The development and evaluation of Large Language Models (LLMs) have primarily focused on assessing individual abilities, overlooking the importance of how these capabilities intersect to handle complex, real-world tasks—referred to as cross capabilities.
To address this gap, a joint research team from Meta and the University of Illinois Urbana-Champaign introduces CrossEval, a benchmark designed to assess both individual and cross capabilities. Their findings, presented in the paper Law of the Weakest Link: Cross Capabilities of Large Language Models, demonstrate that LLMs often adhere to the “Law of the Weakest Link”—where performance on complex tasks is limited by the weakest capability.
The researchers explore the following key questions:
RQ1: How can we define individual and cross capabilities in LLMs?
They identify seven core capabilities—English, Reasoning, Coding, Image Recognition, Tool Use, Long Context, and Spanish—and create common cross-capability pairs like Coding & Reasoning and Image Recognition & Reasoning. These abilities are mapped to a detailed taxonomy that breaks down complex tasks into two levels, providing a foundation for benchmarking.
RQ2: How can we benchmark these capabilities?
Using CrossEval, a framework built on a taxonomy-based approach, they manually create 1,400 prompts across various difficulty levels. Each prompt tests a specific capability or cross-capability combination, generating 4,200 model responses. These responses are evaluated by expert human annotators, who provide 8,400 ratings with explanations. Additionally, LLM-based evaluators are introduced to assess model performance, showing strong agreement with human judgments.
RQ3: What patterns emerge in cross-capability performance?
The evaluations reveal that cross-capability performance is generally constrained by the weakest individual capability, following the “Law of the Weakest Link” effect. This pattern holds across different models and evaluators, underscoring the limiting impact of weak individual abilities on overall performance in complex tasks.
RQ4: How do changes in individual capabilities influence cross-capability performance?
The team investigates how boosting specific capabilities impacts cross-capability tasks. Their findings show that enhancing weaker abilities leads to significant improvements, while changes in stronger capabilities produce only minor effects. This reinforces the idea that cross-capability performance is shaped by the weakest link.
In conclusion, the paper highlights a crucial gap in current LLM development—cross capabilities are essential for handling real-world tasks but remain underexplored in model evaluation.
The paper Law of the Weakest Link: Cross Capabilities of Large Language Models is on arXiv.
Author: Hecate He | Editor: Chain Zhang
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