https://github.com/fpezzuti/multistage-finetuning
공식 코드Exploring the Effectiveness of Multi-stage Fine-tuning for Cross-encoder Re-rankers
Papers with Code 아카이브
Knowledge Graphs
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
- 19
A knowledge graph is a structured representation of information that organizes data into nodes (entities) and edges (relationships) to show how different pieces of knowledge are interconnected. It enables enhanced data integration, search, and inference by modeling the relationships between concepts and entities in a graph format.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/fploetzky/websci2024
공식 코드Lost in Recursion: Mining Rich Event Semantics in Knowledge Graphs
https://github.com/framartin/trap
공식 코드TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification
https://github.com/francesco-sovrano/from-philosophy-to-interfaces-an-explanatory-method-and-a-tool-inspired-by-achinstein-s-theory-of-e
공식 코드https://github.com/francesco-sovrano/yai4hu
공식 코드https://github.com/francescoferrini/multirelationalgnn
공식 코드Meta-Path Learning for Multi-relational Graph Neural Networks
https://github.com/franciscoliu/MLLMU-Bench
공식 코드Protecting Privacy in Multimodal Large Language Models with MLLMU-Bench
https://github.com/franciszchen/surgbox
공식 코드SurgBox: Agent-Driven Operating Room Sandbox with Surgery Copilot
https://github.com/franco-galante/rgb-model
공식 코드Information Retrieval in the Age of Generative AI: The RGB Model
https://github.com/francois-rd/accord
공식 코드$\texttt{ACCORD}$: Closing the Commonsense Measurability Gap
https://github.com/francoisluus/KGReasoning
공식 코드https://github.com/fraunhofer-iais/chunking-strategies
공식 코드Rethinking Chunk Size For Long-Document Retrieval: A Multi-Dataset Analysis
https://github.com/freedomintelligence/acegpt
공식 코드https://github.com/freedomintelligence/huatuo-26m
공식 코드https://github.com/freedomintelligence/huatuogpt
공식 코드https://github.com/freedomintelligence/llmzoo
공식 코드https://github.com/freedomintelligence/rag-instruct
공식 코드RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructions
https://github.com/freedomintelligence/sharegpt-4o-image
공식 코드ShareGPT-4o-Image: Aligning Multimodal Models with GPT-4o-Level Image Generation
https://github.com/freesunshine0316/nary-grn
공식 코드Tackling Graphical NLP problems with Graph Recurrent Networks
https://github.com/frenzymath/REAL-Prover
공식 코드REAL-Prover: Retrieval Augmented Lean Prover for Mathematical Reasoning
https://github.com/frontierlabs/mapdia
공식 코드Interpersonal Memory Matters: A New Task for Proactive Dialogue Utilizing Conversational History
https://github.com/fsoft-ai4code/xmainframe
공식 코드XMainframe: A Large Language Model for Mainframe Modernization
https://github.com/fu-dayuan/preact
공식 코드https://github.com/fudan-zvg/Brain3D
공식 코드https://github.com/fudandisc/disc-finllm
공식 코드DISC-FinLLM: A Chinese Financial Large Language Model based on Multiple Experts Fine-tuning
https://github.com/fudandisc/disc-lawllm
공식 코드DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services
https://github.com/fudandisc/disc-medllm
공식 코드DISC-MedLLM: Bridging General Large Language Models and Real-World Medical Consultation
https://github.com/fudandisc/piors
공식 코드https://github.com/fudandisc/socialagent
공식 코드https://github.com/fudanselab/agent4se-paper-list
공식 코드Large Language Model-Based Agents for Software Engineering: A Survey
https://github.com/funaudiollm/cosyvoice
공식 코드https://github.com/funaudiollm/inspiremusic
공식 코드https://github.com/furrutiav/nllf-emnlp-2023
공식 코드Deep Natural Language Feature Learning for Interpretable Prediction
https://github.com/fusion-jena/automatic-kg-creation-with-llm
공식 코드https://github.com/fusion-jena/information-retrieval-using-multiple-llm-and-rag
공식 코드https://github.com/futuredialchallenge/2024-rag
공식 코드The 2nd FutureDial Challenge: Dialog Systems with Retrieval Augmented Generation (FutureDial-RAG)
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.