Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment (2308.05374v2)

Abstract: Ensuring alignment, which refers to making models behave in accordance with human intentions [1,2], has become a critical task before deploying LLMs in real-world applications. For instance, OpenAI devoted six months to iteratively aligning GPT-4 before its release [3]. However, a major challenge faced by practitioners is the lack of clear guidance on evaluating whether LLM outputs align with social norms, values, and regulations. This obstacle hinders systematic iteration and deployment of LLMs. To address this issue, this paper presents a comprehensive survey of key dimensions that are crucial to consider when assessing LLM trustworthiness. The survey covers seven major categories of LLM trustworthiness: reliability, safety, fairness, resistance to misuse, explainability and reasoning, adherence to social norms, and robustness. Each major category is further divided into several sub-categories, resulting in a total of 29 sub-categories. Additionally, a subset of 8 sub-categories is selected for further investigation, where corresponding measurement studies are designed and conducted on several widely-used LLMs. The measurement results indicate that, in general, more aligned models tend to perform better in terms of overall trustworthiness. However, the effectiveness of alignment varies across the different trustworthiness categories considered. This highlights the importance of conducting more fine-grained analyses, testing, and making continuous improvements on LLM alignment. By shedding light on these key dimensions of LLM trustworthiness, this paper aims to provide valuable insights and guidance to practitioners in the field. Understanding and addressing these concerns will be crucial in achieving reliable and ethically sound deployment of LLMs in various applications.

The Critical Dimensions of Aligning LLMs for Trustworthiness

In the paper titled "Trustworthy LLMs: A Survey and Guideline for Evaluating LLMs' Alignment," the authors present a comprehensive examination of the processes necessary to ensure LLMs act in accordance with human intentions and societal values. The significance of their work is grounded in the growing reliance on LLMs across numerous applications, coupled with the necessity to mitigate risks associated with misaligned model behaviors.

While OpenAI has notably contributed to the deployment of aligned LLMs such as ChatGPT, the authors identify a gap in systematic guidance for evaluating LLM alignment with social norms and regulatory frameworks. Their work embarks on this challenge by surveying the dimensions critical to consider: reliability, safety, fairness, misuse resistance, explainability, social norms, and robustness. Notably, they identify 29 sub-categories under these dimensions, outlining the sophisticated nature of LLM trustworthiness.

Reliability Concerns: Misinformation and Hallucination

A dominant challenge in assessing reliability is addressing LLMs' propensity for generating misinformation and hallucinated content. The survey identifies a disparity in factual accuracies across various tasks and highlights that effective alignment reduces these errors without a one-size-fits-all solution. The complexity of aligning LLMs is evident from documented inconsistencies in model responses to logically equivalent prompts, compounded by issues of miscalibration where models express unjustified confidence in their outputs. As LLMs are increasingly deployed in high-stakes domains such as healthcare and finance, resolving these reliability issues stands critical.

Safety and Ethical Deployment

The paper emphasizes the necessity of ensuring LLM outputs avoid harmful content including violence, unlawful instructions, and privacy violations. Current models can inadvertently echo such content present in vast internet datasets unless strategically aligned. The need for protective measures extends to social norm compliance, suggesting that LLMs should reflect universally acknowledged values while being culturally sensitive. Here, the practice of generating alignment data tapping into societal norms offers utility both in preempting unsafe outputs and in creating a feedback system for continuous improvement.

Fairness and Bias Mitigation

In evaluating fairness, the authors underline potential pitfalls such as stereotype perpetuation and preference biases inherent in LLM training data. They propose proactive alignment strategies, emphasizing meticulous dataset curation and alignment tasks that ensure equitable treatment across demographic groups—a task compounded by LLMs' discrepancies in linguistic and cultural understanding. These dynamics are explored in relation to fairness definitions such as "justice" and "impartiality," reinforcing the moral dimension of deploying LLMs in society.

Misuse and Robustness

Addressing resistance to misuse, the paper highlights potential misuses, such as propagandistic misuse or cyberattacks, facilitated by LLMs' content generation capabilities. To combat these challenges, alignment data must imbue models with the foresight to reject unsafe requests while being resilient to adversarial prompt attacks. The robustness of LLMs is further evaluated against perturbations and distribution shifts, areas where model retraining and reinforcement learning offer potential mitigation pathways. At the same time, awareness of possible interventional effects—where LLM deployment inadvertently shifts user behavior—necessitates adaptive alignment strategies.

Implications and Future Directions

This paper's taxonomy and measurement framework provide valuable insights into the evaluation and development of trustworthy LLMs, suggesting a direction toward iterative refinement of alignment processes and comprehensive assessment criteria. The implications for real-world applications are profound, urging continuous improvements in alignment strategies to enhance LLM reliability and societal integration.

The survey concludes with calls for rigorous advancements in alignment techniques, particularly in underexplored areas such as causal reasoning and robust adversarial testing. This ongoing research requirement underscores the complexity of aligning LLMs within the tapestry of both emergent technological abilities and dynamic human values—an endeavor vital to the reliable and ethical utilization of LLMs in future AI-driven landscapes.