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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/mdda/worldtree_corpus
공식 코드https://github.com/mdelhoneux/avc_analyser
공식 코드What Should/Do/Can LSTMs Learn When Parsing Auxiliary Verb Constructions?
https://github.com/mecuping/dlp-lora
공식 코드https://github.com/meetdavidwan/generationprograms
공식 코드GenerationPrograms: Fine-grained Attribution with Executable Programs
https://github.com/meetyou-ai-lab/can-mc-evaluate-llms
공식 코드Can multiple-choice questions really be useful in detecting the abilities of LLMs?
https://github.com/mega002/DocQN
공식 코드Learning to Search in Long Documents Using Document Structure
https://github.com/megagonlabs/CMDBench
공식 코드CMDBench: A Benchmark for Coarse-to-fine Multimodal Data Discovery in Compound AI Systems
https://github.com/megagonlabs/SubjQA
공식 코드https://github.com/megagonlabs/cypherbench
공식 코드CypherBench: Towards Precise Retrieval over Full-scale Modern Knowledge Graphs in the LLM Era
https://github.com/megagonlabs/witqa
공식 코드https://github.com/mehrdadghassabi/gaokerena
공식 코드Leveraging Online Data to Enhance Medical Knowledge in a Small Persian Language Model
https://github.com/meiru-cam/AttentionInstruction
공식 코드Attention Instruction: Amplifying Attention in the Middle via Prompting
https://github.com/merlresearch/SMART
공식 코드https://github.com/meryemmhamdi1/meta_cross_nlu_qa
공식 코드https://github.com/meta-inf/suq-nlg
공식 코드On Subjective Uncertainty Quantification and Calibration in Natural Language Generation
https://github.com/meta-math/MetaMath
공식 코드MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models
https://github.com/mhardalov/exams-qa
공식 코드https://github.com/mi92/reverse-image-rag
공식 코드Reverse Image Retrieval Cues Parametric Memory in Multimodal LLMs
https://github.com/mianzg/victim_counts
공식 코드https://github.com/michaelyya/wximpactbench
공식 코드https://github.com/michi-3000/eyeclip
공식 코드EyeCLIP: A visual-language foundation model for multi-modal ophthalmic image analysis
https://github.com/michigannlp/task-adaptive_tokenization
공식 코드Task-Adaptive Tokenization: Enhancing Long-Form Text Generation Efficacy in Mental Health and Beyond
https://github.com/michiyasunaga/LinkBERT
공식 코드https://github.com/michiyasunaga/dragon
공식 코드https://github.com/michiyasunaga/qagnn
공식 코드QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
https://github.com/michschli/averitec
공식 코드AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Web
https://github.com/michschli/graphmask
공식 코드Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking
https://github.com/mickeystroller/SetExpan
공식 코드SetExpan: Corpus-Based Set Expansion via Context Feature Selection and Rank Ensemble
https://github.com/microsoft/ContextualSP
공식 코드https://github.com/microsoft/DEKCOR-CommonsenseQA
공식 코드Fusing Context Into Knowledge Graph for Commonsense Question Answering
https://github.com/microsoft/DeBERTa
공식 코드https://github.com/microsoft/DeepSpeed
공식 코드Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam
https://github.com/microsoft/GenerativeImage2Text
공식 코드GIT: A Generative Image-to-text Transformer for Vision and Language
https://github.com/microsoft/KBLaM
공식 코드https://github.com/microsoft/KC/tree/main/papers/TIARA
공식 코드TIARA: Multi-grained Retrieval for Robust Question Answering over Large Knowledge Bases
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.