A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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A survey examining trustworthiness challenges in retrieval-augmented generation for large language models, covering evaluation, robustness, and open problems.
J. Jakeman, Lorena A. Barba, Joaquim R. R. A. Martins, et al.
This paper proposes a framework for verification and validation (V&V) of scientific machine learning models to enhance trustworthiness and reliability in scientific applications.
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This paper proposes a framework to harden LLM fine-tuning by combining differentially private data selection with trustworthiness-aware model quantization to enhance privacy and robustness.
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This paper introduces Multitrust, a comprehensive benchmark for evaluating the trustworthiness of multimodal large language models across multiple dimensions.
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A survey of small language models covering architecture, capabilities, and trustworthiness, with emphasis on robustness evaluation.
Yujia Zhou, Yan Liu, Xiaoxi Li, et al.
This survey systematically reviews trustworthiness in Retrieval-Augmented Generation (RAG) systems, proposing a taxonomy of six key dimensions and analyzing current challenges and future directions.
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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.