Optimizing Temperature for Language Models with Multi-Sample Inference (2502.05234v2)

Abstract: Multi-sample aggregation strategies, such as majority voting and best-of-N sampling, are widely used in contemporary LLMs to enhance predictive accuracy across various tasks. A key challenge in this process is temperature selection, which significantly impacts model performance. Existing approaches either rely on a fixed default temperature or require labeled validation data for tuning, which are often scarce and difficult to obtain. This paper addresses the challenge of automatically identifying the (near)-optimal temperature for different LLMs using multi-sample aggregation strategies, without relying on task-specific validation data. We provide a comprehensive analysis of temperature's role in performance optimization, considering variations in model architectures, datasets, task types, model sizes, and predictive accuracy. Furthermore, we propose a novel entropy-based metric for automated temperature optimization, which consistently outperforms fixed-temperature baselines. Additionally, we incorporate a stochastic process model to enhance interpretability, offering deeper insights into the relationship between temperature and model performance.

• The paper introduces an entropy-based TURN algorithm that automatically tunes temperature to balance sample diversity and quality in LLMs.
• The method leverages token-level entropy turning points to dynamically adjust settings, consistently outperforming fixed-temperature baselines with a mean performance drop of only ~0.4%.
• The study offers a scalable framework for optimizing multi-sample inference in tasks like mathematical problem solving and code generation without relying on expensive labeled data.

Optimizing Temperature for LLMs with Multi-Sample Inference: A Critical Analysis

The paper "Optimizing Temperature for LLMs with Multi-Sample Inference" addresses the pivotal role of temperature tuning in the performance optimization of LLMs. The authors bridge the gap between empirical methods dependent on labeled data and the need for an automated, reliable method for temperature selection without such expensive and often unavailable resources.

Importance of Temperature in Multi-Sample Aggregation

The research begins with an insightful review of multi-sample aggregation techniques such as majority voting and best-of-N sampling, which have become instrumental in extracting superior results from LLMs across diverse applications. Nevertheless, the authors emphasize the critical, yet underexplored, parameter of temperature that influences the quality and diversity of generated samples. The paper argues that optimal temperature varies with model architecture, data distribution, and task specificity—a detail often overlooked by static temperature settings used in current practices.

Novel Contributions

The authors propose a novel entropy-based metric for automatic temperature tuning that exploits the entropy turning point (EntP) in the token-level entropy curve. This point indicates a shift from low to high entropy, marking the onset of sampling quality deterioration. Through rigorous experiments, they demonstrate the proposed TURN method's effectiveness across model types, task domains, and aggregation strategies. The TURN algorithm efficiently identifies temperatures that balance diversity and quality in sampled outputs, adapting dynamically to different model and task characteristics.

Strong Numerical Results

The results presented are compelling. TURN consistently outperforms fixed-temperature baselines across a spectrum of tasks like mathematical problem-solving and code generation. The empirical validations reveal a high correlation between EntP-aligned temperatures and optimal grid-search-derived temperatures, manifesting in minimal performance degradation (mean performance drop around 0.4%).

Theoretical and Practical Implications

The introduction of the stochastic process model for illustrating the EntP phenomenon further augments the theoretical understanding of temperature effects in LLM sampling. This model, alongside the empirical findings, provides a coherent framework that sheds light on the interplay between sampling diversity and predictive accuracy. Practically, TURN offers a scalable solution for deploying LLMs in environments where labeled validation data is scarce, making it a valuable tool for optimizing inference in real-world applications.

Future Directions

While the paper lays a solid foundation for automated temperature tuning, several avenues for future research remain. Exploring the application of the TURN algorithm across additional domains beyond those tested may validate its robustness further. Additionally, refining the EntP detection mechanism to accommodate more granular changes in entropy or incorporating real-time adjustments could enhance the adaptability of LLMs in dynamic environments. Investigating interactions between temperature and other hyperparameters, such as sampling techniques like top-k or nucleus sampling, may further enrich understanding and control over model behavior during inference.

In conclusion, this paper makes significant strides in addressing the challenge of temperature selection in LLMs, providing both a practical tool and theoretical insights that empower practitioners and researchers to enhance model performance efficiently. The work exemplifies the type of research that progressively refines the field of AI by integrating empirical evidence with theoretical advancements, ultimately pushing the boundaries of what LLMs can achieve.