๐ Predictive Benchmark Runner
February 8, 2026 ยท View on GitHub
This directory contains the LLM-based evaluation runner. It consumes ground-truth group files (generated by the data-prep pipeline), prompts an LLM to rank solutions, and produces per-task and cross-task grading reports.
๐ง Functionality
- ๐ฅ Loads ground-truth comparison groups (e.g.,
groups_<task>_n2.json). - ๐ Constructs Prompts using task descriptions and data analysis reports.
- ๐ง Queries LLMs to predict the better solution (pairwise) or rank multiple solutions.
- ๐ Computes Metrics: Accuracy (for n=2), Spearman Correlation & Precision@k (for n>2).
- ๐พ Saves Reports including full interaction logs and machine-readable JSONs.
Entrypoint: run_bench.py
๐ Quick Start
python -m skip_bench.grade.run_bench \
--task-file /path/to/task_name.txt \
--solutions-dir /path/to/solutions_root \
--n 2 \
--model deepseek-v3.2-thinking \
--temperature 1.0 \
--parallel 64 \
--task-parallel 8 \
--prompt-boost \
--desc-dir /path/to/description \
--da-dir /path/to/data_analysis/result \
--cot
๐ Expected Inputs
-
๐ Task List (
--task-file)- A text file with one task name per line.
-
๐๏ธ Solutions Root (
--solutions-dir)- Structure:
<solutions_dir>/<task>/ground_truth/groups_<task>_n*.json
- Structure:
-
๐ Task Resources (Optional but Recommended)
- Mode 1 (Root):
--tasks-rootcontainingdescription/anddata_analysis/. - Mode 2 (Explicit):
--desc-dirfor descriptions and--da-dirfor analysis. - Mode 3 (Raw):
--raw-data-samplefor raw data snippets.
- Mode 1 (Root):
-
๐ฏ Ground Truth Override
--groundtruth-file: Point to a specific JSON file (supports{task}placeholder).
โ๏ธ Key Arguments
| Category | Flag | Description |
|---|---|---|
| Input | --task-file | Path to task list. |
--solutions-dir | Root for solutions & ground truth. | |
--n | Group size (e.g., 2 for pairwise). 0 for all. | |
| Model | --model | Model name (e.g., gpt-4). |
--temperature | Sampling temperature. | |
| Execution | --parallel | Concurrency per task. |
--task-parallel | Number of tasks to run in parallel. | |
| Prompting | --prompt-boost | Enhances instruction emphasis. |
--cot | Enaables Chain-of-Thought reasoning. | |
| Output | --partial-json | Save checkpoints (default: on). |
๐ค Outputs
Artifacts are written to <solutions_dir>/<task>/report/:
- ๐ Human-Readable Report:
grade_report_<task>_... .txt(Metrics + Logs) - ๐ค Alignment JSON:
alignment_<task>_... .json(Structured predictions) - ๐พ Partial Checkpoint:
grade_results_partial.json - ๐ Global Report:
<solutions_dir>/report/grade_report_alltasks_*.txt
Notes
- Group files are created by the data-prep pipeline (see the README in
prepare_bench_subset). - If you pass
--groundtruth-file, you must set--nto a non-zero value. - When using relative paths in
group_entry.paths, keep--solutions-dirset so the runner can resolve files correctly. - The command examples above may not list all tunable flags. For a complete reference, run each script with
--helpor inspect the corresponding Python file and its argparse comments.