PodBench

A benchmark for instruction-aware, audio-oriented podcast script generation.

📃 Paper (ACL 2026)  •  💻 Code & evaluation toolkit

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

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:

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. Results reported in the paper use anthropic/claude-opus-4.5 as the judge.

Citation

@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"
}