Title: RoTBench: A Multi-Level Benchmark for Evaluating the Robustness of Large Language Models in Tool Learning
Benchmark: RoTBench
Summary: LLMs are increasingly deployedin settings where they can use tools, e.g. call functions to retrieve real-time information on weather. This paper proposes benchmark measuring the robustness of LLMs in selecting tools when these are specified under noise (e.g. the function name is perturbed).
Phenomenon type: General Capability (A broadly useful ability, which could be relevant to multiple applications)
Target phenomenon: tool use when tool names or arguments are mislabeled
Phenomenon defined by authors: No
Phenomenon definition: LLMs should exhibit consistent tool use when tools or their arguments are mislabeled.
Definition scope: Subset
Definition integrity: Composite phenomenon
Definition integrity detail: Yes
Phenomenon contested: Contested
Task item definition: Prompt + List of availabe tools + ground truth tool + ground truth arguments
Task source: Procedurally-generated task examples (e.g. Creating instances from a template)
Task source detail: existing benchmark + small perturbations
Task ecology: Constructed task (e.g. predicting medical diagnoses from clinicians' notes)
Dataset size: 735
Dataset metadata available: No
Sampling method: Random sample (creators defined a task space and sampled from it), Convenience sample (creators found a set of tasks that was readily accessible)
Split reviewed: Test, Train
Response format: Free response (e.g. summary paragarph), Structured response (e.g. valid JSON, API call alone)
Metric definition: Exact Match (accuracy, F1, precision, recall)
Metric aggregation: Simple Mean
Metric subscores: Yes
Metric subscores detail: different intermediate stages to a full sucess.
Metric access: Outputs alone
Authorship: Academia
Benchmark availability: Yes
Task face validity: Yes
Metric face validity: Yes
Results interpreted by authors: Yes
Results compared: No
Results comparison explanation: No comparisons made
Results realism: No
Human baseline: Yes
Authors discuss validity: No
Taxonomy: Agents / Tool Use
New taxonomy: General Application / Tool Use
Other notes: A) The noise induced in the benchmark significantly alters the *expected behaviour* of the model. For instance, imagine "Get_GPS_COORDINATES : This tool is used for fetching information weather for specified location." is a perturbation of "Get_WEATHER: This tool is used for fetching infromation weather for specified location." Clearly, the inconsistent information provided to the LLM between the function name and its docstring changes the expected behaviour of the model and hence "consistent" behaviour is not necessarily a sign of robustness. This casts doubt on the construct validity of “Robust Tool Use”. A positive note: The authors test human perofrmance and humans get scores between 69% and 89%, showing the task is still somewhat possible to humans.


B) The authors built their dataset by perturbing an existing dataset. their explanations of the existing dataset are negligle. It should be best practice to at least explain what the task of the original dataset is exactly, its size and limitations.