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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/muhaochen/bilingual_dictionaries
공식 코드https://github.com/mukhal/lepus
공식 코드Few-shot Reranking for Multi-hop QA via Language Model Prompting
https://github.com/mukhal/promptrank
공식 코드Few-shot Reranking for Multi-hop QA via Language Model Prompting
https://github.com/multi-iot/multiiot
공식 코드IoT-LM: Large Multisensory Language Models for the Internet of Things
https://github.com/multimodal-interpretability/nnn
공식 코드Nearest Neighbor Normalization Improves Multimodal Retrieval
https://github.com/munzerdw/gen3dqa
공식 코드Generating Context-Aware Natural Answers for Questions in 3D Scenes
https://github.com/mxschmdt/mrqa-gen-al
공식 코드Combining Data Generation and Active Learning for Low-Resource Question Answering
https://github.com/mxschmdt/mrqa-prompting-gen
공식 코드Prompting-based Synthetic Data Generation for Few-Shot Question Answering
https://github.com/my625/promptqg
공식 코드https://github.com/my89/co-squac
공식 코드https://github.com/n4feng/ResponseQualityAssessment
공식 코드Response Quality Assessment for Retrieval-Augmented Generation via Conditional Conformal Factuality
https://github.com/nadavlab/MedConceptsQA
공식 코드https://github.com/nadiasaeed/medifact-m3g-mediqa-2024
공식 코드MediFact at MEDIQA-M3G 2024: Medical Question Answering in Dermatology with Multimodal Learning
https://github.com/nadiasaeed/medifact-mediqa-corr-2024
공식 코드https://github.com/nafissadeq/informask
공식 코드InforMask: Unsupervised Informative Masking for Language Model Pretraining
https://github.com/najoungkim/qaqa
공식 코드https://github.com/nakulrampal/retchemqa
공식 코드Single and Multi-Hop Question-Answering Datasets for Reticular Chemistry with GPT-4-Turbo
https://github.com/nan-huo/micro-act
공식 코드Micro-Act: Mitigate Knowledge Conflict in Question Answering via Actionable Self-Reasoning
https://github.com/nanami18/snowballed_hallucination
공식 코드https://github.com/narabzad/llm-relevance-judgement-comparison
공식 코드https://github.com/nardien/kala
공식 코드https://github.com/naserahmadi/tdmatch
공식 코드https://github.com/nathanhu0/CaMeLS
공식 코드https://github.com/national-center-for-ai-saudi-arabia/lm-evaluation-harness
공식 코드When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards
https://github.com/naver-ai/cream
공식 코드https://github.com/naver-ai/tablevqabench
공식 코드TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains
https://github.com/naver/biobert-pretrained
공식 코드BioBERT: a pre-trained biomedical language representation model for biomedical text mining
https://github.com/naver/posescript
공식 코드https://github.com/navidmdn/logic_based_qa
공식 코드https://github.com/nbalepur/QG-vs-QA
공식 코드Reverse Question Answering: Can an LLM Write a Question so Hard (or Bad) that it Can't Answer?
https://github.com/nbalepur/mcqa-artifacts
공식 코드Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?
https://github.com/nbalepur/text-fact-transfer
공식 코드https://github.com/ncbi-nlp/medcalc-bench
공식 코드MedCalc-Bench: Evaluating Large Language Models for Medical Calculations
https://github.com/ncbi/aioner
공식 코드AIONER: All-in-one scheme-based biomedical named entity recognition using deep learning
https://github.com/ncsoft/cap2qa
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.