https://github.com/disi-unibo-nlp/rag-when-how-chunk
공식 코드Reconstructing Context: Evaluating Advanced Chunking Strategies for Retrieval-Augmented Generation
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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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Reconstructing Context: Evaluating Advanced Chunking Strategies for Retrieval-Augmented Generation
Learning to Verify Summary Facts with Fine-Grained LLM Feedback
Learning Semantic Association Rules from Internet of Things Data
TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones
Rationale-Guided Retrieval Augmented Generation for Medical Question Answering
SpikE: spike-based embeddings for multi-relational graph data
An energy-based model for neuro-symbolic reasoning on knowledge graphs
Toward General Instruction-Following Alignment for Retrieval-Augmented Generation
Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generation
HeavyWater and SimplexWater: Watermarking Low-Entropy Text Distributions
Fast Think-on-Graph: Wider, Deeper and Faster Reasoning of Large Language Model on Knowledge Graph
Learning from Mistakes via Cooperative Study Assistant for Large Language Models
TypedThinker: Typed Thinking Improves Large Language Model Reasoning
Contrastive Cross-Course Knowledge Tracing via Concept Graph Guided Knowledge Transfer
Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation
Retrieval-Augmented Perception: High-Resolution Image Perception Meets Visual RAG
Face the Facts! Evaluating RAG-based Fact-checking Pipelines in Realistic Settings
Assessing the Promise and Pitfalls of ChatGPT for Automated Code Generation
Beyond Quacking: Deep Integration of Language Models and RAG into DuckDB
Speaker attribution in German parliamentary debates with QLoRA-adapted large language models
AALC: Large Language Model Efficient Reasoning via Adaptive Accuracy-Length Control
LMR-BENCH: Evaluating LLM Agent's Ability on Reproducing Language Modeling Research
MLR-Copilot: Autonomous Machine Learning Research based on Large Language Models Agents
On Diversified Preferences of Large Language Model Alignment
Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM
PclGPT: A Large Language Model for Patronizing and Condescending Language Detection
Taiyi: A Bilingual Fine-Tuned Large Language Model for Diverse Biomedical Tasks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.