Year: 2026
Type: conference-paper
Abstract: Large language models (LLMs) have evolved from pure text generators into interactive agents capable of invoking external tools.However, LLM agents still struggle with goal-oriented queries, which require decomposing high-level objectives into sequences of interdependent API calls with accurate planning and execution.Current approaches rely on zero-shot evaluation due to the absence of training data; while proprietary models such as GPT-4 exhibit strong reasoning capabilities, smaller opensource models remain ineffective at complex tool use.To address this limitation, we propose a novel training framework GOAT, that enables fine-tuning LLM agents without human annotation.GOAT automatically synthesizes goal-oriented API execution data from API documents using a novel call-first generation paradigm, that constructs training data based on executed API call sequences.Through extensive experiments, we show that GOATtrained agents achieve state-of-the-art performance across multiple existing goal-oriented benchmarks.In addition, we introduce GOAT-Bench, a new goal-oriented API execution benchmark, and demonstrate that agents trained with GOAT also excel in this setting.These results highlight GOAT as a practical path toward building robust open-source LLM agents capable of complex reasoning and tool use.
Authors: Hyunji Min, Sangwon Jung, Junyoung Sung, Dosung Lee, Leekyeung Han, Paul Hongsuck Seo
Institution: Korea University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: G7102533171, G7492658114, G8262039678
OpenAlex ID: W4415269766