Societal Alignment Frameworks Can Improve LLM Alignment (2503.00069v1)

Abstract: Recent progress in LLMs has focused on producing responses that meet human expectations and align with shared values - a process coined alignment. However, aligning LLMs remains challenging due to the inherent disconnect between the complexity of human values and the narrow nature of the technological approaches designed to address them. Current alignment methods often lead to misspecified objectives, reflecting the broader issue of incomplete contracts, the impracticality of specifying a contract between a model developer, and the model that accounts for every scenario in LLM alignment. In this paper, we argue that improving LLM alignment requires incorporating insights from societal alignment frameworks, including social, economic, and contractual alignment, and discuss potential solutions drawn from these domains. Given the role of uncertainty within societal alignment frameworks, we then investigate how it manifests in LLM alignment. We end our discussion by offering an alternative view on LLM alignment, framing the underspecified nature of its objectives as an opportunity rather than perfect their specification. Beyond technical improvements in LLM alignment, we discuss the need for participatory alignment interface designs.

Societal Alignment Frameworks Can Enhance LLM Alignment

The paper "Societal Alignment Frameworks Can Improve LLM Alignment" introduces a nuanced perspective on the alignment of LLMs by integrating insights from societal alignment frameworks, which draw from social, economic, and contractual domains. The authors argue that such an interdisciplinary perspective can address ongoing challenges in aligning LLMs with human values—challenges that stem from the complexity and singularity of human values compared to the often narrow and incomplete technological solutions traditionally employed.

Conceptual Framework and Methodology

The authors approach LLM alignment using a principal-agent framework, a well-grounded concept in economic theory. Within this framework, the LLM functions as the agent, while the principal is represented by the model developer or user. This paradigm aids in outlining how the agent's actions can be incentivized through reward mechanisms. Importantly, this involves crafting a "contract," essentially a mapping between actions an LLM might take and the corresponding reward or penalty. However, this inherently results in incomplete contracts due to the difficulty in fully specifying all human values and possible scenarios within the constraints of an LLM's operational framework.

Challenges in LLM Alignment

The paper identifies several critical issues with current LLM alignment practices. Misspecified objectives often lead to "reward hacking," where the model optimizes for the alignment function without fulfilling the intended behavioral norms. Additionally, 'jailbreaking' showcases the ease with which malicious or controversial content can be generated by circumventing the weak points in alignment through specifically crafted inputs. These vulnerabilities highlight the limitations of current approaches reliant on reinforcement learning from human feedback (RLHF) and point to the need for an enriched approach to complete these "contracts" between LLM outputs and human values.

Implications of Societal Alignment Frameworks

1. Social Alignment: The paper suggests incorporating societal and cultural norms into LLM training to reflexively guide models toward better alignment with implicit social expectations. Recognizing that norms and values are both dynamic and diverse, and can help adjust models as public values evolve. However, this proposes a challenge in the predominantly Western-centric datasets and models, indicating a bias that demands a more pluralistic approach.
2. Economic Alignment: Economic theories like Pareto efficiency provide a lens for balancing competing human preferences, ensuring LLMs cater equitably to diverse user groups. By applying welfare-centric objectives, models can strive to balance individual and collective human interests.
3. Contractual Alignment: Formalizing rules and performance metrics, drawn from legal frameworks, can aid in crafting better LLM guidelines and internalize principles akin to legal reasoning within models. Constitutional AI and scalable oversight are potential methodologies to enforce these principles without the burdensome requirement of continuous human oversight.

Uncertainty and Its Role in Alignment

The paper also offers insights into the role of uncertainty in LLM deployment. While epistemic uncertainty can degrade model reliability, societal alignment acknowledges other uncertainties—cultural and normative—as inherent and necessary. A key suggestion is the development of techniques for communicating uncertainty to users, which can mitigate the overconfident projections that often plague LLM outputs.

The Democratic Opportunity in Incompleteness

The authors propose an alternative view that sees the indeterminacy of LLM objectives not as a flaw but as an opportunity to democratize alignment processes, advocating for participatory design that reflects diverse stakeholder input. This perspective considers alignment a societal task beyond mere technical refinement, requiring ongoing engagement from a broad cross-section of users.

Conclusion

In summary, the paper advocates for a multi-faceted approach to LLM alignment, one that transcends traditional technological paradigms and incorporates principles and strategies from societal alignment frameworks. This synthesis not only helps mitigate challenges related to incomplete specification but also opens avenues for more robust and ethical AI systems, which are crucial for future AI deployments. Further research into this interdisciplinary alignment strategy will be vital to refining these methods and ensuring that AI systems evolve to better serve diverse global needs while adhering to ethical considerations.