https://github.com/gautierdag/cast
공식 코드CAST: Cross-modal Alignment Similarity Test for Vision Language Models
Papers with Code 아카이브
Question Answering
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- 평가표
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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/gck25/realmedqa
공식 코드RealMedQA: A pilot biomedical question answering dataset containing realistic clinical questions
https://github.com/gcollell/spatial-commonsense
공식 코드Acquiring Common Sense Spatial Knowledge through Implicit Spatial Templates
https://github.com/gdebarghya/Network-TwoSampleTesting
공식 코드https://github.com/geek-ai/irgan
공식 코드IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
https://github.com/gen-verse/scoreflow
공식 코드ScoreFlow: Mastering LLM Agent Workflows via Score-based Preference Optimization
https://github.com/gengdavid/slca
공식 코드SLCA: Slow Learner with Classifier Alignment for Continual Learning on a Pre-trained Model
https://github.com/genglinliu/unknownbench
공식 코드Examining LLMs' Uncertainty Expression Towards Questions Outside Parametric Knowledge
https://github.com/geobrain-ai/geogalactica
공식 코드GeoGalactica: A Scientific Large Language Model in Geoscience
https://github.com/georg3tom/RoadTextVQA
공식 코드https://github.com/georgestod/sparsistent_model_disco
공식 코드https://github.com/gersteinlab/medagents-benchmark
공식 코드MedAgentsBench: Benchmarking Thinking Models and Agent Frameworks for Complex Medical Reasoning
https://github.com/gewu-lab/mwafm
공식 코드https://github.com/gewu-lab/pstp-net
공식 코드Progressive Spatio-temporal Perception for Audio-Visual Question Answering
https://github.com/gewu-lab/tspm
공식 코드Boosting Audio Visual Question Answering via Key Semantic-Aware Cues
https://github.com/gglab-ku/open-vocab-dialogue-understanding
공식 코드A Zero-Shot Open-Vocabulary Pipeline for Dialogue Understanding
https://github.com/ghomashudson/muld
공식 코드https://github.com/ghoshs/coqex
공식 코드https://github.com/ghrua/seqpe
공식 코드https://github.com/gicheonkang/DAN-VisDial
공식 코드Dual Attention Networks for Visual Reference Resolution in Visual Dialog
https://github.com/giganticode/probes
공식 코드https://github.com/gipplab/MathQA
공식 코드Mining Mathematical Documents for Question Answering via Unsupervised Formula Labeling
https://github.com/gipplab/taxonomy-of-mathematical-plagiarism
공식 코드https://github.com/gissygonzalez/kleiogpt
공식 코드If the Sources Could Talk: Evaluating Large Language Models for Research Assistance in History
https://github.com/gjiaying/zs-ska
공식 코드Prompt-based Zero-shot Relation Extraction with Semantic Knowledge Augmentation
https://github.com/gkaradzhov/FactcheckingRANLP
공식 코드https://github.com/glamor-usc/climb
공식 코드https://github.com/globosco/A-learned-sorted-table-search-library
공식 코드Learned Sorted Table Search and Static Indexes in Small Model Space
https://github.com/gls0425/linvt
공식 코드LinVT: Empower Your Image-level Large Language Model to Understand Videos
https://github.com/gmftbyGMFTBY/HashRetrieval
공식 코드https://github.com/gmpoli/electramed
공식 코드ELECTRAMed: a new pre-trained language representation model for biomedical NLP
https://github.com/gokulkarthik/mucot
공식 코드MuCoT: Multilingual Contrastive Training for Question-Answering in Low-resource Languages
https://github.com/gomate-community/trustrag
공식 코드TrustRAG: An Information Assistant with Retrieval Augmented Generation
https://github.com/gonced8/rachael-scai
공식 코드Question rewriting? Assessing its importance for conversational question answering
https://github.com/google-deepmind/neptune
공식 코드Neptune: The Long Orbit to Benchmarking Long Video Understanding
https://github.com/google-deepmind/tanq
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.