Sh Bench Eval

---
name: sh-bench-eval
description: Evaluates audio LLMs' ability to comprehend multi-speaker conversations while selectively focusing on a target speaker and ignoring bystanders for privacy. It measures both general audio understanding and selective hearing capability under different instruction modes. Use when the user wants to benchmark on SH-Bench, or asks about evaluating this task. Reports Selective Efficacy (SE).
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2512.06380
  bibtex_key: zhan2025protecting
  confidence: high
---

# sh-bench-eval

> Protecting Bystander Privacy via Selective Hearing in Audio LLMs — Xiao Zhan et al. (2025) (arXiv:2512.06380, 2025)

## What this evaluates

Evaluates audio LLMs' ability to comprehend multi-speaker conversations while selectively focusing on a target speaker and ignoring bystanders for privacy. It measures both general audio understanding and selective hearing capability under different instruction modes.

## Datasets

- **SH-Bench** — total ?; splits: test (-1); repo https://github.com/Elocinacademia/SelectiveHearing-Bench.git

## Metrics

- `Selective Efficacy (SE)` **(primary)** — range: [0, 1]
  - Harmonic mean of four accuracy scores: general/main, general/bystander, selective/main, and selective/bystander. Formula: SE = 4 / (Acc_gen_main^-1 + Acc_gen_by^-1 + Acc_sel_main^-1 + Acc_sel_by^-1). High SE requires strong performance across all conditions.
- `Accuracy` — range: [0, 1]
  - Standard classification accuracy calculated separately for each combination of mode (general or selective) and target (main speaker or bystander). In selective mode, the 'I don't know' option is treated as the correct label for bystander questions.

## Input / output format

**Input**: Multi-speaker audio recording (foreground main speaker + background bystander) paired with a 5-way multiple-choice question. Evaluation prompts specify either 'General mode' (answer all questions) or 'Selective mode' (focus only on main speaker, mark bystander questions as 'I don't know').

**Output**: Model selects one of five options: four specific answer choices or an 'I don't know' option.

## Scoring recipe

```python
# Split predictions and gold labels into 4 subsets by mode (gen/sel) and target (main/by)
acc_gen_main = accuracy(pred_gen_main, gold_gen_main)
acc_gen_by = accuracy(pred_gen_by, gold_gen_by)
acc_sel_main = accuracy(pred_sel_main, gold_sel_main)
acc_sel_by = accuracy(pred_sel_by, gold_sel_by)
# Compute harmonic mean for Selective Efficacy
se = 4 / (1/acc_gen_main + 1/acc_gen_by + 1/acc_sel_main + 1/acc_sel_by)
return se
```

## Common pitfalls

- Treating 'I don't know' as incorrect for bystander questions in selective mode, which artificially deflates selective accuracy.
- Failing to separate general and selective mode evaluations, as the instruction prompts and correct answer keys differ significantly between modes.
- Not verifying that models can correctly identify the number of speakers before evaluation, as instructed in the screening process.

## Evidence (verbatim from paper)

> SH-Bench evaluation questions use a 5-way classification format, including an "I don't know" option (or equivalent). With these questions, we evaluate the models under two different modes: i) General mode, where we instruct the model to answer the question given by the audio, and the model is expected to answer all questions correctly regardless of whether they concern the main speaker or the bystander; and ii) Selective mode, where we instruct the model that there is a main speaker in the foreground and provide a content-conditioned description of this speaker... The model is also instructed to only listen to the main speaker and ignore any background speech, so it should choose the IDK option for the questions related to what the bystander said, and should give correct answer for the main speaker... Therefore, accuracies measured for bystander under selective mode should treat the "I don't know" option as the correct choice. In addition to accuracies, we define Selective Efficacy as a unified metric using the harmonic mean of 4 different accuracies on the main speaker and bystanders under general or selective modes as follows. SE = 4 / sum_{m in {gen, sel}} sum_{n in {main, by}} 

## Citation

```bibtex
@misc{zhan2025protecting,
  title={Protecting Bystander Privacy via Selective Hearing in Audio LLMs},
  author={Xiao Zhan et al. (2025)},
  year={2025},
  note={arXiv:2512.06380}
}
```

- arXiv: 2512.06380