https://github.com/yangrui525/kg-rank
공식 코드KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques
Papers with Code 아카이브
Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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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/yangyiben/G-DAUG-c-Generative-Data-Augmentation-for-Commonsense-Reasoning
공식 코드https://github.com/yangyue5114/DME
공식 코드Dynamic Multimodal Evaluation with Flexible Complexity by Vision-Language Bootstrapping
https://github.com/yangzhch6/alignedcot
공식 코드AlignedCoT: Prompting Large Language Models via Native-Speaking Demonstrations
https://github.com/yangzhch6/intermwp
공식 코드LogicSolver: Towards Interpretable Math Word Problem Solving with Logical Prompt-enhanced Learning
https://github.com/yanlingpan/chatgpt_sentiment
공식 코드Public Attitudes Toward ChatGPT on Twitter: Sentiments, Topics, and Occupations
https://github.com/yanmenxue/rah-kbqa
공식 코드Relation-Aware Question Answering for Heterogeneous Knowledge Graphs
https://github.com/yanx27/clevr3d
공식 코드Comprehensive Visual Question Answering on Point Clouds through Compositional Scene Manipulation
https://github.com/yanxinzju/CSS-VQA
공식 코드Counterfactual Samples Synthesizing for Robust Visual Question Answering
https://github.com/yaoching0/gac
공식 코드https://github.com/yaooxu/gog
공식 코드Generate-on-Graph: Treat LLM as both Agent and KG in Incomplete Knowledge Graph Question Answering
https://github.com/yashkant/concat-vqa
공식 코드https://github.com/yasumasaonoe/entity_knowledge_propagation
공식 코드Can LMs Learn New Entities from Descriptions? Challenges in Propagating Injected Knowledge
https://github.com/ybakman/llm_uncertainity
공식 코드MARS: Meaning-Aware Response Scoring for Uncertainty Estimation in Generative LLMs
https://github.com/yeeeqichen/fits
공식 코드FiTs: Fine-grained Two-stage Training for Knowledge-aware Question Answering
https://github.com/yehli/xmodaler
공식 코드X-modaler: A Versatile and High-performance Codebase for Cross-modal Analytics
https://github.com/yeliu0930/Knowledge-guided-Open-Attribute-Value-Extraction-with-Reinforcement-Learning
공식 코드Knowledge-guided Open Attribute Value Extraction with Reinforcement Learning
https://github.com/yeliu918/QREFINE-PPO
공식 코드Generative Question Refinement with Deep Reinforcement Learning in Retrieval-based QA System
https://github.com/yeliu918/dhr
공식 코드Dense Hierarchical Retrieval for Open-Domain Question Answering
https://github.com/yeliudev/VideoMind
공식 코드https://github.com/yellow-binary-tree/STAIR
공식 코드STAIR: Spatial-Temporal Reasoning with Auditable Intermediate Results for Video Question Answering
https://github.com/yellow-binary-tree/mmduet
공식 코드https://github.com/yeonseokjeong/judge-specialist
공식 코드https://github.com/yeonsw/BLANC
공식 코드https://github.com/yetianjhu/alphallm
공식 코드Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing
https://github.com/yfyuan01/mcpqa
공식 코드Unlocking Markets: A Multilingual Benchmark to Cross-Market Question Answering
https://github.com/yfyuan01/ravenea
공식 코드RAVENEA: A Benchmark for Multimodal Retrieval-Augmented Visual Culture Understanding
https://github.com/yfzhang114/llava-align
공식 코드https://github.com/yh-hust/pdf-wukong
공식 코드PDF-WuKong: A Large Multimodal Model for Efficient Long PDF Reading with End-to-End Sparse Sampling
https://github.com/yhleeee/cqa_entitylinking
공식 코드Community Question Answering Entity Linking via Leveraging Auxiliary Data
https://github.com/yhoshi3/ralle
공식 코드RaLLe: A Framework for Developing and Evaluating Retrieval-Augmented Large Language Models
https://github.com/yhshu/distribution-shifts-for-kbqa
공식 코드Data Distribution Bottlenecks in Grounding Language Models to Knowledge Bases
https://github.com/yhzhu99/medagentboard
공식 코드https://github.com/yichen14/fastadasp
공식 코드FastAdaSP: Multitask-Adapted Efficient Inference for Large Speech Language Model
https://github.com/yicheng-w/CommonSenseMultiHopQA
공식 코드Commonsense for Generative Multi-Hop Question Answering Tasks
https://github.com/yifding/in_context_el
공식 코드EntGPT: Linking Generative Large Language Models with Knowledge Bases
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.