https://github.com/mcxiaoxiao/MMSQL
공식 코드Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types
Papers with Code 아카이브
Logical Reasoning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mengfeidu/delan
공식 코드DELAN: Dual-Level Alignment for Vision-and-Language Navigation by Cross-Modal Contrastive Learning
https://github.com/mfe7/cadrl_ros
공식 코드Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning
https://github.com/mgkumar138/TDHL_6PA
공식 코드A nonlinear hidden layer enables actor-critic agents to learn multiple paired association navigation
https://github.com/mh0797/interplan
공식 코드Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?
https://github.com/michaelchen-lab/justlogic
공식 코드JustLogic: A Comprehensive Benchmark for Evaluating Deductive Reasoning in Large Language Models
https://github.com/michigan-traffic-lab/DECODE
공식 코드DECODE: Domain-aware Continual Domain Expansion for Motion Prediction
https://github.com/microsoft/ContextualSP
공식 코드https://github.com/microsoft/UFO
공식 코드https://github.com/microsoft/data-formulator
공식 코드https://github.com/microsoft/pike-rag
공식 코드PIKE-RAG: sPecIalized KnowledgE and Rationale Augmented Generation
https://github.com/microsoft/promptbench
공식 코드DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks
https://github.com/migalkin/rwl
공식 코드https://github.com/mihir3009/gridpuzzle
공식 코드Step-by-Step Reasoning to Solve Grid Puzzles: Where do LLMs Falter?
https://github.com/mihir3009/logicbench
공식 코드LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models
https://github.com/mihir3009/multi-logieval
공식 코드Multi-LogiEval: Towards Evaluating Multi-Step Logical Reasoning Ability of Large Language Models
https://github.com/mikewangwzhl/solo-performance-prompting
공식 코드https://github.com/mililab/logicocr
공식 코드LogicOCR: Do Your Large Multimodal Models Excel at Logical Reasoning on Text-Rich Images?
https://github.com/mililab/timo
공식 코드TiMo: Spatiotemporal Foundation Model for Satellite Image Time Series
https://github.com/mingkai-zheng/genius
공식 코드https://github.com/minhducbui/multi3hate
공식 코드https://github.com/minimax-ai/synlogic
공식 코드SynLogic: Synthesizing Verifiable Reasoning Data at Scale for Learning Logical Reasoning and Beyond
https://github.com/mininglamp-mllm/hmllm
공식 코드https://github.com/mirage-project/mirage
공식 코드https://github.com/miralab-ai/autoreason
공식 코드https://github.com/ml-research/bowll
공식 코드https://github.com/ml-research/vlol-dataset-gen
공식 코드https://github.com/mlds-nus/deeplearningcustomthermodynamics
공식 코드https://github.com/mlgig/amee
공식 코드Robust Explainer Recommendation for Time Series Classification
https://github.com/mneuronico/griw-world-spatial-orientation-task
공식 코드From Text to Space: Mapping Abstract Spatial Models in LLMs during a Grid-World Navigation Task
https://github.com/mnotgod96/AppAgent
공식 코드https://github.com/mohamedaghzal/llms-as-path-planners
공식 코드https://github.com/mohamednagymostafa/dfr-fastmot
공식 코드https://github.com/mohamednagymostafa/kitti-mot.bench-evals
공식 코드https://github.com/molden/probkt
공식 코드Weakly Supervised Knowledge Transfer with Probabilistic Logical Reasoning for Object Detection
https://github.com/monalissaa/disendiff
공식 코드Attention Calibration for Disentangled Text-to-Image Personalization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.