Datasets:
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:
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.