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Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment

Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "CC BY-NC-SA 4.0 (arXiv)" does not allow redistributing the text itself. Original — arxiv.org/pdf/2308.05374

Summary

Teams that align large language models have no clear, principled method for checking whether model outputs respect social norms, values and regulations. This gap makes systematic iteration before deployment difficult. This ByteDance Research survey proposes a taxonomy of LLM trustworthiness built on seven top-level dimensions: how reliable, safe and fair a model is, how well it resists misuse, how explainable it is and how well it reasons, whether it follows social norms, and how robust it is. These are split into 29 sub-categories, each discussed alongside relevant literature and proposed mitigations. The authors then select 8 sub-categories and run automated measurement studies on several widely used LLMs. To avoid expensive human annotation, they recast some questions as multiple-choice items and use a stronger model (gpt-4) as the judge. In general, models with more publicly reported alignment effort score better, but the benefit differs from one dimension to another, which argues for finer-grained testing. The authors also show that their generated prompts can serve as alignment training data. The paper is useful as a checklist and evaluation template for teams building or auditing aligned LLMs.

Key points

  • The taxonomy has 7 major categories and 29 sub-categories. Examples include hallucination, sycophancy, miscalibration, privacy violation, stereotype bias, leaking copyrighted content, limited causal reasoning, cultural insensitivity, prompt attacks and poisoning attacks.
  • The case studies cover:
    • hallucination
    • safety
    • fairness
    • miscalibration
    • propagandistic and cyberattack misuse
    • copyright leakage
    • causal reasoning
    • robustness
  • For the alignment-data experiment, the authors generated:
    • 433 prompts for SFT
    • 443 prompts with positive and negative answers for the reward model, reused for RLHF
    • 100 safety test prompts
  • The pretrained models GPT-2 (aligned with minChatGPT) and LLaMA-7B (fine-tuned with Alpaca) both improved. gpt-4 rated 78% of outputs from the SFT-tuned LLaMA-7B as better than those of the base model.
  • The authors acknowledge limitations: the taxonomy is incomplete, and LLM-as-judge evaluation is less accurate than human review. Open questions include whether RLHF is the best alignment method, and how alignment data and labeler bias affect the results.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Other

Cited by

148 papers of our library cite it (of the 140673 whose bibliographies we read); 6 of them have a page here, 142 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2308.05374 · arXiv 2308.05374

@misc{taufiq2023trustworthy,
  title = {{Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment}},
  author = {Taufiq;, Yang Liu; Yuanshun Yao; Jean-Francois Ton; Xiaoying Zhang; Ruocheng Guo; Hao Cheng; Yegor Klochkov; Muhammad Faaiz and Li, Hang},
  year = {2023},
  doi = {10.48550/arxiv.2308.05374},
  eprint = {2308.05374},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2308.05374}
}

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