Summary of Benchagents: Automated Benchmark Creation with Agent Interaction, by Natasha Butt et al.
BENCHAGENTS: Automated Benchmark Creation with Agent Interaction
by Natasha Butt, Varun Chandrasekaran, Neel Joshi, Besmira Nushi, Vidhisha Balachandran
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework, BENCHAGENTS, leverages large language models (LLMs) to automate benchmark creation for complex generative capabilities. This addresses the limitations of current evaluations, which rely heavily on human-annotated benchmarks that are slow and expensive to create. The framework decomposes the benchmark creation process into planning, generation, data verification, and evaluation, each executed by an LLM agent interacting with other agents and utilizing human-in-the-loop feedback. BENCHAGENTS enables the creation of high-quality benchmarks for complex capabilities like planning and constraint satisfaction during text generation. A study using these benchmarks highlights common failure modes and model differences among seven state-of-the-art models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Benchmarks are important tools that help us evaluate how well AI models can perform certain tasks. Right now, creating new benchmarks is a slow and expensive process because it requires humans to label large amounts of data. This limits our ability to test and improve AI models as they evolve. A team of researchers has developed a framework called BENCHAGENTS that uses artificial intelligence (AI) to automate the creation of these benchmarks. This makes it faster and cheaper to create high-quality benchmarks for complex tasks like planning and constraint satisfaction during text generation. By using BENCHAGENTS, scientists can learn more about what AI models are good at and where they struggle. |
Keywords
* Artificial intelligence * Text generation
