Holistic Evaluation of Language Models (2211.09110v2)

Abstract: LLMs (LMs) are becoming the foundation for almost all major language technologies, but their capabilities, limitations, and risks are not well understood. We present Holistic Evaluation of LLMs (HELM) to improve the transparency of LLMs. First, we taxonomize the vast space of potential scenarios (i.e. use cases) and metrics (i.e. desiderata) that are of interest for LMs. Then we select a broad subset based on coverage and feasibility, noting what's missing or underrepresented (e.g. question answering for neglected English dialects, metrics for trustworthiness). Second, we adopt a multi-metric approach: We measure 7 metrics (accuracy, calibration, robustness, fairness, bias, toxicity, and efficiency) for each of 16 core scenarios when possible (87.5% of the time). This ensures metrics beyond accuracy don't fall to the wayside, and that trade-offs are clearly exposed. We also perform 7 targeted evaluations, based on 26 targeted scenarios, to analyze specific aspects (e.g. reasoning, disinformation). Third, we conduct a large-scale evaluation of 30 prominent LLMs (spanning open, limited-access, and closed models) on all 42 scenarios, 21 of which were not previously used in mainstream LM evaluation. Prior to HELM, models on average were evaluated on just 17.9% of the core HELM scenarios, with some prominent models not sharing a single scenario in common. We improve this to 96.0%: now all 30 models have been densely benchmarked on the same core scenarios and metrics under standardized conditions. Our evaluation surfaces 25 top-level findings. For full transparency, we release all raw model prompts and completions publicly for further analysis, as well as a general modular toolkit. We intend for HELM to be a living benchmark for the community, continuously updated with new scenarios, metrics, and models.

Holistic Evaluation of LLMs

The paper "Holistic Evaluation of LLMs" presents an extensive framework for the evaluation of LLMs (LMs), proposed by the Center for Research on Foundation Models (CRFM) at Stanford University. This work articulates a structured approach to assessing these models, departing from traditional evaluation methods, and emphasizing a comprehensive examination across scenarios and metrics.

Core Contributions

The primary contributions of this paper are outlined as follows:

1. Top-down Taxonomy Approach: The proposed approach is a departure from prior bottom-up evaluations. The authors introduce a systematic taxonomy that first identifies the evaluation criteria (scenarios and metrics) before implementing and assessing models. This methodology contrasts with previous practices by explicitly specifying evaluation desiderata from the outset, thereby identifying gaps in current evaluation practices.
2. Multi-metric Evaluation: Unlike traditional benchmarks that predominantly focus on accuracy, this paper argues for a multi-metric approach. The authors posit that metrics beyond accuracy—such as robustness, fairness, and efficiency—are of equal importance and should be integrated within the evaluation framework. This multi-faceted evaluation is tailored to reflect the specific requirements of different use cases, ensuring a holistic view of model performance.
3. Standardization of Evaluation: The work addresses the inconsistencies in the evaluation of LLMs. By standardizing the conditions under which models are evaluated, the proposed framework ensures comparability across diverse scenarios. This is a significant improvement over prior evaluations, where many core scenarios lacked a standardized evaluation process.

Methodology

The evaluation framework is rooted in an architecture termed evaluation primitives, which consists of:

• Scenarios: Defined use cases expressing what we want to evaluate.
• Models and Adaptation: The LLMs themselves and the processes used to adapt them for specific tasks.
• Metrics: Quantitative measures used to determine performance, tailored to the specific use case requirements.

This modular approach delineates the evaluation process into discrete, manageable components, facilitating comprehensive and consistent assessments.

Numerical Results and Findings

The paper presents detailed numerical results highlighting the efficacy of their evaluation framework. Post-implementation, the framework has resulted in a standardized evaluation across various scenarios where previously no such standardization existed. The paper reports a significant increase in the comprehensiveness and consistency of model evaluations, demonstrating the benefits of the proposed taxonomy and multi-metric approach.

Implications and Future Directions

The implications of this research are both practical and theoretical. Practically, the proposed framework provides a robust mechanism for stakeholders to evaluate and benchmark LLMs, enhancing transparency and comparability. Theoretically, the work paves the way for future research to explore and refine multi-metric evaluation criteria, ensuring they keep pace with the evolving demands of AI applications.

Future developments in this area may include expanding the taxonomy to incorporate emerging metrics and scenarios, fostering greater alignment between model evaluation practices and real-world applications. Additionally, the continuous refinement of the multi-metric approach could lead to more nuanced insights into model performance, thereby driving advancements in model development and deployment practices.

In conclusion, this paper presents a meticulously devised framework that promises to elevate the standards of LLM evaluation. By prioritizing a holistic assessment strategy, it sets a foundation for future research aimed at crafting more effective and reliable AI systems.