| --- |
| license: mit |
| task_categories: |
| - text-generation |
| language: |
| - zh |
| - en |
| tags: |
| - benchmark |
| - podcast |
| - instruction-following |
| - long-context |
| - script-generation |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| data_files: |
| - split: test |
| path: data/podbench_800.jsonl |
| --- |
| |
| # PodBench |
|
|
| A benchmark for **instruction-aware, audio-oriented podcast script generation**. |
|
|
| 📃 [Paper (ACL 2026)](https://aclanthology.org/2026.acl-long.2019/) • |
| 💻 [Code & evaluation toolkit](https://github.com/xucncn/PodBench) |
|
|
| PodBench evaluates whether a model can turn real-world source material into a |
| podcast script that satisfies an explicit user brief. It contains **800 queries** |
| (400 Chinese, 400 English) with inputs up to **21K tokens**, spanning eight |
| requirement dimensions including multi-speaker instructions. |
|
|
| ## Quick Start |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("cnxu/PodBench", split="test") |
| |
| # `input_prompt` is the fully assembled model input. |
| print(ds[0]["input_prompt"]) |
| ``` |
|
|
| Generate a script for each `input_prompt`, then score the outputs with the |
| toolkit in the [GitHub repository](https://github.com/xucncn/PodBench): |
|
|
| ```bash |
| export OPENROUTER_API_KEY="sk-or-v1-your-key-here" |
| python evaluate_benchmark.py --model_file generations.jsonl --output_file ./results/my_model_eval.jsonl |
| python calculate_scores.py --results_dir ./results --detailed |
| ``` |
|
|
| ## Fields |
|
|
| | Field | Type | Description | |
| |---|---|---| |
| | `id` | string | Sample identifier | |
| | `instruction` | string | The user requirement under test | |
| | `input_prompt` | string | **Fully assembled model input** — feed this to your model | |
| | `meta` | string (JSON) | Everything else: source material, speaker spec, dimensions, languages, token counts, provenance | |
|
|
| ## Evaluation |
|
|
| Scripts are scored on two axes, averaged into the final metric: |
|
|
| 1. **Instruction Following (0–100)** — the judge derives a sample-specific |
| checklist from the instruction and verifies each item against the script. |
| 2. **Podcast Script Quality (0–100)** — an audio-first rubric covering Content |
| Substance (45), Narrative Engagement (30), and Conversational Naturalness (25). |
|
|
| Both rubrics and the scoring scripts are in the |
| [GitHub repository](https://github.com/xucncn/PodBench). Results reported in the |
| paper use `anthropic/claude-opus-4.5` as the judge. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{xu-etal-2026-podbench, |
| title = "{P}od{B}ench: A Comprehensive Benchmark for Instruction-Aware Audio-Oriented Podcast Script Generation", |
| author = "Xu, Chenning and |
| Zheng, Mao and |
| Zheng, Mingyu and |
| Song, Mingyang", |
| booktitle = "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", |
| month = jul, |
| year = "2026", |
| address = "San Diego, California, United States", |
| publisher = "Association for Computational Linguistics", |
| url = "https://aclanthology.org/2026.acl-long.2019/", |
| doi = "10.18653/v1/2026.acl-long.2019", |
| pages = "43605--43621", |
| ISBN = "979-8-89176-390-6" |
| } |
| ``` |
|
|
