github.com/tonyby/myth-qa
공식 코드https://github.com/uclnlp/APE
공식 코드Training Adaptive Computation for Open-Domain Question Answering with Computational Constraints
https://github.com/ustc-starteam/genki
공식 코드https://github.com/uwnlp/denspi
공식 코드Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index
https://github.com/varal7/conformal-language-modeling
공식 코드https://github.com/velocitycavalry/crepe
공식 코드CREPE: Open-Domain Question Answering with False Presuppositions
https://github.com/verdurechen/sos-retrieval-loop
공식 코드https://github.com/voidism/ear
공식 코드Expand, Rerank, and Retrieve: Query Reranking for Open-Domain Question Answering
https://github.com/wangcunxiang/graph-as-tokens
공식 코드Exploiting Abstract Meaning Representation for Open-Domain Question Answering
https://github.com/wangcunxiang/rfid
공식 코드RFiD: Towards Rational Fusion-in-Decoder for Open-Domain Question Answering
https://github.com/wangskygit/passage-sieve
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.