https://github.com/tyler-hayes/REMIND
공식 코드REMIND Your Neural Network to Prevent Catastrophic Forgetting
Papers with Code 아카이브
Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/ucfnlp/summ_qa_rewards
공식 코드Guiding Extractive Summarization with Question-Answering Rewards
https://github.com/uci-soe/fairytaleqadata
공식 코드https://github.com/uclmr/jack
공식 코드https://github.com/uclnlp/APE
공식 코드Training Adaptive Computation for Open-Domain Question Answering with Computational Constraints
https://github.com/uclnlp/ctp
공식 코드Differentiable Reasoning on Large Knowledge Bases and Natural Language
https://github.com/uclnlp/emat
공식 코드An Efficient Memory-Augmented Transformer for Knowledge-Intensive NLP Tasks
https://github.com/uclnlp/gntp
공식 코드Differentiable Reasoning on Large Knowledge Bases and Natural Language
https://github.com/uclnlp/ntp
공식 코드Differentiable Reasoning on Large Knowledge Bases and Natural Language
https://github.com/ucsb-nlp-chang/augment_tableqa
공식 코드Augment before You Try: Knowledge-Enhanced Table Question Answering via Table Expansion
https://github.com/uds-lsv/premise
공식 코드Label-Descriptive Patterns and Their Application to Characterizing Classification Errors
https://github.com/uds-lsv/unanswerable-question-generation
공식 코드A Lightweight Method to Generate Unanswerable Questions in English
https://github.com/uiucnlp/mumuqa
공식 코드https://github.com/ukp-square/square-core
공식 코드UKP-SQUARE: An Online Platform for Question Answering Research
https://github.com/ukplab/arxiv2024-lfqa-hallucination
공식 코드Localizing and Mitigating Errors in Long-form Question Answering
https://github.com/ukplab/arxiv2025-misleading-visualizations
공식 코드Protecting multimodal large language models against misleading visualizations
https://github.com/ukplab/care
공식 코드https://github.com/ukplab/catfood
공식 코드CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and Calibration
https://github.com/ukplab/eacl2023-xlingvqa
공식 코드https://github.com/ukplab/emnlp2020-multicqa
공식 코드MultiCQA: Zero-Shot Transfer of Self-Supervised Text Matching Models on a Massive Scale
https://github.com/ukplab/incorporating-relevance
공식 코드https://github.com/ukplab/m2qa
공식 코드https://github.com/ukplab/metaqa
공식 코드MetaQA: Combining Expert Agents for Multi-Skill Question Answering
https://github.com/ukplab/peerqa
공식 코드PeerQA: A Scientific Question Answering Dataset from Peer Reviews
https://github.com/ulab-uiuc/time-r1
공식 코드https://github.com/uma-pi1/OPIEC
공식 코드https://github.com/umanlp/zusammenqa
공식 코드https://github.com/umass-ml4ed/reticl
공식 코드RetICL: Sequential Retrieval of In-Context Examples with Reinforcement Learning
https://github.com/umass-ml4ed/syllabusqa
공식 코드https://github.com/umd-huang-lab/sima
공식 코드Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement
https://github.com/umich-vl/think_visually
공식 코드https://github.com/uni-medical/GMAI-MMBench
공식 코드GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AI
https://github.com/uni-medical/gmai-vl
공식 코드https://github.com/uni-medical/gmai-vl-r1
공식 코드GMAI-VL-R1: Harnessing Reinforcement Learning for Multimodal Medical Reasoning
https://github.com/unicamp-dl/corpus2question
공식 코드Can questions summarize a corpus? Using question generation for characterizing COVID-19 research
https://github.com/unicomai/unicombenchmark
공식 코드CHiSafetyBench: A Chinese Hierarchical Safety Benchmark for Large Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.