arXiv:2606.03657v1 Announce Type: new Abstract: Large language models for code generation often need to use APIs that are absent from their pretraining data. This requires more than recalling a function name: models must coordinate signatures, module paths, input-output contracts, semantics, and executable usage patterns. Existing novel-API benchmarks are typically static, rely on coarse pass/fail metrics, or use synthetic APIs that may not reflect real library evolution. We introduce NovelAPIBench, a fully automated dynamic benchmark that, for any base model and target library, discovers nove
The rapid development and deployment of LLMs necessitate robust benchmarks to assess and improve their ability to interact with novel, real-world APIs, which is crucial for practical application.
Improving LLMs' ability to acquire and use new APIs independently is a critical step towards more autonomous and versatile AI agents, impacting productivity and system design.
This new benchmark provides a more dynamic and realistic evaluation method for LLMs' tool-use capabilities, moving beyond static and synthetic tests.
- · AI model developers
- · Developers of custom APIs
- · Enterprise software companies
- · LLMs with poor generalization to new APIs
- · Static API benchmarking methods
Benchmarks like NovelAPIBench will accelerate the development of more capable and adaptable LLM-based AI agents.
Enhanced API acquisition will lead to LLMs seamlessly integrating with a broader range of software and services, expanding their utility.
The increased sophistication of LLM tool use could fuel a further wave of automation, creating new categories of agentic software.
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Read at arXiv cs.AI