Eliciting Human Preferences with Language Models (2310.11589v1)

Abstract: LLMs (LMs) can be directed to perform target tasks by using labeled examples or natural language prompts. But selecting examples or writing prompts for can be challenging--especially in tasks that involve unusual edge cases, demand precise articulation of nebulous preferences, or require an accurate mental model of LM behavior. We propose to use LMs themselves to guide the task specification process. In this paper, we introduce Generative Active Task Elicitation (GATE): a learning framework in which models elicit and infer intended behavior through free-form, language-based interaction with users. We study GATE in three domains: email validation, content recommendation, and moral reasoning. In preregistered experiments, we show that LMs prompted to perform GATE (e.g., by generating open-ended questions or synthesizing informative edge cases) elicit responses that are often more informative than user-written prompts or labels. Users report that interactive task elicitation requires less effort than prompting or example labeling and surfaces novel considerations not initially anticipated by users. Our findings suggest that LM-driven elicitation can be a powerful tool for aligning models to complex human preferences and values.

• The paper introduces the gate method, enabling language models to actively generate queries that clarify ambiguous user preferences.
• It demonstrates that gate outperforms traditional supervised and active learning techniques in content recommendation, moral reasoning, and email validation.
• The framework reduces user effort and enhances satisfaction, paving the way for more personalized and adaptive AI decision-making systems.

A Critical Analysis of Generative Active Task Elicitation in LLMs

This paper introduces an innovative approach termed Generative Active Task Elicitation (gate), expanding the capabilities of LLMs (LMs) in aligning with complex human preferences through a process of interactive, language-based engagement. The authors construct a novel framework where the LMs themselves take on the active role in eliciting intended user behaviors and preferences, surpassing traditional methods that passively rely on prompts or examples provided by the user.

Research Methodology and Findings

The authors evaluate their newly proposed framework, gate, across three different domains: content recommendation, moral reasoning, and email validation. These domains represent challenges with varying complexities, thereby testing the adaptability and robustness of gate as a method for preference elicitation.

Their experiments reveal that gate methods are not only effective but often superior to traditional supervised learning, prompting, and active learning techniques. The generative open-ended questions policy, a sub-method under gate, is particularly effective in eliciting nuanced user preferences even with reduced mental effort required from users compared to other methods. These results are evidenced by improved prediction accuracies and user-reported measures of ease and satisfaction in task specification.

Technical Contributions

The paper's primary technical contribution lies in its ability to utilize LMs to actively generate questions or scenarios that help resolve ambiguities in user preferences. Unlike traditional active learning which relies on task-specific examples, gate can dynamically adapt to new information provided by the user, forming a feedback loop that refines the model's understanding of human preferences. This dynamic interaction is pivotal for tasks that are inherently complex or ambiguous, such as discerning a user's broad content interests or their intricate moral judgments.

Implications and Future Directions

From a practical standpoint, gate offers a pathway to developing more personalized and efficiently aligned AI systems, capable of understanding and replicating human-like decision-making processes. The fluidity with which LMs can now engage in preference elicitation suggests promising applications in areas requiring high degrees of personalization, such as custom content delivery, policy-making, and advisory systems.

Theoretically, this work stimulates further inquiry into interactive machine learning frameworks, inviting future research to explore more structured optimization strategies in generative elicitation processes. The flexibility in modality suggests potential applications beyond text, including speech and multimodal inputs which further broaden its applicability.

Conclusion

While the authors provide compelling evidence for the enhanced capabilities of gate, the paper acknowledges its limitations, particularly the reliance on specific datasets and demographic biases inherent in participant samples. The research thereby serves as a foundational step, urging the exploration of diverse and inclusive datasets to fully capture the spectrum of human preferences and decision-making processes.

In conclusion, the paper makes a significant contribution to human-computer interaction research, championing a shift from passive specification to an interactive, user-driven paradigm that leverages the full potential of generative LLMs. As AI continues to integrate deeper into societal frameworks, frameworks like gate could be instrumental in ensuring these systems better reflect the nuanced values and preferences of the communities they serve.