サインイン
インサイト - NaturalLanguageProcessing
Enhancing Large Language Models for Legal Reasoning: The KRAG Framework and Soft PROLEG Implementation
Integrating structured knowledge representation (Knowledge Representation Augmented Generation - KRAG) significantly improves the accuracy, stability, and explainability of Large Language Models (LLMs) in legal reasoning, as demonstrated by the Soft PROLEG system.
TableRAG: Enhancing Language Model Understanding of Large Tables Using Retrieval-Augmented Generation
TableRAG, a novel Retrieval-Augmented Generation framework, significantly improves the ability of Language Models (LMs) to understand and answer questions on large tables by selectively retrieving the most relevant schema and cell data.
Reward-RAG: Improving Retrieval-Augmented Generation by Fine-tuning Retrieval Models with Reward-Driven Supervision from a CriticGPT
Reward-RAG enhances the relevance and quality of Retrieval-Augmented Generation (RAG) systems by using a reward model, trained on CriticGPT feedback, to fine-tune retrieval models for better alignment with human preferences.
© 2024 by Linnk AI