Optimizing Language Models for Human Preferences is a Causal Inference Problem (2402.14979v2)

Abstract: As LLMs see greater use in academic and commercial settings, there is increasing interest in methods that allow LLMs to generate texts aligned with human preferences. In this paper, we present an initial exploration of LLM optimization for human preferences from direct outcome datasets, where each sample consists of a text and an associated numerical outcome measuring the reader's response. We first propose that LLM optimization should be viewed as a causal problem to ensure that the model correctly learns the relationship between the text and the outcome. We formalize this causal language optimization problem, and we develop a method--causal preference optimization (CPO)--that solves an unbiased surrogate objective for the problem. We further extend CPO with doubly robust CPO (DR-CPO), which reduces the variance of the surrogate objective while retaining provably strong guarantees on bias. Finally, we empirically demonstrate the effectiveness of (DR-)CPO in optimizing state-of-the-art LLMs for human preferences on direct outcome data, and we validate the robustness of DR-CPO under difficult confounding conditions.

• The paper introduces Causal Preference Optimization (CPO) and DR-CPO, leveraging causal inference to align model outputs with human preferences.
• It demonstrates that these methods outperform traditional optimization techniques under significant confounding conditions.
• The findings pave the way for integrating causal inference in AI, offering robust, human-aligned language model development.

Optimizing LLMs for Human Preferences as a Causal Inference Problem

Introduction

The customization of LLMs to align with human preferences is becoming increasingly important within both academic and commercial spheres. Standard optimization practices often run into considerable difficulties, notably when unravelling the intricate relationship between generated texts and the human responses they elicit. This research introduces a novel approach that frames the optimization of LLMs in light of human preferences as essentially a causal inference problem. By doing so, it aims to eliminate biases from unobserved confounders—variables that influence both the text being read and the reader's response to it, thereby causing misinterpretations of the data.

The Core of the Approach

The paper proposes a method known as Causal Preference Optimization (CPO), alongside its advanced form, Doubly Robust CPO (DR-CPO). These methods are designed to optimize LLMs by focusing on direct outcomes—numerical measures of reader responses—while controlling for confounding variables that could distort the optimization process. By leveraging the causal relationships inherent within the data, (DR-)CPO aims to make LLMs generate text that is objectively aligned with human preferences under rigorous evaluation conditions.

The CPO and DR-CPO methods are theoretically grounded in causal inference techniques, using importance weighting to address observed confounding bias. In essence, DR-CPO improves upon the straightforward CPO through variance reduction, enhancing the stability and reliability of optimization outcomes. Through a series of carefully designed experiments involving state-of-the-art LLMs and various datasets, the paper empirically proves the effectiveness of these methods.

Results and Implications

The paper’s experimental validation demonstrates that (DR-)CPO methods notably outperform traditional LLM optimization techniques in aligning model outputs with human preferences. Particularly under conditions of significant confounding, DR-CPO showcases robust performance, underscoring its theoretical promise with practical efficacy. Moreover, these findings reveal the potential pitfalls of relying solely on outcome modeling in LLM optimization, as this approach can be severely compromised under strong confounding.

The Future of AI and LLM Optimization

Looking ahead, the research opens several promising pathways. Firstly, it sets the stage for further exploration into the integration of causal inference with machine learning, beyond LLMs, into broader AI system optimization. Secondly, it invites methodological innovations to enhance the robustness and efficiency of causal optimization methods, such as integrating entropy regularization techniques into (DR-)CPO.

Furthermore, extending the application of DR-CPO to paired completion data presents an attractive avenue for bridging the gap between direct outcome optimization and reinforcement learning from human feedback (RLHF) paradigms. Such advancements could usher in a new era of LLM development, where models are not only exceptionally proficient in understanding and generating human language but are also intrinsically aligned with human values and preferences.

In conclusion, by framing LLM optimization as a causal inference problem and introducing (DR-)CPO as a solution, this paper provides a significant leap forward in our understanding and methodology for tailoring LLMs to human preferences. The implications for both the theoretical underpinnings of AI research and practical applications in developing socially beneficial technologies are profound, charting a course for future investigations into the nexus of causality, human feedback, and artificial intelligence.