SELECT-LLM is the first active model selection framework for LLMs that uses expected information gain from pairwise output similarities to minimize required annotations, reporting up to 84.78% cost reduction across 23 datasets and 156 models.
Prompt packer: Deceiving llms through compositional instruction with hidden attacks.arXiv preprint arXiv:2310.10077, 2023
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A survey that proposes a lifecycle-centric framework and the Financial AI Security and Robustness Taxonomy to organize 17 attack subtypes on AI pipelines in finance.
A survey that organizes LLMs-as-judges research into functionality, methodology, applications, meta-evaluation, and limitations.
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Large Language Model Selection with Limited Annotations
SELECT-LLM is the first active model selection framework for LLMs that uses expected information gain from pairwise output similarities to minimize required annotations, reporting up to 84.78% cost reduction across 23 datasets and 156 models.
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When AI Meets Wall Street: A Survey on Trustworthy AI in Fintech
A survey that proposes a lifecycle-centric framework and the Financial AI Security and Robustness Taxonomy to organize 17 attack subtypes on AI pipelines in finance.
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LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods
A survey that organizes LLMs-as-judges research into functionality, methodology, applications, meta-evaluation, and limitations.