A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Strategy (2501.09431v1)

Abstract: While LLMs present significant potential for supporting numerous real-world applications and delivering positive social impacts, they still face significant challenges in terms of the inherent risk of privacy leakage, hallucinated outputs, and value misalignment, and can be maliciously used for generating toxic content and unethical purposes after been jailbroken. Therefore, in this survey, we present a comprehensive review of recent advancements aimed at mitigating these issues, organized across the four phases of LLM development and usage: data collecting and pre-training, fine-tuning and alignment, prompting and reasoning, and post-processing and auditing. We elaborate on the recent advances for enhancing the performance of LLMs in terms of privacy protection, hallucination reduction, value alignment, toxicity elimination, and jailbreak defenses. In contrast to previous surveys that focus on a single dimension of responsible LLMs, this survey presents a unified framework that encompasses these diverse dimensions, providing a comprehensive view of enhancing LLMs to better serve real-world applications.

An Examination of Responsible LLMs: Addressing Risks and Mitigation Strategies

LLMs have emerged as transformative tools within the field of natural language processing, with their applications spanning code generation, autonomous systems, urban planning, and various societal benefits such as enhancing education and supporting underserved groups. However, the deployment of these models is not without its challenges, notably the inherent risks tied to privacy leakage, hallucinated outputs, and value misalignment. Furthermore, there is potential for their malicious use, such as generating toxic content when models are manipulated or jailbroken. A recent survey by Wang et al. tackles these concerns by presenting a structured framework for responsibly developing and utilizing LLMs. This paper organizes the mitigation strategies across different phases: data collection and pre-training, fine-tuning and alignment, prompting and reasoning, and post-processing and auditing.

Risks in LLMs

The paper thoroughly categorizes risks into two overarching groups: inherent risks and malicious use. Inherent risks include privacy issues, hallucinations, and value misalignment. Privacy risks involve the potential leakage of sensitive information, residing in both the model's training data and its responses, while hallucinations refer to the generation of inaccurate or misleading information. Value misalignment arises when LLM outputs do not reflect a broad set of human values, potentially propagating unethical content. In the context of malicious use, the production of toxic content such as hate speech through jailbroken models is a pressing concern.

Mitigation Strategies

1. Data Collection and Pre-training: The initial step involves rigorous data cleansing to remove noise and potential bias from raw datasets. This step aims to minimize hallucinations and privacy threats by ensuring the quality and relevance of the pre-training data.
2. Fine-tuning and Alignment: Here, models are adapted for specific tasks via techniques like Reinforcement Learning from Human Feedback (RLHF), which aligns model outputs with human values and expectations. This phase is crucial for refining the model’s ethical orientation and reducing unwanted biases.
3. Prompting and Reasoning: The paper explores advanced prompting strategies such as Chain-of-Thought reasoning to enhance the model's decision-making process. New methods aim to guide LLMs to produce coherent, ethical, and accurate responses, lowering the risk of generating inappropriate or harmful content.
4. Post-Processing and Auditing: The final phase focuses on scrutinizing and sanitizing model outputs. Techniques in this phase include the use of algorithmic audits to identify and remove any remnants of toxic or unethical language from generated texts.

Comprehensive Framework

Contrasted with single-dimension surveys, this paper’s framework integrates multiple dimensions of LLM responsibility, facilitating a holistic understanding of how to optimize model integrity. Notably, the inclusion of the entire lifecycle of LLM development—from data gathering to response auditing—underscores the necessity for continuous and multifaceted oversight.

Implications and Future Directions

The insights offered by Wang et al. have significant implications for both theoretical advancements in AI and practical applications. The proposed strategies suggest pathways to elevate model trustworthiness while maintaining their utility in real-world applications. A promising area for future exploration is the development of LLMs with built-in mechanisms to self-identify and rectify irresponsible behavior, drawing inspiration from the human cognitive process.

In summary, the paper contributes a comprehensive examination of the risks associated with LLMs and provides detailed methodologies for their mitigation across various phases of the model lifecycle. As LLMs continue to evolve, ongoing research will be pivotal in addressing the complex interplay between model capabilities and ethical deployment, ensuring that they serve as beneficial and reliable tools in society.