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+# HumDial-EIBench
+
+> A human-recorded multi-turn emotional intelligence benchmark for audio language models.
+
+<div align="center">
+
+[![arXiv](https://img.shields.io/badge/arXiv-Paper-COLOR.svg)](https://arxiv.org/pdf/2604.11594)  [![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Dataset-yellow)](https://huggingface.co/datasets/ASLP-lab/HumDial-EIBench)  [![GitHub](https://img.shields.io/badge/GitHub-Repo-green)](https://github.com/ASLP-lab/HumDial-EIBench)
+
+</div>
+
+<div align="center">
+  <img src="assets/humdial-bench.png" alt="HumDial-EIBench pipeline and task overview" width="90%">
+  <p><em>Figure: Three-stage data pipeline and the four evaluation tasks in HumDial-EIBench.</em></p>
+</div>
+
+HumDial-EIBench is designed to evaluate whether audio language models (ALMs) truly understand emotion in speech, rather than relying on text transcription shortcuts.
+
+The benchmark is built from authentic human-recorded dialogues from the ICASSP 2026 HumDial Challenge and includes both Chinese and English subsets.
+
 ---
-license: apache-2.0
+
+## Why HumDial-EIBench
+
+Existing ALM benchmarks often suffer from one or more of these issues:
+
+- synthetic (TTS-only) speech instead of authentic human recordings
+- single-turn settings that miss emotional evolution over context
+- subjective open-ended scoring that confounds reasoning and generation quality
+
+HumDial-EIBench addresses these gaps by combining:
+
+- **real human multi-turn audio**
+- **objective adversarial MCQ tasks for reasoning-heavy evaluation**
+- **a dedicated acoustic-semantic conflict task**
+- **separate diagnosis of textual empathy vs acoustic empathy**
+
 ---
+
+## Benchmark at a Glance
+
+- **Total samples:** 1,077
+- **Languages:** Chinese + English
+- **Core goal:** diagnose emotional intelligence in ALMs across memory, reasoning, generation, and cross-modal robustness
+
+| Task | Type | CN / EN | Turns | Main Metric |
+| :--- | :---: | :---: | :---: | :---: |
+| **Task 1: Emotional Trajectory Detection** | MCQ | 150 / 150 | 3-5 | Accuracy |
+| **Task 2: Implicit Causal Reasoning** | MCQ | 134 / 149 | 3-5 | Accuracy |
+| **Task 3: Empathetic Response Generation** | Open Generation | 144 / 150 | 3-5 | LLM + Human |
+| **Task 4: Acoustic-Semantic Conflict** | MCQ | 100 / 100 | 1 | Accuracy |
+| **Total** |  | **528 / 549** |  |  |
+
+---
+
+## Four Tasks
+
+### Task 1: Emotional Trajectory Detection
+Track emotion changes across dialogue turns (for example, `E_t1 -> E_t2 -> E_t3`), instead of classifying isolated utterances.
+
+### Task 2: Implicit Causal Reasoning
+Infer the latent emotional trigger from scattered context clues. The MCQ format helps reduce evaluator subjectivity.
+
+### Task 3: Empathetic Response Generation
+Evaluate generated responses in three dimensions:
+
+- **D1: Textual Empathy & Insight** (LLM-judge, 1-5)
+- **D2: Vocal Empathy & Congruence** (human rating, 1-5)
+- **D3: Audio Quality & Naturalness** (human rating, 1-5)
+
+### Task 4: Acoustic-Semantic Conflict
+Test robustness when text sentiment contradicts vocal affect (for example, sarcasm-like cases), exposing text-dominance bias.
+
+---
+
+## Key Findings
+
+- Most ALMs still struggle with **multi-turn emotional tracking** and **implicit causal reasoning**.
+- Strong **decoupling exists between textual empathy and acoustic empathy**.
+- All tested models show a notable **text-dominance bias** under acoustic-semantic conflict.
+
+---
+
+## Repository Structure
+
+```text
+.
+├── assets/
+│   └── humdial-bench.png
+├── eval/
+│   └── eval_task3.py
+└── README.md
+```
+
+---
+
+## Data and Code Access
+
+
+- The dataset is publicly available on HuggingFace: [HumDial-EIBench](https://huggingface.co/datasets/ASLP-lab/HumDial-EIBench)
+
+---
+
+## Evaluation Usage
+
+### Task 3 (Empathetic Generation) Scoring
+
+`eval/eval_task3.py` scores model outputs for D1/D2/D3 and writes per-sample + summary results.
+
+#### Input format (`jsonl`)
+
+```jsonc
+{
+  "dialogue_id": "sample_001",
+  "turns": [
+    {
+      "input_emotion": "sad",
+      "input_text": "I've been feeling really overwhelmed lately...",
+      "response_text": "It sounds like you're carrying a lot right now.",
+      "response_audio": "outputs/sample_001_turn1.wav"
+    }
+  ]
+}
+```
+
+#### Run
+
+```bash
+python eval/eval_task3.py \
+  --model Qwen3-Omni-30B-A3B-Instruct \
+  --input_file results/task3_outputs.jsonl \
+  --output_file results/task3_scores.jsonl
+```
+
+The script automatically identifies the target evaluation turn (second non-neutral turn) and builds context from prior turns.
+
+> Environment note: this script requires a GPU runtime and `vLLM`. Set the local judge checkpoint path in `eval/eval_task3.py` before running.
+
+---
+
+
+---
+
+## Citation
+
+If you find this work useful, please cite:
+
+```bibtex
+@misc{wang2026humdialeibenchhumanrecordedmultiturnemotional,
+      title={HumDial-EIBench: A Human-Recorded Multi-Turn Emotional Intelligence Benchmark for Audio Language Models}, 
+      author={Shuiyuan Wang and Zhixian Zhao and Hongfei Xue and Chengyou Wang and Shuai Wang and Hui Bu and Xin Xu and Lei Xie},
+      year={2026},
+      eprint={2604.11594},
+      archivePrefix={arXiv},
+      primaryClass={eess.AS},
+      url={https://arxiv.org/abs/2604.11594}, 
+}
+```
+
+## License
+
+The code in this repository is released under the **Apache 2.0 License**.
+
+## Contact
+
+- **Issues**: Please open a GitHub Issue for bug reports or suggestions.
+- **Email**: wangshuiyuan@mail.nwpu.edu.cn, lxie@nwpu.edu.cn
+
+