Lab-MSP/comparative-reasoning-sft-cot

Comparative Reasoning — SFT-CoT (Qwen2.5-Omni-3B LoRA)

The SFT-CoT model from Comparative Reasoning: Making an Audio Language Model Better at Comparing Emotions (Interspeech 2026). Given two utterances, it answers which one has higher arousal, valence, or dominance and explains why.

This repository contains a LoRA adapter (rank 64, alpha 128, all linear layers) for Qwen/Qwen2.5-Omni-3B.

Training	SFT, lr 1e-4, total batch size 32
Training data	10k MSP-Podcast v2.0 preference pairs per attribute (30k total), target: reasoning trace + answer
MSP-Podcast test (A / V / D / Avg)	85.5 / 86.5 / 84.6 / 85.5
BIIC-Podcast / WHiSER (Avg)	74.1 / 85.4

Usage

Merge the adapter and evaluate with the code repository:

swift export --adapters Lab-MSP/comparative-reasoning-sft-cot --merge_lora true \
  --model Qwen/Qwen2.5-Omni-3B --output_dir outputs/experiments/sft-cot_merged
bash src/eval.sh msp_test outputs/experiments/sft-cot_merged

Prompt format (two audios followed by the question):

system: You are a helpful assistant for emotion comparative reasoning.
user:   <audio><audio>You will hear two audio clips. Clip 1 is the first audio clip. Clip 2 is the second audio clip. <attribute definition> Which clip has higher <attribute>?

The model answers <think> ... </think> <answer> Clip 1 </answer> or ... Clip 2 ....

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}
}