Law of the Weakest Link: Cross Capabilities of Large Language Models

Abstract: The development and evaluation of LLMs have largely focused on individual capabilities. However, this overlooks the intersection of multiple abilities across different types of expertise that are often required for real-world tasks, which we term cross capabilities. To systematically explore this concept, we first define seven core individual capabilities and then pair them to form seven common cross capabilities, each supported by a manually constructed taxonomy. Building on these definitions, we introduce CrossEval, a benchmark comprising 1,400 human-annotated prompts, with 100 prompts for each individual and cross capability. To ensure reliable evaluation, we involve expert annotators to assess 4,200 model responses, gathering 8,400 human ratings with detailed explanations to serve as reference examples. Our findings reveal that, in both static evaluations and attempts to enhance specific abilities, current LLMs consistently exhibit the "Law of the Weakest Link," where cross-capability performance is significantly constrained by the weakest component. Specifically, across 58 cross-capability scores from 17 models, 38 scores are lower than all individual capabilities, while 20 fall between strong and weak, but closer to the weaker ability. These results highlight the under-performance of LLMs in cross-capability tasks, making the identification and improvement of the weakest capabilities a critical priority for future research to optimize performance in complex, multi-dimensional scenarios.

• The paper defines a comprehensive taxonomy by pairing seven core capabilities to systematically assess LLM performance.
• The paper introduces the CrossEval benchmark with 1,400 human-annotated prompts and 8,400 ratings for robust evaluation.
• The paper reveals that LLM performance on integrated tasks is limited by the weakest involved capability, underlining the need for targeted improvements.

Law of the Weakest Link: Cross Capabilities of LLMs

The research paper, "Law of the Weakest Link: Cross Capabilities of LLMs," investigates an often-overlooked aspect of LLM performance known as cross capabilities, which are critical for real-world tasks requiring the integration of multiple expertise areas. This paper introduces a systematic framework to explore these cross capabilities, aiming to fill the gap left by the prevalent isolated evaluation of individual LLM capabilities.

Key Contributions

The paper's contributions are anchored in three primary areas:

1. Taxonomy Definition: The authors define seven core individual capabilities (English, Reasoning, Coding, Image Recognition, Tool Use, Long Context, and Spanish) and formulate corresponding cross capabilities by pairing them. This taxonomy serves as a foundational organization that enables comprehensive evaluation across a wider spectrum of model functionalities.
2. CrossEval Benchmark: To empirically evaluate both individual and cross capabilities, the authors present CrossEval, a benchmark comprising 1,400 human-annotated prompts. Evaluation is performed on responses generated by various models, assessed by expert annotators, resulting in 8,400 detailed ratings with explanations, thus providing a robust dataset for evaluating cross capabilities.
3. Law of the Weakest Link: The paper reveals that LLM performance on cross-capability tasks is significantly constrained by the weakest of the involved capabilities, termed the "Law of the Weakest Link." This finding is consistent across multiple models and evaluation scenarios, emphasizing the critical need to identify and improve weaker capabilities to enhance overall performance.

Taxonomy and Benchmark Construction

The systematic approach to defining and evaluating capabilities is outlined through detailed taxonomies for both individual and cross capabilities. By constructing tasks with increasing complexity across capabilities such as mathematical calculation, coding, and image recognition, the paper ensures that the prompts reflect real-world scenarios requiring integrated skills.

The CrossEval benchmark, built from these taxonomies, includes 1,400 prompts annotated for capability and difficulty levels, accompanied by multiple model responses and human ratings. This framework allows for an in-depth analysis of LLM performance and the interplay between different capabilities.

Evaluating LLMs with CrossEval

The paper utilizes the CrossEval benchmark to evaluate 17 models from five major LLM families (GPT, Claude, Gemini, Llama, and Reka). The findings show that cross-capability performance is typically constrained by the weakest capability involved. For instance, in scenarios involving Tool Use, LLMs demonstrated significant performance dips, indicating the complexity and current inadequacies in this area.

Implications of the Findings

Practical Implications

The identification of the "Law of the Weakest Link" for cross capabilities implicates a targeted approach in LLM development. Enhancing weaker capabilities could lead to substantial improvements in overall model performance, particularly for complex, multifaceted tasks. For developers and researchers, this means prioritizing areas where models consistently underperform, such as tool use or context comprehension, can yield more practical and efficient systems in real-world applications.

Future Directions

The research opens up several avenues for future work. Enhancing weak capabilities without compromising strong ones will be a challenging but necessary endeavor, possibly involving fine-tuning methods or architectural changes to better integrate multiple capabilities. Going forward, the paper suggests a more nuanced evaluation approach, emphasizing the need for comprehensive benchmarks like CrossEval that can guide the development of more balanced and capable LLMs.

Conclusion

The paper provides a significant contribution to the evaluation of LLMs by introducing the CrossEval benchmark and highlighting the critical oversight in current evaluation practices. The "Law of the Weakest Link" effects observed in cross-capability performance underscore the importance of systematic benchmarking and targeted improvements in LLM development. This research not only advances our understanding of LLM capabilities but also sets a precedent for future evaluations focusing on the intersection of multiple skills necessary for real-world tasks.