https://github.com/lihaoxin2020/summarization-based-document-ids-for-generative-retrieval
공식 코드Summarization-Based Document IDs for Generative Retrieval with Language Models
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/likicode/QA-generalize
공식 코드Challenges in Generalization in Open Domain Question Answering
https://github.com/mayer123/udt-qa
공식 코드Open Domain Question Answering with A Unified Knowledge Interface
https://github.com/microsoft/ProphetNet
공식 코드CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing
https://github.com/microsoft/ar2
공식 코드https://github.com/microsoft/simxns
공식 코드https://github.com/morningmoni/GAR
공식 코드Generation-Augmented Retrieval for Open-domain Question Answering
https://github.com/namespace-pt/tsgen
공식 코드https://github.com/noviscl/answerequiv
공식 코드What's in a Name? Answer Equivalence For Open-Domain Question Answering
https://github.com/paddlepaddle/rocketqa
공식 코드https://github.com/pluslabnlp/zero_shot_cqa
공식 코드Zero-shot Commonsense Question Answering with Cloze Translation and Consistency Optimization
https://github.com/primeqa/clapnq
공식 코드CLAPNQ: Cohesive Long-form Answers from Passages in Natural Questions for RAG systems
https://github.com/princeton-nlp/corpus-poisoning
공식 코드Poisoning Retrieval Corpora by Injecting Adversarial Passages
https://github.com/raivnlab/adanns
공식 코드https://github.com/reutapel/meeqa
공식 코드https://github.com/sarthak268/c3vqg-official
공식 코드C3VQG: Category Consistent Cyclic Visual Question Generation
https://github.com/shuoli90/traq
공식 코드TRAQ: Trustworthy Retrieval Augmented Question Answering via Conformal Prediction
https://github.com/siagholami/zero-shot-open-book-qa
공식 코드https://github.com/slanglab/IndiaPoliceEvents
공식 코드https://github.com/stanford-futuredata/ColBERT
공식 코드https://github.com/stanford-oval/genie-toolkit
공식 코드AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Data
https://github.com/stanford-oval/genienlp
공식 코드AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Data
https://github.com/stanford-oval/schema2qa
공식 코드AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Data
https://github.com/stanfordnlp/ColBERT-QA
공식 코드https://github.com/studio-ousia/bpr
공식 코드Efficient Passage Retrieval with Hashing for Open-domain Question Answering
https://github.com/tangzhy/dptdr
공식 코드https://github.com/tensorflow/mesh
공식 코드ST-MoE: Designing Stable and Transferable Sparse Expert Models
https://github.com/terrierteam/pyterrier_rag
공식 코드Constructing and Evaluating Declarative RAG Pipelines in PyTerrier
https://github.com/thunlp/fastfid
공식 코드FastFiD: Improve Inference Efficiency of Open Domain Question Answering via Sentence Selection
https://github.com/utcsnlp/lfqa_discourse
공식 코드How Do We Answer Complex Questions: Discourse Structure of Long-form Answers
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/wenhuchen/theoremqa
공식 코드https://github.com/whitzard-ai/jade-db
공식 코드JADE: A Linguistics-based Safety Evaluation Platform for Large Language Models
https://github.com/wyripple/s1_bench
공식 코드S1-Bench: A Simple Benchmark for Evaluating System 1 Thinking Capability of Large Reasoning Models
https://github.com/xinyadu/eeqa
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.