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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/deep-spin/sparse_continuous_distributions
공식 코드https://github.com/deep-spin/treqa
공식 코드Do LLMs Understand Your Translations? Evaluating Paragraph-level MT with Question Answering
https://github.com/deepaknlp/medvidqacl
공식 코드A Dataset for Medical Instructional Video Classification and Question Answering
https://github.com/deepmind/rc-data
공식 코드https://github.com/deepmind/streamingqa
공식 코드StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models
https://github.com/deepmind/svo_probes
공식 코드https://github.com/deepmind/xquad
공식 코드On the Cross-lingual Transferability of Monolingual Representations
https://github.com/deepseek-ai/deepseek-math
공식 코드DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
https://github.com/deepseek-ai/deepseek-r1
공식 코드DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
https://github.com/deepseek-ai/deepseek-vl2
공식 코드DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding
https://github.com/demoleiwang/cgmwp
공식 코드Improving Compositional Generalization in Math Word Problem Solving
https://github.com/dengc2023/longdocurl
공식 코드https://github.com/dengyang17/MTQA
공식 코드https://github.com/dengyang17/OAAG
공식 코드Opinion-aware Answer Generation for Review-driven Question Answering in E-Commerce
https://github.com/dengyang17/pacific
공식 코드https://github.com/dengyang17/tois-page
공식 코드Towards Personalized Answer Generation in E-Commerce via Multi-Perspective Preference Modeling
https://github.com/dengyang17/wikihowQA
공식 코드Joint Learning of Answer Selection and Answer Summary Generation in Community Question Answering
https://github.com/deokhk/dotter
공식 코드Denoising Table-Text Retrieval for Open-Domain Question Answering
https://github.com/deqingfu/scene
공식 코드SCENE: Self-Labeled Counterfactuals for Extrapolating to Negative Examples
https://github.com/deriq-qian-dong/iii-retriever
공식 코드https://github.com/desy-ml/rl-vs-bo
공식 코드https://github.com/devichand579/HPT
공식 코드https://github.com/devnkong/FLAG
공식 코드Robust Optimization as Data Augmentation for Large-scale Graphs
https://github.com/dfki-nlp/fewie
공식 코드A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition
https://github.com/dfki-nlp/interrolang
공식 코드InterroLang: Exploring NLP Models and Datasets through Dialogue-based Explanations
https://github.com/dfki-nlp/llmcheckup
공식 코드https://github.com/dhananjayashok/littlehumandata
공식 코드https://github.com/dhdhagar/byokg
공식 코드Bring Your Own KG: Self-Supervised Program Synthesis for Zero-Shot KGQA
https://github.com/dheeraj7596/conda
공식 코드Leveraging QA Datasets to Improve Generative Data Augmentation
https://github.com/dheeraj7596/qagen
공식 코드Leveraging QA Datasets to Improve Generative Data Augmentation
https://github.com/dice-group/MST5
공식 코드MST5 -- Multilingual Question Answering over Knowledge Graphs
https://github.com/dice-group/NLIWOD
공식 코드https://github.com/dice-group/NLIWOD/tree/master/qa.ml
공식 코드Using Multi-Label Classification for Improved Question Answering
https://github.com/dice-group/TeBaQA
공식 코드Knowledge Graph Question Answering using Graph-Pattern Isomorphism
https://github.com/diegodoimo/geometry_icl_finetuning
공식 코드The representation landscape of few-shot learning and fine-tuning in large language models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.