The Calls are Coming from Inside the Model: Investigating Probe-based Detection of Tool-Calling Errors in LLMs
Linear probes applied to LLM hidden states can effectively detect a range of tool-calling errors, including subtle type-correct-but-value-incorrect mistakes that standard logging frameworks miss Probe efficacy was evaluated across 18 tool-calling LLMs on the Berkeley Function Calling Leaderboard, revealing that model size, probing layer, and post-training type are critical success factors Probes demonstrate the ability to generalize to novel error types, a capability essential for real-world dep
Analysis
TL;DR
- Linear probes applied to LLM hidden states can effectively detect a range of tool-calling errors, including subtle type-correct-but-value-incorrect mistakes that standard logging frameworks miss
- Probe efficacy was evaluated across 18 tool-calling LLMs on the Berkeley Function Calling Leaderboard, revealing that model size, probing layer, and post-training type are critical success factors
- Probes demonstrate the ability to generalize to novel error types, a capability essential for real-world deployment safety
- Hidden states encode rich behavioral information about tool usage that is not accessible through input-output examination alone
- This work establishes probing as a practical internal monitoring mechanism for LLM-based systems interacting with external tools and APIs
Why It Matters
As LLMs are increasingly deployed in production systems that call external tools and APIs, detecting incorrect tool usage in real time is a critical safety and reliability concern. This research provides a lightweight, model-agnostic method for surfacing errors that would otherwise go unnoticed by conventional logging, enabling better monitoring and error recovery in production LLM pipelines.
Technical Details
- The study employs linear probes trained on hidden states from 18 different tool-calling LLMs to classify whether a tool call is correct or erroneous, evaluated on the Berkeley Function Calling Leaderboard
- Probes successfully detect not only overt failures but also subtle semantic errors, such as arguments with correct types but incorrect values, which standard validation and logging typically miss
- Key variables affecting probe performance include model size (larger models yield more probeable representations), the specific transformer layer chosen for probing, and the model's post-training methodology (e.g., SFT vs. RLHF)
- The probes generalize to previously unseen error types, suggesting that hidden state representations capture generalizable notions of tool-calling correctness rather than merely memorizing specific failure modes
Industry Insight
- Organizations deploying tool-using LLMs should consider integrating linear probes as a lightweight internal monitoring layer to catch subtle errors that external validation alone cannot detect
- Model selection for production tool-calling systems should account not only for benchmark accuracy but also for probe-ability of hidden states, as this determines the feasibility of internal error detection
- The generalization capability of probes to novel error types makes them a promising component for building robust, adaptive safety systems that can handle edge cases without continuous retraining
Disclaimer: The above content is generated by AI and is for reference only.