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Q-Probe: A Lightweight Approach to Reward Maximization for Language Models (2402.14688v2)

Published 22 Feb 2024 in cs.LG

Abstract: We present an approach called Q-probing to adapt a pre-trained LLM to maximize a task-specific reward function. At a high level, Q-probing sits between heavier approaches such as finetuning and lighter approaches such as few shot prompting, but can also be combined with either. The idea is to learn a simple linear function on a model's embedding space that can be used to reweight candidate completions. We theoretically show that this sampling procedure is equivalent to a KL-constrained maximization of the Q-probe as the number of samples increases. To train the Q-probes we consider either reward modeling or a class of novel direct policy learning objectives based on importance weighted policy gradients. With this technique, we see gains in domains with ground-truth rewards (code generation) as well as implicit rewards defined by preference data, even outperforming finetuning in data-limited regimes. Moreover, a Q-probe can be trained on top of an API since it only assumes access to sampling and embeddings. Code: https://github.com/likenneth/q_probe .

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Citations (7)
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Summary

  • The paper introduces Q-Probing, a method that leverages a simple linear function on the embedding space to reweight model completions for improved task-specific rewards.
  • It employs rejection sampling and importance weighted policy gradients, providing a compute-efficient alternative to traditional finetuning.
  • Empirical evaluations show that Q-Probe outperforms finetuning and few-shot prompting in tasks like code generation and preference-based adaptation in data-limited scenarios.

Q-Probe: Enhancing LLM Performance with Lightweight Probing

Introduction

LLMs have demonstrated impressive capabilities in various natural language processing tasks. However, adapting these models to specific tasks often requires further adjustment to better align their output with specific goals or reward functions. Traditional methods for this adaptation include finetuning and prompting, each with its costs and benefits. We introduce an alternative approach named Q-probing, designed to optimize a pre-trained LLM for a task-specific reward function efficiently. Q-probing operates by learning a simple linear function on the model's embedding space to reweight candidate completions, striking a middle ground between less intensive methods like few-shot prompting and more comprehensive ones like finetuning.

Theoretical Framework

Q-probing capitalizes on the insight that the requisite knowledge for many tasks already exists within the LLM due to its pre-training, and task-specific adaptation is more about extracting relevant information. It employs a method that can be viewed as a form of rejection sampling, where candidate completions are drawn from the LLM, evaluated through the probe, and reweighted according to their estimated utility. Theoretically, this method approximates a KL-constrained optimization process, aiming to maximize the task-specific reward as the number of completions (samples) increases.

Methodology

Q-probing's implementation involves training a Q-probe, a linear model, using either a reward modeling approach or direct policy learning via importance weighted policy gradients. This allows the adaptation to different feedback types (ground-truth rewards or user preferences) and interaction levels (offline datasets or online reward access). Notably, Q-probe training requires minimal computational resources compared to traditional finetuning methods and can easily be applied on top of LLMs accessed via API, broadening its applicability.

Empirical Evaluation

Q-probing demonstrates superior performance in several domains, notably in code generation tasks where it outperforms both traditional finetuning methods and few-shot prompting, especially in data-limited scenarios. For tasks with implicit rewards defined by preference data, Q-probe again shows an ability to surpass baseline methods, including finetuning strategies, validating its effectiveness across different types of feedback.

Implications and Future Directions

The Q-probing method presents a compelling alternative for adapting LLMs to specific tasks, especially when computational resources are limited or when working with API-based models. Its ability to effectively utilize the model's pre-trained capabilities with minimal additional training represents a significant efficiency gain. Future research could explore the potential of Q-probes in iterative finetuning processes or in conjunction with other adaptation strategies to further enhance performance. Additionally, investigating the transferability of probes across tasks could unveil new insights into LLMs' representational properties and the nature of task-specific knowledge within these models.

Conclusion

Q-probing offers a balanced and efficient approach to optimizing LLMs for specific tasks, bridging the gap between lightweight prompting methods and computationally expensive finetuning. By leveraging the model's inherent capabilities with a targeted probing strategy, it sets the stage for more adaptable, efficient, and effective use of LLMs across a wide range of applications.

This summary highlights the key points of the Q-probing approach, focusing on its theoretical foundation, implementation details, evaluation results, and implications for future research within the field of generative AI and LLMs.

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