Lab-MSP/comparative-reasoning-traces

Comparative Reasoning Traces for Preference-based SER

Reasoning traces used to train the SFT-CoT and DPO-CoT models of Comparative Reasoning: Making an Audio Language Model Better at Comparing Emotions (Interspeech 2026).

Models: SFT · SFT-CoT · DPO · DPO-CoT

Each example is an MSP-Podcast v2.0 training pair (10k pairs per attribute) with

• a chosen trace that explains why the clip with the higher attribute value is the answer, and
• a rejected trace that argues for the wrong clip (used as the rejected response in DPO-CoT).

The traces were generated by Qwen/Qwen3-Next-80B-A3B-Thinking-FP8 from Qwen3-Omni-Captioner audio captions and GeMAPS acoustic features (discretized into low / medium / high) of both clips, and kept only if their final answer was consistent with the label.

The audio is not included. Obtain MSP-Podcast (release 2.0) from the MSP Lab under its license.

Fields

Field	Description
key	<sen1 stem>_<sen2 stem>
emotion	arousal, valence, or dominance
sen1, sen2	MSP-Podcast file names of Clip 1 and Clip 2
preference	1 if Clip 1 has the higher attribute value, 0 if Clip 2 does
question	Question asked to the audio LM, after the two clips (Clip 1 = sen1, Clip 2 = sen2)
answer	Clip 1 or Clip 2 (ground truth)
reasoning	Chosen reasoning trace
reasoning_source	blind: the reasoning LLM was not given the answer and reached the correct one; guided: regenerated with the ground-truth answer given, for pairs the blind mode got wrong
rejected_answer	The wrong answer
rejected_reasoning	Reasoning trace generated with the wrong answer given (null for 5 pairs where verification failed)

Training format

The audio LM gets a system prompt, the two clips, and question:

system: You are a helpful assistant for emotion comparative reasoning.
user:   <audio: sen1> <audio: sen2> {question}

The training response is <think> {reasoning} </think> <answer> {answer} </answer> (SFT-CoT, DPO-CoT chosen), and <think> {rejected_reasoning} </think> <answer> {rejected_answer} </answer> for the DPO-CoT rejected response.

Usage

from datasets import load_dataset

traces = load_dataset("Lab-MSP/comparative-reasoning-traces", "arousal", split="train")

To build the ms-swift training data, download data/*.jsonl into a folder and run data/post_process/make_swift_data.py --traces_dir <folder> from the code repository.

Citation

@inproceedings{naini2026comparative,
  title     = {Comparative Reasoning: Making an Audio Language Model Better at Comparing Emotions},
  author    = {Naini, Abinay Reddy and Kim, Jaeyeon and Yang, Chao-Han Huck and Watanabe, Shinji and Busso, Carlos},
  booktitle = {Interspeech},
  year      = {2026}
}