| --- |
| license: mit |
| language: |
| - en |
| task_categories: |
| - question-answering |
| tags: |
| - finance |
| - tool-calling |
| - function-calling |
| - agent |
| - trajectory |
| - llm-evaluation |
| size_categories: |
| - n<1K |
| --- |
| |
| # FinTrace |
|
|
| **FinTrace** is a benchmark for evaluating LLM tool-calling (function-calling) |
| agents on financial queries, built on the Financial Modeling Prep (FMP) MCP |
| toolset. It contains expert-curated multi-turn tool-calling trajectories |
| across 30+ financial task categories, evaluated with a nine-metric rubric |
| spanning action correctness, execution efficiency, process quality, and |
| output quality. |
|
|
| Paper: *FinTrace* (COLM 2026) — link coming soon. |
|
|
| ## Repository structure |
|
|
| | Path | Description | Status | |
| |---|---|---| |
| | `evaluation/testset.json` | 800-query evaluation set with output and golden trajectories | ✅ available | |
| | `trajectories/` | model-generated trajectories, one folder per model | 🔜 coming soon | |
| | `training/` | FinTrace-Training SFT / DPO preference data | 🔜 coming soon | |
|
|
| ## Evaluation set (`evaluation/testset.json`) |
|
|
| 800 entries. Each entry pairs a model **output trajectory** with a **golden |
| reference trajectory** (the best of three frontier-model runs, selected by an |
| LLM judge) for the same query: |
|
|
| | Field | Description | |
| |---|---| |
| | `id` | unique query id | |
| | `source_query` | the financial question | |
| | `task_type` / `task_type_bucket` | task category (32 types / 12 buckets) | |
| | `resource` / `data_source` | origin of the query | |
| | `difficulty_score` / `difficulty_tier` | difficulty annotation | |
| | `traj_len_bin` | trajectory length bin | |
| | `endpoints_called` | FMP endpoints invoked in the output trajectory | |
| | `reference_answer` | ground-truth answer | |
| | `output_answer` | final answer of the output trajectory | |
| | `reasoning` | reasoning summary of the output trajectory | |
| | `output_trajectory` | full multi-turn message list (reasoning, tool calls, tool responses) | |
| | `golden_trajectories` | golden reference trajectory for the same query | |
|
|
| ## Usage |
|
|
| The trajectories are deeply nested, so we recommend downloading the raw JSON |
| directly rather than `load_dataset`: |
|
|
| ```python |
| import json |
| from huggingface_hub import hf_hub_download |
| |
| path = hf_hub_download( |
| "YupengCao/FinTrace", "evaluation/testset.json", repo_type="dataset" |
| ) |
| data = json.load(open(path)) |
| print(len(data), data[0]["source_query"]) |
| ``` |
|
|
| ## Citation |
|
|
| Citation entry coming with the camera-ready release. |
|
|
