https://github.com/chenmnz/prefixquant
공식 코드PrefixQuant: Eliminating Outliers by Prefixed Tokens for Large Language Models Quantization
Papers with Code 아카이브
Common Sense Reasoning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 17
- 평가표
- 24
- 보존된 평가 기록
- 397
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/chenyuheidizhang/vl-commonsense
공식 코드Visual Commonsense in Pretrained Unimodal and Multimodal Models
https://github.com/chongjie-si/homi
공식 코드Multi-label Classification with High-rank and High-order Label Correlations
https://github.com/cog-model/ambik-dataset
공식 코드https://github.com/cognitiveailab/bytesized32
공식 코드https://github.com/cognitiveailab/neurosymbolic
공식 코드https://github.com/cognitiveailab/textworldexpress
공식 코드TextWorldExpress: Simulating Text Games at One Million Steps Per Second
https://github.com/colinzhaoust/fact-and-reflection
공식 코드Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models
https://github.com/coobiw/trivqa
공식 코드https://github.com/cooelf/compassmtl
공식 코드Task Compass: Scaling Multi-task Pre-training with Task Prefix
https://github.com/csebuetnlp/illusionvqa
공식 코드IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models
https://github.com/cylnlp/DialogSum
공식 코드DialogSum: A Real-Life Scenario Dialogue Summarization Dataset
https://github.com/danshi777/corecode
공식 코드https://github.com/dawenl/expo-mf
공식 코드https://github.com/declare-lab/CICERO
공식 코드Multiview Contextual Commonsense Inference: A New Dataset and Task
https://github.com/devichand579/HPT
공식 코드https://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/divyakoyy/codenames
공식 코드https://github.com/dw-dengwei/treesearchgen
공식 코드Global-Local Tree Search in VLMs for 3D Indoor Scene Generation
https://github.com/ease-crc/vr-neem-converter
공식 코드Knowledge-Driven Robot Program Synthesis from Human VR Demonstrations
https://github.com/ease-crc/vr-program-synthesis
공식 코드Knowledge-Driven Robot Program Synthesis from Human VR Demonstrations
https://github.com/eleutherai/gpt-neox
공식 코드Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling
https://github.com/eleutherai/pythia
공식 코드Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling
https://github.com/enscma2/knowledje
공식 코드https://github.com/epochx/elmo4irony
공식 코드Deep contextualized word representations for detecting sarcasm and irony
https://github.com/eric-ai-lab/llm_coordination
공식 코드https://github.com/estherrolf/implicit-posterior
공식 코드https://github.com/eternityyw/gemini-commonsense-evaluation
공식 코드Gemini in Reasoning: Unveiling Commonsense in Multimodal Large Language Models
https://github.com/ezelikman/STaR
공식 코드https://github.com/facebookresearch/distributed-faiss
공식 코드The Web Is Your Oyster -- Knowledge-Intensive NLP against a Very Large Web Corpus
https://github.com/facebookresearch/layerskip
공식 코드LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding
https://github.com/facebookresearch/llama
공식 코드https://github.com/facebookresearch/phosa
공식 코드Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the Wild
https://github.com/facebookresearch/sphere
공식 코드The Web Is Your Oyster -- Knowledge-Intensive NLP against a Very Large Web Corpus
https://github.com/fanqiwan/fusellm
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.