Large Language Model
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 0
- 보존된 평가 기록
- 0
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/daehuikim/WikiOFGraph
공식 코드https://github.com/daiquocnguyen/QGNN
공식 코드https://github.com/daiquocnguyen/QuatRE
공식 코드QuatRE: Relation-Aware Quaternions for Knowledge Graph Embeddings
https://github.com/dair-iitd/tkbi
공식 코드Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols
https://github.com/damo-nlp-sg/chain-of-knowledge
공식 코드https://github.com/damo-nlp-sg/llm-r2
공식 코드LLM-R2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency
https://github.com/dandoge/palm
공식 코드https://github.com/dangxingyu/rnn-icrag
공식 코드RNNs are not Transformers (Yet): The Key Bottleneck on In-context Retrieval
https://github.com/daniel-chin/flute-x-gpt
공식 코드https://github.com/daniel-gong/chatgpt-wm
공식 코드https://github.com/danieladam2001/llm-based-validation
공식 코드Traceable LLM-based validation of statements in knowledge graphs
https://github.com/danielfperez/castillo
공식 코드CASTILLO: Characterizing Response Length Distributions of Large Language Models
https://github.com/danielkty/debiasing-rag
공식 코드https://github.com/danilo-dessi/skg
공식 코드https://github.com/daod/spring
공식 코드https://github.com/daromog/q-vid
공식 코드Question-Instructed Visual Descriptions for Zero-Shot Video Question Answering
https://github.com/darthreca/segmentation-losses-nlp
공식 코드Beyond Accuracy Optimization: Computer Vision Losses for Large Language Model Fine-Tuning
https://github.com/data-engineering-and-semantics/openkgbiblio
공식 코드https://github.com/datarobot/syftr
공식 코드https://github.com/datascienceuibk/triviahg
공식 코드TriviaHG: A Dataset for Automatic Hint Generation from Factoid Questions
https://github.com/davendw49/PK-Chat
공식 코드PK-Chat: Pointer Network Guided Knowledge Driven Generative Dialogue Model
https://github.com/david-li0406/contextulization-distillation
공식 코드Contextualization Distillation from Large Language Model for Knowledge Graph Completion
https://github.com/davidheineman/category-fluency
공식 코드https://github.com/davidlamprecht/autordf2gml
공식 코드AutoRDF2GML: Facilitating RDF Integration in Graph Machine Learning
https://github.com/davidlamprecht/rdf-gnn-recommendation
공식 코드Bridging RDF Knowledge Graphs with Graph Neural Networks for Semantically-Rich Recommender Systems
https://github.com/davidlvxin/TransC
공식 코드Differentiating Concepts and Instances for Knowledge Graph Embedding
https://github.com/daynauth/elo_paper
공식 코드Ranking Unraveled: Recipes for LLM Rankings in Head-to-Head AI Combat
https://github.com/dcdmllm/align2llava
공식 코드https://github.com/dcdmllm/momentor
공식 코드Momentor: Advancing Video Large Language Model with Fine-Grained Temporal Reasoning
https://github.com/dcdmllm/worldgpt
공식 코드https://github.com/dchang56/snomed_kge
공식 코드Benchmark and Best Practices for Biomedical Knowledge Graph Embeddings
https://github.com/dcy0577/Text2BIM
공식 코드Text2BIM: Generating Building Models Using a Large Language Model-based Multi-Agent Framework
https://github.com/ddrrnn123/casc-ai
공식 코드CASC-AI: Consensus-aware Self-corrective AI Agents for Noise Cell Segmentation
https://github.com/de-tum/gnce
공식 코드Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networks
https://github.com/de-tum/mperl
공식 코드Markov Process-Based Graph Convolutional Networks for Entity Classification in Knowledge Graphs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.