FinToolSyn: A forward synthesis Framework for Financial Tool-Use Dialogue Data with Dynamic Tool Retrieval (2026.findings-acl)
Copied to clipboard
Caishuang Huang, Yang Qiao, Rongyu Zhang, Junjie Ye, Pu Lu, null Wuwenxi, Meng Zhou, Xiku Du, Qi Zhang, Tao Gui, Xuanjing Huang
| Challenge: | Existing data synthesis methods rely on static tools to generate queries . this approach fails to capture the implicit, event-driven nature of real-world needs . |
| Approach: | They propose a forward synthesis framework to generate high-quality financial dialogues . they construct a repository of 43,066 tools and synthesize over 148k dialogue instances . |
| Outcome: | Experiments show that models trained on FinToolSyn achieve a 21.06% improvement . the framework is designed to generate high-quality financial dialogues . |
Similar Papers
Equipping Language Models with Tool Use Capability for Tabular Data Analysis in Finance (2024.eacl-short)
Copied to clipboard
| Challenge: | Large language models (LLMs) have an array of reasoning capabilities but face limitations such as error propagation and hallucination. |
| Approach: | They propose to use a LLAMA-2 13B CHAT model to act as a task router and task solver to offload certain reasoning steps to external tools that are more suited for the task. |
| Outcome: | The proposed model improves by 35.2% and 5.06% over baseline models and strong GPT-3.5 results. |
ToolFlow: Boosting LLM Tool-Calling Through Natural and Coherent Dialogue Synthesis (2025.naacl-long)
Copied to clipboard
Zezhong Wang, Xingshan Zeng, Weiwen Liu, Liangyou Li, Yasheng Wang, Lifeng Shang, Xin Jiang, Qun Liu, Kam-Fai Wong
| Challenge: | Large Language Models (LLMs) can be enhanced by using supervised fine-tuning . however, access to fine-timing data can be limited. |
| Approach: | They propose a Graph-based Sampling strategy and a Planned-generation strategy to enhance the coherence between dialogues by using 8,000 synthetic dialogues. |
| Outcome: | The proposed model achieves tool-calling performance comparable to or surpassing GPT-4 while maintaining strong general capabilities. |
Deriving Strategic Market Insights with Large Language Models: A Benchmark for Forward Counterfactual Generation (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for forward counterfactual generation face limitations . large language models (LLMs) offer promise but remain unexplored for this application . |
| Approach: | They propose a benchmark to support forward counterfactual generation in finance . they use financial news headlines to curate financial news and provide structured evaluation . |
| Outcome: | The proposed benchmark aims to provide scalable, automated insights into potential market opportunities and risks for stakeholders. |
CodeTool: Enhancing Programmatic Tool Invocation of LLMs via Process Supervision (2025.acl-long)
Copied to clipboard
YifeiLu YifeiLu, Fanghua Ye, Jian Li, Qiang Gao, Cheng Liu, Haibo Luo, Nan Du, Xiaolong Li, Feiliang Ren
| Challenge: | Existing approaches to tool invocation are often unnecessarily long and require lengthy reasoning paths. |
| Approach: | They propose a framework for stepwise code generation that improves LLM tool invocation . they incorporate two distinct process rewards: the On-the-spot and the Latent Reward . |
| Outcome: | The proposed framework improves LLM tool invocation by leveraging the concise nature of code. |
Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks (2023.emnlp-industry)
Copied to clipboard
| Challenge: | Recent large language models such as ChatGPT and GPT-4 have shown exceptional capabilities of generalist models . however, their applicability and effectiveness in specific domains like finance needs a better understanding . |
| Approach: | They conduct empirical studies to compare the performance of ChatGPT and GPT-4 on financial text analytical problems using eight benchmark datasets from five categories of tasks. |
| Outcome: | The proposed models outperform the state-of-the-art models on a wide range of financial text analytical tasks. |
GenTool: Enhancing Tool Generalization in Language Models through Zero-to-One and Weak-to-Strong Simulation (2025.findings-acl)
Copied to clipboard
Jie He, Jennifer Neville, Mengting Wan, Longqi Yang, Hui Liu, Xiaofeng Xu, Xia Song, Jeff Z. Pan, Pei Zhou
| Challenge: | Large Language Models (LLMs) can expand their capabilities by integrating external tools. |
| Approach: | They propose a training framework that prepares LLMs for diverse generalization challenges in tool utilization. |
| Outcome: | The proposed framework improves the tool-usage capabilities of LLMs by up to 8B parameters, surpassing GPT-4o. |
Toolscaler: Scalable Generative Tool Calling via Structure-Aware Semantic Tokenization (2025.findings-emnlp)
Copied to clipboard
Yunyue Su, Zhang Jinshuai, Bowen Fang, Wen Ye, Jinghao Zhang, Bowen Song, Weiqiang Wang, Qiang Liu, Liang Wang
| Challenge: | Extensive experiments demonstrate the effectiveness of SGTC across various tasks. |
| Approach: | They propose a generative tool invocation framework that introduces structure-aware semantic tokenization to encode tools as discrete code sequences. |
| Outcome: | The proposed framework reduces the size of the representation space and underutilizes collaborative signals among tools in downstream tasks. |
BioTool: A Comprehensive Tool-Calling Dataset for Enhancing Biomedical Capabilities of Large Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | despite the success of large language models, their performance in highly specialized domains remains unsatisfactory. |
| Approach: | They propose a biomedical tool-calling dataset designed for fine-tuning LLMs . the dataset contains 34 frequently used tools from the NCBI, Ensembl, and UniProt databases . |
| Outcome: | The proposed dataset outperforms commercial LLMs on biomedical domains. |
Towards Autonomous Tool Utilization in Language Models: A Unified, Efficient and Scalable Framework (2024.lrec-main)
Copied to clipboard
| Challenge: | Recent advances in tool learning for large language models have led to a new trend to allow LLMs to leverage external tools. |
| Approach: | They propose a framework for fine-tuning language models that categorizes queries into three different types . they also introduce an "instruct, execute, and reformat" strategy specifically designed for efficient data annotation . |
| Outcome: | The proposed framework surpasses open-source language models and GPT-3.5/4 on multiple evaluation metrics. |
Unlocking Implicit Experience: Synthesizing Tool-Use Trajectories from Text (2026.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) can be used to effectively utilize tools in multi-turn interactions, but acquiring diverse and realistic multi-step tool-use data remains a challenge. |
| Approach: | They propose a text-based data synthesis pipeline that generates multi-turn tool-use trajectories from text corpora using relevance filtering, workflow tool extraction, trajectory grounding, and complexity refinement. |
| Outcome: | The proposed model achieves 14.9% improvement on the BFCL V3 Multi-turn benchmark while significantly reducing inference latency and costs. |