Reward-Augmented Decoding: Efficient Controlled Text Generation With a Unidirectional Reward Model (2310.09520v4)

Abstract: While LLMs have proven effective in a huge range of downstream applications, they often generate text that is problematic or lacks a desired attribute. In this paper, we introduce Reward-Augmented Decoding (RAD), a text generation procedure that uses a small unidirectional reward model to encourage a LLM to generate text that has certain properties. Specifically, RAD uses the reward model to score generations as they are produced and rescales sampling probabilities to favor high-reward tokens. By using a unidirectional reward model, RAD can cache activations from prior generation steps to decrease computational overhead. Through experiments on generating non-toxic and sentiment-controlled text, we demonstrate that RAD performs best among methods that change only the generation procedure and matches the performance of state-of-the-art methods that involve re-training the LLM. We further validate that RAD is effective on very LLMs while incurring a minimal computational overhead.

• The paper presents RAD, which integrates a unidirectional reward model to adjust text generation without retraining large language models.
• RAD leverages caching and top-k sampling to minimize computational overhead while enhancing detoxification and sentiment control.
• Empirical evaluations on large-scale LLaMA models demonstrate that RAD efficiently balances output control and computational cost.

An Analysis of Reward-Augmented Decoding for Controlled Text Generation

The paper "Reward-Augmented Decoding: Efficient Controlled Text Generation With a Unidirectional Reward Model," presents an innovative approach to controlled text generation that does not require retraining a LLM. Instead, the authors propose a method called Reward-Augmented Decoding (RAD), which employs a unidirectional reward model to evaluate and steer text generation in real-time, effectively mitigating the computational and cost burdens associated with further training of LLMs.

The significance of this work lies in its ability to bridge the performance gap between standard weighted decoding techniques and methods involving substantial retraining of the model, such as DAPT and PPO. RAD introduces a minimal computational footprint by leveraging a smaller, unidirectional reward model that uses caching techniques. This allows for efficient computation of reward adjustments in a manner that minimizes computational overhead.

Key Contributions and Methodology

The paper's primary contribution is the RAD technique, which integrates a unidirectional reward model within the decoding process of LLMs. This reward model assesses the generated text against a specified attribute, such as non-toxicity or sentiment, and adjusts the token probabilities to favor those aligning with the desired attribute. This adjustment is done using a top-k sampling strategy where the probabilities are rescaled based on the reward scores.

RAD's efficiency stems from its unidirectionality; it caches activations from prior steps, thereby reducing overhead. The reward model is a smaller transformer model adapted to predict attribute-specific scores, thus ensuring it operates with manageable computational complexity even when applied to very large LLMs, such as LLaMA models with up to 65 billion parameters.

Numerical Results and Empirical Evaluation

Empirically, the RAD method demonstrates superior control over generated text attributes compared to existing decoding techniques. In detoxification tasks, RAD outperforms weighted decoding methods like GeDi and DExperts, and performs comparably to retraining strategies without incurring their computational expenses. Furthermore, in sentiment-controlled generation, RAD achieves a high alignment with desired sentiment outputs while maintaining fluency and diversity in the text.

The performance evaluation extends to several settings, including deployment on large-scale LLaMA LLMs, where RAD sustains its effectiveness and induces minimal additional computational cost. These results underscore RAD's potential as a scalable and economical solution for controlled text generation in practical applications.

Implications and Future Research Directions

The RAD method brings forth practical and theoretical implications for the use of LLMs in scenarios requiring specific output attributes. Practically, RAD offers a method to enhance safety and user alignment in AI text systems without the extensive costs of model retraining. Theoretically, it opens avenues for further research into efficient control mechanisms within generation processes, potentially expanding to tasks requiring more complex control or multi-attribute alignment.

The paper suggests directions for future work, notably the application of RAD to more sophisticated tasks such as instruction following, where reward models could guide complex conditional generation tasks. Another promising area is enhancing reward models through better architectural choices or by combining multiple smaller models.

In conclusion, RAD represents a substantial stride towards efficient, controlled text generation, providing a modular and adaptive solution capable of integrating with existing LLMs without necessitating costly retraining interventions. The approach outlined in this paper positions itself as a viable strategy for improved real-time control over text generation in deployed AI systems.