Uncertainty Quantification for LLM Function-Calling
Apple ML Research
Researchers addressed uncertainty quantification for LLM function-calling, which enables large language models to use external tools autonomously. The study focuses on measuring model confidence before executing function calls that could have irreversible consequences like financial transfers or data deletion. Better uncertainty estimates allow systems to defer high-risk decisions to human operators or alternative fallback mechanisms.
Why it matters
Large Language Models (LLMs) are increasingly deployed to autonomously solve real-world tasks. A key ingredient for this is the LLM Function-Calling paradigm, a widely used approach for equipping LLMs with tool-use capabilities. However, an LLM calling functions incorrectly can have severe implications, especially when their effects are irreversible, e.g., transferring money or deleting data. Hence, it is of paramount importance to consider the LLM’s confidence that a function call solves the task correctly prior to executing it. Uncertainty Quantification (UQ) methods can be used to quantify…
