HyPerAlign: Interpretable Personalized LLM Alignment via Hypothesis Generation (2505.00038v2)

Abstract: Alignment algorithms are widely used to align LLMs to human users based on preference annotations. Typically these (often divergent) preferences are aggregated over a diverse set of users, resulting in fine-tuned models that are aligned to the ``average-user'' preference. Nevertheless, current models are used by individual users in very specific contexts and situations, emphasizing the need for user-dependent preference control. In this work we address the problem of personalizing LLM outputs to their users. We aim to generate customized responses tailored to specific individuals instead of generic outputs that emulate the collective voices of diverse populations. We propose HyPerAlign, an interpretable and sample-efficient hypothesis-driven personalization approach for LLM models. Given few-shot examples written by a particular user, we first infer hypotheses about their communication strategies, personality, and writing style, then prompt LLM models with these hypotheses and user-specific attributes to generate customized outputs. We conduct experiments on two different personalization tasks, namely authorship attribution and deliberative alignment, with datasets from diverse domains (news articles, blog posts, emails, jailbreaking benchmarks). Results demonstrate the superiority of hypothesis-driven LLM personalization compared to preference-based fine-tuning methods. For authorship attribution, HyPerAlign generations have consistently high win-rates (commonly $> 90\%$) against state-of-the-art preference fine-tuning approaches across diverse user profiles and LLM models. For deliberative alignment, the helpfulness of LLM models is improved by up to $70\%$ on average. Overall, HyPerAlign represents an interpretable and sample-efficient strategy for the personalization of LLM models to individual users.

Hypotheses-driven Personalized Alignment for LLMs

The paper by Cristina Gârbacea and Chenhao Tan presents a novel approach to enhance the personalization of LLMs. Current alignment techniques, such as reinforcement learning from human feedback (RLHF) and contrastive learning, often result in models tuned to a generalized user preference profile, potentially leading to generic and uninspiring outputs. This paper addresses the need for more individualized output by proposing a hypotheses-driven personalization method that is interpretable and sample-efficient in aligning LLMs with individual users.

Methodology

The paper introduces a two-step methodology for the personalization of LLM models. Initially, it infers user-specific attributes, which include the user's writing style, personality traits, communication strategies, and overall persona through few-shot user demonstrations. Hypogenic, a system designed for generating human-interpretable hypotheses, is utilized to extract these characteristics. Once the hypotheses are formed, they are employed in prompting the LLM to generate tailored responses that align with the inferred user attributes. One of the notable aspects of this approach is its reliance on instruction-tuned models, which leverage existing zero-shot and in-context learning capabilities to customize outputs efficiently, negating the necessity for extensive preference datasets or costly fine-tuning algorithms.

Experimental Setup and Results

The proposed method is tested on two personalization tasks: authorship attribution and deliberative alignment, with datasets covering domains such as news articles, blog posts, emails, and benchmarks for jailbreaking. Results are striking, with hypotheses-driven personalization exhibiting high win-rates—often exceeding 90%—against preference-based fine-tuning methods in authorship attribution tasks. For deliberative alignment, the approach dramatically reduces harmfulness scores by up to 70% on average, making models safer and more helpful in contexts requiring a delicate balance between helpfulness and harmlessness.

The experiments demonstrate that the inferred hypotheses are robust across various LLM models and adaptable to out-of-distribution datasets. Even smaller models, like Mistral 7B, show competitive performance when guided by well-formed hypotheses, suggesting that input structuring might be more critical than model size for personalization. This robust generalization across models highlights the practicality and reliability of the proposed strategy.

Implications and Future Directions

The implications of this research are significant, both theoretically and practically. By enabling LLMs to produce more individualized outputs that resonate with the user's voice and intent, it enhances user engagement and satisfaction. Furthermore, the method's interpretability and sample-efficient nature offer insights into aligning AI systems more closely with human preferences without requiring invasive data acquisition processes.

Future investigations could delve into continuous adaptation to evolving user preferences and the integration of long-term personalization memory within LLMs. Such advancements would aim to foster nuanced and context-aware personalization that supports diverse real-world applications. Moreover, exploring user feedback mechanisms to validate the fidelity of hypothesis generation and model outputs would be an interesting direction for further research.

Ethical Considerations

While personalized alignment presents exciting opportunities for improving the user experience and model helpfulness, it necessitates a cautious approach to mitigate risks associated with echo chambers and potential amplification of biased or unethical viewpoints. Ensuring that personalized models align within societal norms and ethical guidelines is crucial in harnessing personalization's benefits responsibly.

In conclusion, the hypotheses-driven personalized alignment approach represents a meaningful leap toward individualized LLM output, presenting promising avenues to refine AI systems’ adaptability to human preferences more explicitly and beneficially.