https://github.com/sohanpatnaik106/cabinet_qa
공식 코드CABINET: Content Relevance based Noise Reduction for Table Question Answering
Papers with Code 아카이브
Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
- 153
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Question answering can be segmented into domain-specific tasks like community question answering and knowledge-base question answering. Popular benchmark datasets for evaluation question answering systems include SQuAD, HotPotQA, bAbI, TriviaQA, WikiQA, and many others. Models for question answering are typically evaluated on metrics like EM and F1. Some recent top performing models are T5 and XLNet.
( Image credit: SQuAD )
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/somiltg/bert
공식 코드BERT Based Multilingual Machine Comprehension in English and Hindi
https://github.com/songtaoliu0823/lagnn
공식 코드https://github.com/sonjbin/tcqa
공식 코드Time-Aware Representation Learning for Time-Sensitive Question Answering
https://github.com/sonlam1102/vicoqa-cmc
공식 코드Conversational Machine Reading Comprehension for Vietnamese Healthcare Texts
https://github.com/sonqt/agent-unanswerable
공식 코드AGent: A Novel Pipeline for Automatically Creating Unanswerable Questions
https://github.com/sonqt/robust_qa
공식 코드Towards Robust Extractive Question Answering Models: Rethinking the Training Methodology
https://github.com/sonvx/word2vecVN
공식 코드https://github.com/sony/talkhier
공식 코드Talk Structurally, Act Hierarchically: A Collaborative Framework for LLM Multi-Agent Systems
https://github.com/souradipp76/mm-poe
공식 코드MM-PoE: Multiple Choice Reasoning via. Process of Elimination using Multi-Modal Models
https://github.com/sourjyadip/ervqa-data
공식 코드ERVQA: A Dataset to Benchmark the Readiness of Large Vision Language Models in Hospital Environments
https://github.com/soyoung97/RoToR
공식 코드RoToR: Towards More Reliable Responses for Order-Invariant Inputs
https://github.com/spackows/icaai-2024_rag-cd
공식 코드https://github.com/spandan-madan/in_distribution_adversarial_examples
공식 코드In-distribution adversarial attacks on object recognition models using gradient-free search
https://github.com/speechbrain/speechbrain/tree/develop/recipes/REAL-M/sisnr-estimation
공식 코드https://github.com/sperfu/eyedoc
공식 코드https://github.com/splend1d/t5lephone
공식 코드https://github.com/spongeorge/long-context-multihop
공식 코드https://github.com/springcty/rag-fragility-to-linguistic-variation
공식 코드https://github.com/srsawant34/efficient_instruction_learning
공식 코드https://github.com/ssm123ssm/medGPT
공식 코드https://github.com/stan-lei/kwaichat-naacl2025
공식 코드KwaiChat: A Large-Scale Video-Driven Multilingual Mixed-Type Dialogue Corpus
https://github.com/standardmodelbio/llama3-med
공식 코드Advancing High Resolution Vision-Language Models in Biomedicine
https://github.com/stanford-crfm/biomedlm
공식 코드BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text
https://github.com/stanford-crfm/halie
공식 코드https://github.com/stanford-crfm/helm
공식 코드https://github.com/stanford-futuredata/Baleen
공식 코드Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval
https://github.com/stanford-futuredata/ColBERT
공식 코드https://github.com/stanford-oval/SPL
공식 코드Localizing Open-Ontology QA Semantic Parsers in a Day Using Machine Translation
https://github.com/stanford-oval/genie-toolkit
공식 코드Schema2QA: High-Quality and Low-Cost Q&A Agents for the Structured Web
https://github.com/stanford-oval/genienlp
공식 코드Schema2QA: High-Quality and Low-Cost Q&A Agents for the Structured Web
https://github.com/stanford-oval/schema2qa
공식 코드Schema2QA: High-Quality and Low-Cost Q&A Agents for the Structured Web
https://github.com/stanford-oval/spinach
공식 코드SPINACH: SPARQL-Based Information Navigation for Challenging Real-World Questions
https://github.com/stanford-oval/wikidata-emnlp23
공식 코드https://github.com/stanfordnlp/ColBERT-QA
공식 코드https://github.com/stanfordnlp/dsp
공식 코드Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.