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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/liujch1998/rainier
공식 코드Rainier: Reinforced Knowledge Introspector for Commonsense Question Answering
https://github.com/liuxiangyuan/mba
공식 코드Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment
https://github.com/liweim/TextLayoutLLM
공식 코드https://github.com/lixinyuan1015/nlp-for-education
공식 코드Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends
https://github.com/lixinze777/lc_vs_rag
공식 코드https://github.com/liyucheng09/selective_context
공식 코드https://github.com/lizekai-richard/isqa
공식 코드ISQA: Informative Factuality Feedback for Scientific Summarization
https://github.com/lizhaoliu-lec/ccse
공식 코드Instance Segmentation for Chinese Character Stroke Extraction, Datasets and Benchmarks
https://github.com/lizhecheng02/drs
공식 코드https://github.com/lizw14/calico
공식 코드Calibrating Concepts and Operations: Towards Symbolic Reasoning on Real Images
https://github.com/lizw14/super-clevr
공식 코드Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoning
https://github.com/ljungang/rtv-bench
공식 코드https://github.com/lkwate/neural-question-generation
공식 코드Semantic-Based Self-Critical Training For Question Generation
https://github.com/llamatouch/vlm-reasoning-traces
공식 코드Does Chain-of-Thought Reasoning Help Mobile GUI Agent? An Empirical Study
https://github.com/llm-vlm-gsl/discuss-rag
공식 코드Talk Before You Retrieve: Agent-Led Discussions for Better RAG in Medical QA
https://github.com/lmous/openai-gpt4-coding-assistant
공식 코드https://github.com/lnikolenko/EQA
공식 코드When in Doubt, Ask: Generating Answerable and Unanswerable Questions, Unsupervised
https://github.com/lockon-n/self-prompting
공식 코드Self-Prompting Large Language Models for Zero-Shot Open-Domain QA
https://github.com/longbai1006/cat-vil
공식 코드https://github.com/longbai1006/surgical-vqla
공식 코드https://github.com/longbai1006/surgical-vqlaplus
공식 코드https://github.com/longmalongma/desa2va
공식 코드Decoupled Seg Tokens Make Stronger Reasoning Video Segmenter and Grounder
https://github.com/longquanjiang/ontoscprompt
공식 코드Ontology-Guided, Hybrid Prompt Learning for Generalization in Knowledge Graph Question Answering
https://github.com/longvideobench/longvideobench
공식 코드LongVideoBench: A Benchmark for Long-context Interleaved Video-Language Understanding
https://github.com/lorenzkuhn/semantic_uncertainty
공식 코드https://github.com/lorenzovaquero/busca
공식 코드Lost and Found: Overcoming Detector Failures in Online Multi-Object Tracking
https://github.com/lovodkin93/attribute-first-then-generate
공식 코드Attribute First, then Generate: Locally-attributable Grounded Text Generation
https://github.com/lqh52/promvil
공식 코드Progressive Multi-granular Alignments for Grounded Reasoning in Large Vision-Language Models
https://github.com/lqzxt/ngtr
공식 코드Enhancing Table Recognition with Vision LLMs: A Benchmark and Neighbor-Guided Toolchain Reasoner
https://github.com/lranaldii/crossalpaca
공식 코드https://github.com/lrudl/sad
공식 코드Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs
https://github.com/ltgoslo/brent
공식 코드BRENT: Bidirectional Retrieval Enhanced Norwegian Transformer
https://github.com/ltgoslo/norquad
공식 코드https://github.com/ltl3a87/kb-binder
공식 코드Few-shot In-context Learning for Knowledge Base Question Answering
https://github.com/ltu-machine-learning/idocvqa
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.