EDT: Improving Large Language Models' Generation by Entropy-based Dynamic Temperature Sampling (2403.14541v2)

Abstract: Recently, LLMs have demonstrated outstanding performance across a wide range of downstream language tasks. Temperature sampling is a commonly used decoding strategy for LLMs' generation process. However, a fixed temperature parameter is used in most cases, which may not always be an optimal choice for balancing generation quality and diversity. In this paper, we propose an effective Entropy-based Dynamic Temperature (EDT) Sampling method, to achieve a more balanced performance in terms of both generation quality and diversity by dynamically selecting the temperature parameter. Additionally, we also show model performance and comprehensive analyses for 4 different generation benchmarks. Our experiments show that EDT significantly outperforms the existing strategies across different tasks.

• The paper presents an entropy-based dynamic temperature sampling approach that adjusts the temperature at each token based on predictive entropy.
• The paper demonstrates that this dynamic method outperforms fixed and KL-divergence-based strategies across various NLG tasks like summarization, QA, and translation.
• The paper suggests future work integrating learnable networks for automatic hyperparameter tuning to further optimize LLM performance.

Enhancing LLMs with Entropy-based Dynamic Temperature Sampling for Improved Generation Quality and Diversity

Introduction to Dynamic Temperature Sampling

LLMs have achieved remarkable performance across a variety of natural language generation (NLG) tasks. However, the challenge of balancing generation quality and diversity remains. This is particularly evident in the common practice of using a fixed temperature setting during the decoding process, which does not always yield optimal results. To address this, the paper introduces an Entropy-based Dynamic Temperature (EDT) Sampling method. This approach dynamically adjusts the temperature parameter based on the entropy of the predictive distribution, aiming to strike a better balance between generation quality and diversity.

Analyzing Fixed Temperature Drawbacks

The authors commence their exploration by analyzing the limitations of fixed temperature strategies across four different NLG tasks. They illustrate that a singular temperature setting does not consistently guarantee the best generation quality, underscoring the need for a more adaptable decoding strategy. This preliminary paper forms the basis for their argument in favor of dynamic temperature adjustment.

Entropy-based Dynamic Temperature (EDT) Sampling

The notion of adjusting the decoding temperature based on the entropy of the predictive distribution is central to the EDT method. High entropy, indicating lower model confidence, prompts a higher temperature to encourage diversity. Conversely, lower entropy, reflecting higher confidence, leads to a reduced temperature, thereby favoring generation quality.

Algorithm Overview

The EDT algorithm operates at the token level, dynamically selecting the temperature for each decoding step. It computes the entropy of the predictive distribution and adjusts the temperature accordingly. The algorithm is showcased to be efficient, requiring minimal computational overhead compared to a fixed temperature strategy and significantly reducing the resources needed compared to other dynamic temperature methods like KL-divergence-based temperature sampling.

Experimental Evaluation

The paper thoroughly evaluates the EDT method across various benchmarks including summarization, question answering, and machine translation. The results show that EDT consistently outperforms both fixed and dynamic (KL-divergence-based) temperature strategies in balancing generation quality and diversity. Additionally, the experiments reveal that applying dynamic temperature adjustments at the token level is more effective than adjusting on an instance level.

Insights and Implications

The superior performance of EDT underscores the importance of aligning the temperature parameter more closely with the model's predictive confidence. This strategy not only enhances the output quality in terms of both relevance and variety but also introduces a novel paradigm in the deployment of LLMs for NLG tasks, which could significantly influence future research and applications in the field.

Conclusion and Future Directions

The paper presents a compelling case for Entropy-based Dynamic Temperature Sampling as a superior method for controlling the balance between quality and diversity in LLM-generated text. Looking forward, the authors suggest exploring the potential of integrating learnable networks for automatic hyperparameter tuning and selection, which could further refine the efficiency and applicability of the EDT method across a broader spectrum of NLG tasks.

The introduction of the EDT method represents a significant step forward in the ongoing advancement of LLMs, offering a promising avenue for future research aimed at fully unlocking the generative capabilities of these powerful models.