A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Dongyu Ru, Lin Qiu, Xiangkun Hu, et al.
RAGChecker is a fine-grained evaluation framework with diagnostic metrics for retrieval and generation modules in RAG systems, showing better correlation with human judgments.
Juncheng Wu, Sheng Liu, Haoqin Tu, et al.
This paper introduces a fine-grained evaluation framework decomposing LLM reasoning into knowledge correctness and reasoning quality, revealing that SFT improves accuracy but degrades reasoning, while RL enhances medical reasoning by pruning inaccura
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LongCite enables LLMs to generate fine-grained sentence-level citations in long-context QA, improving trustworthiness via a new benchmark, pipeline, dataset, and trained models.
Yubin Hong, Chaofan Li, Jingyi Zhang, et al.
FG-RAG introduces a context-aware fine-grained graph retrieval-augmented generation framework to improve query-focused summarization.
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This paper proposes perception-centric process reward models to improve vision-language models by providing fine-grained feedback during reasoning.
Sanjiban Choudhury
Proposes Agent Process Reward Models (AgentPRM) for fine-grained step-level supervision as a tractable alternative to large-scale RL for LLM agents.