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Small Models, Big Tasks: An Exploratory Empirical Study on Small Language Models for Function Calling
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“While early paradigms successfully demonstrated the feasibility of tool invocation, their reliance on SFT or prompt engineering limited agents to mimicking static patterns, lacking the strategic flexibility to adapt to novel scenarios or recover from errors (Chen et al., 2024g;Kavathekar et al., 2025).”
From The landscape of agentic reinforcement learning for LLMs: a survey (2025) · cites this paper as Kavathekar et al., 2025
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LoRA: Low-Rank Adaptation of Large Language Models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., & Chen, W. (2021) · arXiv · doi:10.48550/arxiv.2106.09685 · Cited by 13,453
The Power of Scale for Parameter-Efficient Prompt Tuning
Lester, B., Al-Rfou, R., & Constant, N. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.243 · Cited by 3,096 · cited 1×
“To address this, 1 https://github.com/Raghav010/Small-Models-Big-Tasks techniques like LoRA [22], prompt tuning [27] and prefix tuning [28] are employed.”
A Survey of Large Language Models
Zhao, W. X., Zhou, K., Li, J., Tang, T., Dong, Z., Hou, Y., Zhang, B., Min, Y., Zhang, J., Liu, P., et al. (2026) · Frontiers of Computer Science · doi:10.1007/s11704-026-60308-3 · Cited by 2,093
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Ahmad, W., Chakraborty, S., Ray, B., & Chang, K.-W. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.211 · Cited by 642
ReAct: Synergizing Reasoning and Acting in Language Models
Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2022) · arXiv · doi:10.48550/arxiv.2210.03629 · Cited by 608
A Survey on Model Compression for Large Language Models
Zhu, X., Li, J., Liu, Y., Ma, C., & Wang, W. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00704 · Cited by 273
Gorilla: Large Language Model Connected with Massive APIs
Patil, S., Zhang, T., Wang, X., & Gonzalez, J. (2024) · Advances in Neural Information Processing Systems 37 · doi:10.52202/079017-4020 · Cited by 226
DebugBench: Evaluating Debugging Capability of Large Language Models
Tian, R., Ye, Y., Qin, Y., Cong, X., Lin, Y., Pan, Y., Wu, Y., Haotian, H., Weichuan, L., Liu, Z., et al. (2024) · Findings of the Association for Computational Linguistics ACL 2024 · doi:10.18653/v1/2024.findings-acl.247 · Cited by 54
A New Era in LLM Security: Exploring Security Concerns in Real-World LLM-based Systems
Wu, F., Zhang, N., Jha, S., McDaniel, P., & Xiao, C. (2024) · arXiv · doi:10.48550/arxiv.2402.18649 · Cited by 23
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Cited by
The landscape of agentic reinforcement learning for LLMs: a survey
Zhang, G., Geng, H., Yu, X., Yin, Z., Zhang, Z., Tan, Z., Zhou, H., Li, Z., Xue, X., Li, Y., et al. (2025) · arXiv · doi:10.48550/arxiv.2509.02547 · Cited by 229
Metadata
- Authors
- Kavathekar, I., Donakanti, R., Kumaraguru, P., & Vaidhyanathan, K.
- Affiliations
- International Institute of Information Technology, Hyderabad, India
- Venue
- Proceedings of the 29th International Conference on Evaluation and Assessment in Software Engineering
- Publisher
- ACM
- Published
- 2025-06-17
- Pages
- 1117-1126
- Cited by
- 6
- References
- 11
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