SkillsBench: Benchmarking how well agent skills work across diverse tasks (arxiv.org)

Article summary

The article presents SkillsBench, a benchmark for measuring the effectiveness of agent skills in large language models (LLMs) across diverse tasks. The benchmark consists of 87 tasks across 8 domains, paired with curated skills and deterministic verifiers. The evaluation shows that curated skills can improve the average pass rate from 33.9% to 50.5%. The study also finds that smaller models with skills can match larger models without them.

Main themes

• Agent skills in LLMs
• Benchmarking and evaluation
• Task performance and improvement
• Model size and complexity
• Human feedback and guidance
• Knowledge degradation and loss

What commenters say

• The effectiveness of LLMs degrades with each successive layer of automation, leading to decreased performance and increased errors.
• The use of human feedback and guidance is crucial for improving the performance of LLMs and preventing knowledge degradation.
• The concept of 'semantic collapse' describes the degradation of meaning and understanding that occurs when LLMs are used to generate and regenerate text or code without human input.
• Some argue that LLMs can learn and improve through self-generated skills, while others believe that human input and guidance are necessary for meaningful progress.
• The evaluation of LLMs and their skills should consider real-world scenarios and tasks, rather than limited and artificial benchmarks.
• The importance of human judgment and common sense in guiding LLMs and preventing them from going off the rails is often underestimated.
• The use of skills and knowledge engineering can help LLMs to develop and improve, but the framework and approach used are critical to success.
• There is a need for more nuanced and realistic evaluations of LLMs and their capabilities, rather than relying on simplistic or misleading benchmarks and metrics.