Efficient Benchmarking of Language Models (2308.11696v5)

Abstract: The increasing versatility of LLMs (LMs) has given rise to a new class of benchmarks that comprehensively assess a broad range of capabilities. Such benchmarks are associated with massive computational costs, extending to thousands of GPU hours per model. However, the efficiency aspect of these evaluation efforts had raised little discussion in the literature. In this work, we present the problem of Efficient Benchmarking, namely, intelligently reducing the computation costs of LM evaluation without compromising reliability. Using the HELM benchmark as a test case, we investigate how different benchmark design choices affect the computation-reliability trade-off. We propose to evaluate the reliability of such decisions, by using a new measure -- Decision Impact on Reliability, DIoR for short. We find, for example, that a benchmark leader may change by merely removing a low-ranked model from the benchmark, and observe that a correct benchmark ranking can be obtained by considering only a fraction of the evaluation examples. Based on our findings, we outline a set of concrete recommendations for efficient benchmark design and utilization practices. To take a step further, we use our findings to propose an evaluation algorithm, that, when applied to the HELM benchmark, leads to dramatic cost savings with minimal loss of benchmark reliability, often reducing computation by x100 or more.

Efficient Benchmarking of LLMs: A Summary

The paper under review introduces the concept of Efficient Benchmarking in the context of evaluating LLMs (LMs), proposing strategies to alleviate the computational cost associated with such tasks. The increasing diversity and capabilities of LMs necessitate comprehensive benchmarks that stretch beyond niche tasks, thereby demanding substantial computational resources. The authors address this resource challenge by focusing on the HELM benchmark and presenting novel methods to reduce the cost of LM evaluation without compromising reliability.

Key Contributions and Methodologies

The primary contribution of this paper lies in its exploration and analytical validation of the strategies for efficient benchmarking. The authors propose the Decision Impact on Reliability (DIoR) metric, a novel measure designed to evaluate the impact of design decisions on the reliability of benchmarks. Through DIoR, the authors quantitatively assess various components of benchmark design, including the choice of scenarios, subscenarios, examples, few-shot prompts, and aggregation metrics like Mean Win Rate (MWR).

The empirical analysis reveals several findings of significant relevance:

1. Scenarios Selection: The authors demonstrate that dropping scenarios to save computational resources leads to reduced reliability. The benchmark's reliability heavily depends on the choice of scenarios, suggesting that existing practices of reducing the number of scenarios require reevaluation.
2. Subscenarios Aggregation: The investigation into subscenarios indicates that aggregating them into scenarios adversely affects reliability. Surprisingly, treating subscenarios as standalone entities improves reliability, necessitating a reconsideration of aggregation practices.
3. Example Utilization: A notable result is the high reliability achieved even with a significantly reduced number of examples. The finding that ranks remain stable with fewer examples challenges the need for extensive examples in every instance.
4. Prompt Sampling: The paper suggests that uniform sampling of few-shot prompts improves reliability compared to the comprehensive evaluation approach. This finding emphasizes the potential of sampling strategies that balance computational cost and reliability.
5. Metric Analysis: Critically, the choice of comparative measures like MWR, although prevalent, introduces variability issues and susceptibility to gaming. This insight encourages the development and adoption of more robust metric systems that account for true model ability rather than relative comparisons.

Implications and Future Directions

The implications of this paper are profound for the field of AI evaluation. Practically, implementing the proposed techniques can bring remarkable computational savings, making benchmarks more accessible and environmentally considerate. The theoretically rigorous approach to benchmarking decisions encourages a rethinking of evaluation protocols to align with both validity and reliability standards.

The introduction of the DIoR metric is particularly noteworthy for future benchmarking strategies, serving as a quantitative guide to weigh efficiency against reliability. The paper hints at potential advancements in dynamic evaluation algorithms, signaled by the successful Flash-HELM demonstration that cuts computation by up to 200x while preserving benchmark integrity.

In conclusion, this research provides a foundational step toward more efficient benchmarking in machine learning, emphasizing the need to revisit longstanding assumptions in benchmark design. Future explorations may further refine these methodologies, expand their applicability to other AI domains, and investigate the intricate balance between computational feasibility and robust evaluation. The work, therefore, not only informs current practices but also sets a trajectory toward sustainable and valid model assessment paradigms.