What Affects the Stability of Tool Learning? An Empirical Study on the Robustness of Tool Learning Frameworks
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916310536945664 |
|---|---|
| author | Huang, Chengrui Shi, Zhengliang Wen, Yuntao Chen, Xiuying Han, Peng Gao, Shen Shang, Shuo |
| author_facet | Huang, Chengrui Shi, Zhengliang Wen, Yuntao Chen, Xiuying Han, Peng Gao, Shen Shang, Shuo |
| contents | Tool learning methods have enhanced the ability of large language models (LLMs) to interact with real-world applications. Many existing works fine-tune LLMs or design prompts to enable LLMs to select appropriate tools and correctly invoke them to meet user requirements. However, it is observed in previous works that the performance of tool learning varies from tasks, datasets, training settings, and algorithms. Without understanding the impact of these factors, it can lead to inconsistent results, inefficient model deployment, and suboptimal tool utilization, ultimately hindering the practical integration and scalability of LLMs in real-world scenarios. Therefore, in this paper, we explore the impact of both internal and external factors on the performance of tool learning frameworks. Through extensive experiments on two benchmark datasets, we find several insightful conclusions for future work, including the observation that LLMs can benefit significantly from increased trial and exploration. We believe our empirical study provides a new perspective for future tool learning research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_03007 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | What Affects the Stability of Tool Learning? An Empirical Study on the Robustness of Tool Learning Frameworks Huang, Chengrui Shi, Zhengliang Wen, Yuntao Chen, Xiuying Han, Peng Gao, Shen Shang, Shuo Computation and Language Artificial Intelligence Tool learning methods have enhanced the ability of large language models (LLMs) to interact with real-world applications. Many existing works fine-tune LLMs or design prompts to enable LLMs to select appropriate tools and correctly invoke them to meet user requirements. However, it is observed in previous works that the performance of tool learning varies from tasks, datasets, training settings, and algorithms. Without understanding the impact of these factors, it can lead to inconsistent results, inefficient model deployment, and suboptimal tool utilization, ultimately hindering the practical integration and scalability of LLMs in real-world scenarios. Therefore, in this paper, we explore the impact of both internal and external factors on the performance of tool learning frameworks. Through extensive experiments on two benchmark datasets, we find several insightful conclusions for future work, including the observation that LLMs can benefit significantly from increased trial and exploration. We believe our empirical study provides a new perspective for future tool learning research. |
| title | What Affects the Stability of Tool Learning? An Empirical Study on the Robustness of Tool Learning Frameworks |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2407.03007 |