reviews: 
Comments: This paper introduces a "tool unlearning" method for tool-augmented large language models (LLMs), which effectively removes the capability to use specific tools without compromising the model's overall abilities. The study presents the TOOLDELETE algorithm, which successfully erases targeted tool knowledge while retaining the model’s performance in using other tools and general tasks. The LiRA-Tool evaluation method confirms the effectiveness of this approach. This technique is significant for meeting data privacy and compliance requirements.
Strengths: * Propose the novel task tool unlearning and describe the difference between this and traditional sample-level unlearning.

* Make concerns about unlearning knowledge of targeted tools, retaining knowledge of other tools and general ability of LLMs.

* Main experiments show good performance in unlearning and retaining knowledge of the proposed method.
Weaknesses: * Clear real-world applications cannot be found. This paper provides a real-world harmful case: unlearning tool-augmented LLM with knowledge of insecure HTTP requests. However, it is hard to find the applications that need the proposed tool unlearning. The authors should clearly demonstrate the practice of the proposed approach in real-world applications. Firstly, this case does not match the authors' claim of "preserving its ability to use others tools and perform general tasks". Secondly, as described in the paper, LLMs retain "harmful knowledge" from tools, and this knowledge may be unlearned through a traditional unlearning process(constructing a dataset of question-answer pairs). The author must prove why we need tool unlearning rather than traditional unlearning in open-world cases. 

* Lack of description of loss function design and mistakes found in section 4.4. As shown in the authors' objective function, the design of the function '$g(f, t)$' is of great importance and directly decides whether the tool unlearning can succeed or not. However, the authors do not report details of the function $g$. This makes it hard to evaluate the effectiveness of the method. Thus, in Eq. (5), $[g(f_0, t_i) − g(f′, t_i)]$ and $[g(f, t_m) − g(f′, t_m)]$, as far as I'm concerned, lead to a certain score, while $\alpha(\theta_0 − \theta_R)$ leads to a vector. These cannot be added together. The authors should elaborate more on each term of the loss function. 

* The author claims that their method can keep the knowledge from other tools after the tool unlearning process. However, they do not conduct experiments on this. I would like the authors to conduct more experiments on an LLM with two or more tools' augmentation to prove the claimed benefits.

* Further suggestions: more mainstream datasets and benchmarks can be utilized in the experiment, e.g., [1], [2].

* Typos: "preservemode's" in line 215 on page 5. 

[1] Li, Minghao, et al. "Api-bank: A comprehensive benchmark for tool-augmented LLMs." arXiv preprint arXiv:2304.08244 (2023).
[2] Zhuang, Yuchen, et al. "Toolqa: A dataset for LLM question answering with external tools." Advances in Neural Information Processing Systems 36 (2023): 50117-50143.
Recommendation: 6
Reviewer confidence: 3
Soundness/correctness: 3
Clarity: 3