https://github.com/prdwb/bert_hae
공식 코드BERT with History Answer Embedding for Conversational Question Answering
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/prdwb/okvqa-release
공식 코드Passage Retrieval for Outside-Knowledge Visual Question Answering
https://github.com/prdwb/orconvqa-release
공식 코드https://github.com/prdwb/ws-orconvqa
공식 코드Weakly-Supervised Open-Retrieval Conversational Question Answering
https://github.com/prijatelj/bayesian_eval_ground_truth-free
공식 코드A Bayesian Evaluation Framework for Subjectively Annotated Visual Recognition Tasks
https://github.com/primeqa/clapnq
공식 코드CLAPNQ: Cohesive Long-form Answers from Passages in Natural Questions for RAG systems
https://github.com/primeqa/primeqa
공식 코드https://github.com/princeton-nlp/DensePhrases
공식 코드https://github.com/princeton-nlp/c-sts
공식 코드https://github.com/princeton-nlp/cqa-evaluation
공식 코드Ditch the Gold Standard: Re-evaluating Conversational Question Answering
https://github.com/princeton-nlp/entityquestions
공식 코드https://github.com/princeton-nlp/evalconvqa
공식 코드Ditch the Gold Standard: Re-evaluating Conversational Question Answering
https://github.com/princeton-nlp/llm-shearing
공식 코드Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning
https://github.com/princeton-nlp/lm-science-tutor
공식 코드https://github.com/princeton-nlp/made
공식 코드https://github.com/princeton-nlp/mquake
공식 코드MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions
https://github.com/princeton-nlp/tree-of-thought-llm
공식 코드Tree of Thoughts: Deliberate Problem Solving with Large Language Models
https://github.com/princeton-pli/vlm_s2h
공식 코드Generalizing from SIMPLE to HARD Visual Reasoning: Can We Mitigate Modality Imbalance in VLMs?
https://github.com/princeton-vl/uniqa-3d
공식 코드https://github.com/princetonvisualai/pointingqa
공식 코드Point and Ask: Incorporating Pointing into Visual Question Answering
https://github.com/prithv1/cvpr2017_counting
공식 코드https://github.com/priya22/semantic-textual-relatedness
공식 코드What Makes Sentences Semantically Related: A Textual Relatedness Dataset and Empirical Study
https://github.com/priyankamandikal/vedantany-10m
공식 코드Ancient Wisdom, Modern Tools: Exploring Retrieval-Augmented LLMs for Ancient Indian Philosophy
https://github.com/probe2/multi-hop
공식 코드MINTQA: A Multi-Hop Question Answering Benchmark for Evaluating LLMs on New and Tail Knowledge
https://github.com/projectdossier/cruise-screening
공식 코드https://github.com/promptslab/rosettaeval
공식 코드https://github.com/psclklnk/spdl
공식 코드https://github.com/psudslab/FEDMEKI
공식 코드FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injection
https://github.com/psunlpgroup/ConEntail
공식 코드https://github.com/psunlpgroup/multihiertt
공식 코드MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data
https://github.com/psunlpgroup/veras
공식 코드https://github.com/pudumagico/kr2024
공식 코드Declarative Knowledge Distillation from Large Language Models for Visual Question Answering Datasets
https://github.com/pujari-rajkumar/naacl2019
공식 코드Using Natural Language Relations between Answer Choices for Machine Comprehension
https://github.com/pyf98/NeMo_VoiceTextBlender
공식 코드https://github.com/pykeen/pykeen
공식 코드https://github.com/pytorch/fairseq
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.