Papers with Code 아카이브
Common Sense Reasoning
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Common sense reasoning tasks are intended to require the model to go beyond pattern recognition. Instead, the model should use "common sense" or world knowledge to make inferences.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/openai/gpt-3
공식 코드https://github.com/opengvlab/gitm
공식 코드https://github.com/opengvlab/omniquant
공식 코드OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models
https://github.com/opennlg/openba-v2
공식 코드OpenBA-V2: Reaching 77.3% High Compression Ratio with Fast Multi-Stage Pruning
https://github.com/openrobotlab/pointllm
공식 코드PointLLM: Empowering Large Language Models to Understand Point Clouds
https://github.com/optimalscale/lmflow
공식 코드LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning
https://github.com/p1nksnow/MSIC
공식 코드Collaborative Synthesis of Patient Records through Multi-Visit Health State Inference
https://github.com/paperswithcode/galai
공식 코드https://github.com/pchizhov/hellaswag-evaluation
공식 코드What the HellaSwag? On the Validity of Common-Sense Reasoning Benchmarks
https://github.com/pjlab-adg/gpt4v-ad-exploration
공식 코드On the Road with GPT-4V(ision): Early Explorations of Visual-Language Model on Autonomous Driving
https://github.com/pku-rl/adarefiner
공식 코드AdaRefiner: Refining Decisions of Language Models with Adaptive Feedback
https://github.com/pku-yuangroup/wise
공식 코드WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation
https://github.com/ptrichel/How-Reasonable-are-Common-Sense-Reasoning-Tasks
공식 코드https://github.com/py-why/pywhy-llm
공식 코드Causal Reasoning and Large Language Models: Opening a New Frontier for Causality
https://github.com/pytorch/fairseq
공식 코드https://github.com/qwenlm/qwen2.5
공식 코드https://github.com/qxc101/probeval_cfc
공식 코드Every Answer Matters: Evaluating Commonsense with Probabilistic Measures
https://github.com/radarFudan/mamba
공식 코드Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/real-stanford/reflect
공식 코드REFLECT: Summarizing Robot Experiences for Failure Explanation and Correction
https://github.com/roihn/sabm
공식 코드Smart Agent-Based Modeling: On the Use of Large Language Models in Computer Simulations
https://github.com/s-nlp/dont-fight-hallucinations
공식 코드Don't Fight Hallucinations, Use Them: Estimating Image Realism using NLI over Atomic Facts
https://github.com/sail-sg/regmix
공식 코드RegMix: Data Mixture as Regression for Language Model Pre-training
https://github.com/samuelyu2002/pacs
공식 코드PACS: A Dataset for Physical Audiovisual CommonSense Reasoning
https://github.com/scofield7419/thor-isa
공식 코드Reasoning Implicit Sentiment with Chain-of-Thought Prompting
https://github.com/seonghyeonye/flipped-learning
공식 코드Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners
https://github.com/shrahimim/boosting-theory-of-mind-in-llms-with-prompting
공식 코드Boosting Theory-of-Mind Performance in Large Language Models via Prompting
https://github.com/siddharthl93/design_kgex
공식 코드Retrieval Augmented Generation using Engineering Design Knowledge
https://github.com/siddharthl93/engineering-design-knowledge
공식 코드Retrieval Augmented Generation using Engineering Design Knowledge
https://github.com/sjoerdvansteenkiste/Relational-NEM
공식 코드Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions
https://github.com/sled-group/heuristic-analytic-reasoning
공식 코드https://github.com/snumprlab/flare
공식 코드https://github.com/soumyardash/SemEval2020-Task4
공식 코드https://github.com/state-spaces/mamba
공식 코드Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/strong-ai-lab/abductionrules
공식 코드AbductionRules: Training Transformers to Explain Unexpected Inputs
https://github.com/strong-ai-lab/logical-and-abstract-reasoning
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.