https://github.com/megvii-research/caspl
공식 코드Cascade Prompt Learning for Vision-Language Model Adaptation
Papers with Code 아카이브
General Knowledge
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 7
- 평가표
- 1
- 보존된 평가 기록
- 2
This task aims to evaluate the ability of a model to answer general-knowledge questions.
Source: BIG-bench
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/michiyasunaga/LinkBERT
공식 코드https://github.com/microsoft/ProphetNet
공식 코드CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing
https://github.com/microsoft/ar2
공식 코드https://github.com/microsoft/simxns
공식 코드https://github.com/ml-research/MoRT_NMI
공식 코드Large Pre-trained Language Models Contain Human-like Biases of What is Right and Wrong to Do
https://github.com/mlii0117/dcl
공식 코드Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report Generation
https://github.com/mlo-lab/bvcd_generative_models
공식 코드A Bias-Variance-Covariance Decomposition of Kernel Scores for Generative Models
https://github.com/mlvlab/SugaFormer
공식 코드Super-class guided Transformer for Zero-Shot Attribute Classification
https://github.com/mlvlab/prometar
공식 코드https://github.com/monikshah/caption-explanation-hmln
공식 코드Disentangling Fine-Tuning from Pre-Training in Visual Captioning with Hybrid Markov Logic
https://github.com/morningmoni/GAR
공식 코드Generation-Augmented Retrieval for Open-domain Question Answering
https://github.com/mtkresearch/mr-models
공식 코드https://github.com/muradtuk/385ApproximationSubMax
공식 코드Practical $0.385$-Approximation for Submodular Maximization Subject to a Cardinality Constraint
https://github.com/namespace-pt/tsgen
공식 코드https://github.com/nardien/kala
공식 코드https://github.com/nashchou/multigprompt
공식 코드MultiGPrompt for Multi-Task Pre-Training and Prompting on Graphs
https://github.com/night-chen/hydra
공식 코드HYDRA: Model Factorization Framework for Black-Box LLM Personalization
https://github.com/nkandpa2/long_tail_knowledge
공식 코드https://github.com/noviscl/answerequiv
공식 코드What's in a Name? Answer Equivalence For Open-Domain Question Answering
https://github.com/npupilab/curriculumloc
공식 코드CurriculumLoc: Enhancing Cross-Domain Geolocalization through Multi-Stage Refinement
https://github.com/nusnlp/amanda
공식 코드A Question-Focused Multi-Factor Attention Network for Question Answering
https://github.com/openbmb/rageval
공식 코드RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework
https://github.com/opendrivelab/drivelm
공식 코드https://github.com/ozyyshr/structchem
공식 코드https://github.com/paddlepaddle/rocketqa
공식 코드https://github.com/pbaghershahi/TGCN
공식 코드Efficient Relation-aware Neighborhood Aggregation in Graph Neural Networks via Tensor Decomposition
https://github.com/pkargupta/taxoadapt
공식 코드TaxoAdapt: Aligning LLM-Based Multidimensional Taxonomy Construction to Evolving Research Corpora
https://github.com/pku0xff/cc-riddle
공식 코드CC-Riddle: A Question Answering Dataset of Chinese Character Riddles
https://github.com/pluslabnlp/zero_shot_cqa
공식 코드Zero-shot Commonsense Question Answering with Cloze Translation and Consistency Optimization
https://github.com/pnugues/petit_larousse_1905
공식 코드Connecting a French Dictionary from the Beginning of the 20th Century to Wikidata
https://github.com/primeqa/clapnq
공식 코드CLAPNQ: Cohesive Long-form Answers from Passages in Natural Questions for RAG systems
https://github.com/princeton-nlp/corpus-poisoning
공식 코드Poisoning Retrieval Corpora by Injecting Adversarial Passages
https://github.com/quackenbushlab/dash
공식 코드Pruning neural network models for gene regulatory dynamics using data and domain knowledge
https://github.com/raivnlab/adanns
공식 코드https://github.com/rajarshd/Multi-Step-Reasoning
공식 코드Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.