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TrustLLM: Future Work
Abstract Introduction Background Trust LLM Preliminaries Assessments Trustworthiness Truthfulness Safety Fairness Robustness Privacy Protection Machine Ethics Transparency AccountabilityOpen Challenges Future Work Types of Ethical Agents Conclusions TRUSTLLM is a comprehensive framework designed to analyze and enhance the trustworthiness of Large Language Models (LLMs). This framework includes principles for different trust dimensions, established benchmarks, evaluation methods, and analysis of mainstream LLMs. Limitations and Future Directions: The research acknowledges several limitations and outlines seven future directions to improve LLM trustworthiness: Expansion of Prompt Templates: Aims to reduce errors and randomness by diversifying prompt templates. Inclusion of Diverse Datasets: Incorporates a broader...