TrustLLM: Trustworthiness in Large Language Models (AI summary)

Key Points

1. LLMs (Large Language Models) have gained considerable attention for their natural language processing capabilities, but they present challenges in terms of trustworthiness.

2. The paper introduces T RUST LLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions.

3. The study proposes a set of principles for trustworthy LLMs that span eight different dimensions and establishes a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics.

4. Evaluation of 16 mainstream LLMs in T RUST LLM showed that trustworthiness and utility (i.e., functional effectiveness) are positively related. Some LLMs exhibited strong performance in stereotype categorization, natural language inference, and resilience to adversarial attacks.

5. Proprietary LLMs generally outperformed most open-source counterparts in terms of trustworthiness, but a few open-source LLMs demonstrated competitive trustworthiness.

6. Challenges in truthfulness were identified, and the importance of incorporating external knowledge sources for improvement in performance was highlighted.

7. Safety concerns in open-source LLMs were emphasized, with the significance of balancing safety without over-caution being discussed.

8. Most LLMs exhibited unsatisfactory performance in stereotype recognition and fairness, highlighting the need for improvements in this area.

9. The complexity of trustworthiness in LLMs and the importance of ensuring transparency in the models themselves and the technologies that underpin trustworthiness were emphasized, with a call for collaboration to advance the trustworthiness of LLMs.

Summary

The paper "TRUST LLM: Trustworthiness in Large Language Models" provides a comprehensive study of trustworthiness in large language models (LLMs) and its importance in natural language processing and generative AI. The paper introduces the TRUST LLM framework, which includes principles for different dimensions of trustworthiness, a benchmark across six dimensions, and an evaluation of trustworthiness for mainstream LLMs. The eight facets of trustworthiness identified in the paper are truthfulness, safety, fairness, robustness, privacy, machine ethics, transparency, and accountability.

Capabilities and Applications of LLMs
The capabilities and applications of LLMs, including their usage in diverse language-related tasks such as automated article writing, translation, search functionalities, software engineering, and various fields of scientific research, are extensively discussed. The exceptional capabilities of LLMs are attributed to factors such as the usage of large-scale raw texts for training, transformer architecture with a large number of parameters, and advanced training schemes.

Addressing Concerns about Trustworthiness
The paper addresses concerns about the trustworthiness of LLMs, highlighting challenges such as complexity and diversity of outputs, data biases and private information in training datasets, and high user expectations. The efforts taken by developers to enhance trustworthiness are discussed, including the usage of alignment with human preference, employment of methodologies such as supervised fine-tuning and reinforcement learning from human feedback, and the establishment of safety mechanisms and ethical considerations.

The evaluation of trustworthiness of LLMs, benchmarking across various tasks and datasets, and the comparison of proprietary and open-source LLMs are highlighted. The findings of the study indicate a positive correlation between trustworthiness and utility, concerns about over-alignment in LLMs, and the disparity between proprietary and open-source models in terms of trustworthiness. Additionally, insights into individual dimensions of trustworthiness such as truthfulness, safety, fairness, robustness, privacy, and machine ethics are discussed, emphasizing the complexity of ensuring trustworthiness in LLMs.

Importance of Transparency
The paper underscores the importance of transparency not only in the LLMs themselves but also in the technologies that underpin trustworthiness. It emphasizes the need for continued research efforts to enhance the reliability and ethical alignment of LLMs. The comparison with other trustworthiness-related benchmarks and the importance of principles for trustworthiness assessment of LLMs are also addressed in the paper.

Overall, the paper provides a comprehensive overview of the capabilities, concerns, and efforts to enhance the trustworthiness of large language models.

Reference: https://arxiv.org/abs/2401.05561