https://github.com/knowlab/medexqa
공식 코드MedExQA: Medical Question Answering Benchmark with Multiple Explanations
Papers with Code 아카이브
Question Answering
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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/knowledgetechnologyuhh/grid-3d
공식 코드https://github.com/koalazf99/tacube
공식 코드TaCube: Pre-computing Data Cubes for Answering Numerical-Reasoning Questions over Tabular Data
https://github.com/kobayashikanna01/chain-of-discussion
공식 코드Chain-of-Discussion: A Multi-Model Framework for Complex Evidence-Based Question Answering
https://github.com/kobayashikanna01/plms_are_lifelong_learners
공식 코드Can BERT Refrain from Forgetting on Sequential Tasks? A Probing Study
https://github.com/koen-47/columbus
공식 코드COLUMBUS: Evaluating COgnitive Lateral Understanding through Multiple-choice reBUSes
https://github.com/kojima-takeshi188/zero_shot_cot
공식 코드https://github.com/kolk/low-resource-tableqa-indic-languages
공식 코드https://github.com/kolk/multitabqa
공식 코드MultiTabQA: Generating Tabular Answers for Multi-Table Question Answering
https://github.com/kolk/pea-qa
공식 코드Parameter-Efficient Abstractive Question Answering over Tables or Text
https://github.com/koustuvsinha/clutrr
공식 코드Compositional Language Understanding with Text-based Relational Reasoning
https://github.com/kpeng9510/ravar
공식 코드https://github.com/kracr/rcone-qa-mmkg
공식 코드RConE: Rough Cone Embedding for Multi-Hop Logical Query Answering on Multi-Modal Knowledge Graphs
https://github.com/krafton-ai/LaPael
공식 코드Latent Paraphrasing: Perturbation on Layers Improves Knowledge Injection in Language Models
https://github.com/kramerlab/4StepFocus
공식 코드https://github.com/kramerlab/focusedretriever
공식 코드Focus, Merge, Rank: Improved Question Answering Based on Semi-structured Knowledge Bases
https://github.com/kristiyanvachev/leaf-question-generation
공식 코드https://github.com/kshitij98/cvil
공식 코드cViL: Cross-Lingual Training of Vision-Language Models using Knowledge Distillation
https://github.com/ksreenivasan/OOD_Federated_Learning
공식 코드Attack of the Tails: Yes, You Really Can Backdoor Federated Learning
https://github.com/kuanchihhuang/reason3d
공식 코드Reason3D: Searching and Reasoning 3D Segmentation via Large Language Model
https://github.com/kugwzk/dide
공식 코드Distilled Dual-Encoder Model for Vision-Language Understanding
https://github.com/kugwzk/distilled-dualencoder
공식 코드Distilled Dual-Encoder Model for Vision-Language Understanding
https://github.com/kumar-shridhar/screws
공식 코드https://github.com/kushalkafle/DVQA_dataset
공식 코드DVQA: Understanding Data Visualizations via Question Answering
https://github.com/kushalkafle/PREFIL
공식 코드Answering Questions about Data Visualizations using Efficient Bimodal Fusion
https://github.com/kvt0012/viclevr
공식 코드https://github.com/kyleseelman/vqa_accessibility
공식 코드https://github.com/kylrth/procedure-generation
공식 코드Pairing Analogy-Augmented Generation with Procedural Memory for Procedural Q&A
https://github.com/kyrochi/qlip
공식 코드QLIP: A Dynamic Quadtree Vision Prior Enhances MLLM Performance Without Retraining
https://github.com/kyunnilee/visual_puzzles
공식 코드Puzzled by Puzzles: When Vision-Language Models Can't Take a Hint
https://github.com/l3cube-pune/MarathiNLP
공식 코드MahaSQuAD: Bridging Linguistic Divides in Marathi Question-Answering
https://github.com/l3cube-pune/indic-nlp
공식 코드https://github.com/lab-rasool/awesome-medical-vlms-and-datasets
공식 코드Vision-Language Models for Medical Report Generation and Visual Question Answering: A Review
https://github.com/lackel/dka
공식 코드https://github.com/laituan245/StackExchangeQA
공식 코드https://github.com/lalithjets/surgical_vqa
공식 코드Surgical-VQA: Visual Question Answering in Surgical Scenes using Transformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.