@inproceedings{kumar-2026-meta,
    title = "Meta-Tool: Efficient Few-Shot Tool Adaptation for Small Language Models",
    author = "Kumar, Sachin",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.492/",
    pages = "10132--10147",
    ISBN = "979-8-89176-395-1",
    abstract = "Can small language models achieve strong tool-use performance without complex adaptation mechanisms? This paper investigates this question through Meta-Tool, a controlled empirical study comparing hypernetwork-based LoRA adaptation against carefully designed few-shot prompting. Using a Llama-3.2-3B-Instruct backbone, we evaluate four adaptation mechanisms{---}few-shot prompting, documentation encoding, hypernetwork-generated LoRA weights, and value-guided beam search{---}across four diverse benchmarks: Gorilla APIBench, Spider 2.0, WebArena, and InterCode. Our central finding is a well-supported negative result: despite generating non-trivial weight matrices, the 227.8M-parameter hypernetwork provides no measurable improvement over few-shot prompting alone. Comprehensive ablation studies reveal that few-shot examples contribute +21.5{\%} to performance and documentation contributes +5.0{\%}, while the hypernetwork adds 0{\%}. A 3B model with well-designed prompts achieves 79.7{\%} of GPT-5{'}s average performance at $10 \times$ lower latency. Error analysis across 722 failure cases spanning all shot counts (0{--}5) shows that at the 5-shot configuration (106 failures), failure modes are task-dependent: schema-heavy tasks (Spider 2.0, WebArena) show near-zero format errors with remaining failures semantic, while format errors dominate on Gorilla (100{\%}) and InterCode (70{\%}). These findings redirect practitioners toward prompt engineering and example curation rather than complex adaptation architectures."
}