AI BriefingBenchmarking Agent Tool-Use Capabilities
Key point
LangChain has released 4 new testing environments that measure the tool use and function calling performance of LLM agents.
Details
To address the challenges of building and evaluating LLM agents, LangChain has launched 4 new testing environments. These benchmarks focus on measuring Planning, Task Decomposition, and Function Calling capabilities, which are essential requirements for agent workflows.
The key testing environments are as follows:
- Typewriter (Single/26 tools): Typing out words by calling tools sequentially
- Relational Data: Answering questions based on information from 3 tables
- Multiverse Math: Solving problems with tools by applying altered math rules
According to the benchmark results, all models showed a tendency for failure probability to increase as task steps grew longer. GPT-4 recorded the highest score on the relational data task, but on Multiverse Math, an 'inverse scaling' phenomenon was observed where it performed worse than GPT-3.5 due to pre-trained bias.
Also, Claude-2.1 showed performance similar to GPT-4 on most tasks but lagged somewhat on the relational data task. The open-source Mistral 7b variant model was found to be accurate on single calls, but struggled with composing 2 or more functions together.
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