https://github.com/vicomtech/eusquad
공식 코드EuSQuAD: Automatically Translated and Aligned SQuAD2.0 for Basque
Papers with Code 아카이브
Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 19
- 평가표
- 153
- 보존된 평가 기록
- 1,781
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/victordibia/neuralqa
공식 코드https://github.com/viczong/extract_covid19_events_from_twitter
공식 코드COVID-19 event extraction from Twitter via extractive question answering with continuous prompts
https://github.com/vijilAI/chain_of_guidance
공식 코드Improving Consistency in Large Language Models through Chain of Guidance
https://github.com/vik0/LangevinDQN
공식 코드https://github.com/vikas95/AIR-retriever
공식 코드Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering
https://github.com/vikas95/Pref_Suff_Span_NN
공식 코드A Survey on Recent Advances in Named Entity Recognition from Deep Learning models
https://github.com/vikshree/QA_PersonSearchLanguageData
공식 코드https://github.com/vincent-zhq/hlv-1k
공식 코드HLV-1K: A Large-scale Hour-Long Video Benchmark for Time-Specific Long Video Understanding
https://github.com/vinusankars/reliability-of-ai-text-detectors
공식 코드https://github.com/vipulgupta1011/calm
공식 코드CALM : A Multi-task Benchmark for Comprehensive Assessment of Language Model Bias
https://github.com/vipulgupta1011/swapmix
공식 코드https://github.com/vis-nlp/chartgemma
공식 코드ChartGemma: Visual Instruction-tuning for Chart Reasoning in the Wild
https://github.com/vis-nlp/chartinstruct
공식 코드ChartInstruct: Instruction Tuning for Chart Comprehension and Reasoning
https://github.com/vis-nlp/chartqa
공식 코드ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning
https://github.com/vis-nlp/chartqapro
공식 코드ChartQAPro: A More Diverse and Challenging Benchmark for Chart Question Answering
https://github.com/vis-nlp/opencqa
공식 코드https://github.com/vis-nlp/unichart
공식 코드UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning
https://github.com/vishwajeet93/clqg
공식 코드https://github.com/vision-cair/chatcaptioner
공식 코드ChatGPT Asks, BLIP-2 Answers: Automatic Questioning Towards Enriched Visual Descriptions
https://github.com/vision-cair/dochaystacks
공식 코드Document Haystacks: Vision-Language Reasoning Over Piles of 1000+ Documents
https://github.com/vision-cair/minigpt-med
공식 코드MiniGPT-Med: Large Language Model as a General Interface for Radiology Diagnosis
https://github.com/vision-x-nyu/thinking-in-space
공식 코드Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces
https://github.com/vistec-ai/nitibench
공식 코드NitiBench: A Comprehensive Studies of LLM Frameworks Capabilities for Thai Legal Question Answering
https://github.com/visual-haystacks/vhs_benchmark
공식 코드Visual Haystacks: A Vision-Centric Needle-In-A-Haystack Benchmark
https://github.com/visual-text-QA/VTQA-Demo
공식 코드VTQA: Visual Text Question Answering via Entity Alignment and Cross-Media Reasoning
https://github.com/visualaikhu/missing-avqa
공식 코드https://github.com/vita-mllm/lucy
공식 코드LUCY: Linguistic Understanding and Control Yielding Early Stage of Her
https://github.com/vita-mllm/vita-audio
공식 코드VITA-Audio: Fast Interleaved Cross-Modal Token Generation for Efficient Large Speech-Language Model
https://github.com/vityavitalich/imad
공식 코드https://github.com/vivisimo/QuestionGeneration
공식 코드https://github.com/vl2g/KaLMA
공식 코드https://github.com/vladislavneon/RuBQ
공식 코드RuBQ: A Russian Dataset for Question Answering over Wikidata
https://github.com/vlgiitr/Are-VLMs-Really-Blind
공식 코드https://github.com/vlievin/medical-reasoning
공식 코드https://github.com/vlivashkin/pygkernels
공식 코드Dissecting graph measure performance for node clustering in LFR parameter space
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.