A Survey of Useful LLM Evaluation (2406.00936v1)

Abstract: LLMs have gotten attention across various research domains due to their exceptional performance on a wide range of complex tasks. Therefore, refined methods to evaluate the capabilities of LLMs are needed to determine the tasks and responsibility they should undertake. Our study mainly discussed how LLMs, as useful tools, should be effectively assessed. We proposed the two-stage framework: from core ability'' toagent'', clearly explaining how LLMs can be applied based on their specific capabilities, along with the evaluation methods in each stage. Core ability refers to the capabilities that LLMs need in order to generate high-quality natural language texts. After confirming LLMs possess core ability, they can solve real-world and complex tasks as agent. In the "core ability" stage, we discussed the reasoning ability, societal impact, and domain knowledge of LLMs. In the ``agent'' stage, we demonstrated embodied action, planning, and tool learning of LLMs agent applications. Finally, we examined the challenges currently confronting the evaluation methods for LLMs, as well as the directions for future development.

Insights into LLM Evaluation: Core Abilities and Agent Applications

LLMs have seen widespread adoption due to their capability to handle complex tasks, from text generation to intricate robotic manipulation. The paper “A Survey of Useful LLM Evaluation” presents a nuanced exploration of evaluation methodologies, dissecting the transition from “core ability” to “agent” roles in LLMs.

The authors propose a two-stage evaluation framework that ensures a comprehensive assessment of LLM capabilities, initially focusing on intrinsic linguistic competencies and subsequently on practical applications. This dual-phased evaluation provides a structured means of analyzing LLM utility.

Core Ability Evaluation

The paper divides the evaluation of core abilities into three key areas: reasoning, societal impact, and domain knowledge.

1. Reasoning: This fundamental ability includes logical, mathematical, commonsense, multi-hop, and structured data reasoning. Diverse datasets such as GSM8K for mathematical problem-solving or WebCPM for commonsense tasks are employed, showing that LLMs exhibit variability in performance across different reasoning tasks. Achieving human-level reasoning remains an evolving challenge, with finer nuances like causal detection or sequential decision-making being areas of active development.
2. Societal Impact: Here, the focus is on safety and truthfulness. Evaluations extend beyond traditional metrics to include aspects like content safety, privacy concerns, and bias mitigation. Benchmarks such as ToxicChat and CValues highlight the broader ethical considerations, driving the focus on creating methods that enhance the trustworthiness and security of LLM outputs. The discussions on bias reveal that despite advancements, systemic biases remain a critical challenge.
3. Domain Knowledge: The paper examines how LLMs are leveraged across finance, legislation, psychology, medicine, and education. Notably, specialized models like BloombergGPT illustrate how domain-specific training can enhance performance. The evaluation often relies on domain-specific datasets, prompting a closer look at the integration of expert knowledge in evaluation frameworks.

Agent-Level Applications

Building on core abilities, the paper explores LLMs as agents, focusing on planning, various applications, and established benchmarks.

1. Planning: This capability emphasizes the LLM-based agent's role in dynamic environments, requiring autonomous task decomposition and execution. Tools like SayPlan indicate the growing sophistication in task planning capabilities.
2. Application Scenarios: Evaluation covers areas like web grounding, code generation, and robotic manipulation. Scenarios such as API interactions via tools like API-Bank extend LLM utility into practical domains, with the evaluation focusing on executing real-world tasks. The enhancement of code generation through frameworks like Code as Policies demonstrates the transition of LLMs into areas requiring precise and adaptive decision-making.
3. Benchmarks: Several benchmarks such as WebArena and MIND2WEB explore the LLM capabilities in controlled environments, assessing the integration of multiple tools. These benchmarks are essential for diagnosing model behavior under various conditions, underscoring the need for diverse yet coherent evaluation strategies.

Future Directions and Challenges

The paper highlights the need for dynamic evaluations and suggests integrating intelligent models as evaluators, to keep pace with LLM advancements. Addressing root-cause analysis is emphasized, as understanding model decisions becomes crucial for iterative improvement. Furthermore, as LLM applications in robotics expand, benchmarks in robotic applications need enhancement to account for real-world complexities and sim-to-real challenges.

Conclusion

While LLMs continue to exhibit remarkable capabilities, their evaluation, as outlined in the paper, remains a rapidly evolving challenge. By adopting comprehensive evaluation frameworks and addressing existing limitations, we can ensure that LLMs evolve as a genuinely useful tool, consistently meeting the growing demands of complex human-centered applications. The paper serves as both a critique and a guide, outlining past achievements and paving the way for future advancements in LLM evaluation methodologies.