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Super-resolution Fluorescence Microscopy

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Related Experiment Video

Updated: Sep 13, 2025

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LLM-Enhanced Chinese Morph Resolution in E-Commerce Live Streaming Scenarios.

Xiaoye Ouyang¹, Liu Yuan¹, Xiaocheng Hu¹

¹China Academy of Electronic and Information Technology, Beijing 100041, China.

Entropy (Basel, Switzerland)

|July 29, 2025

Summary

This summary is machine-generated.

This study introduces a new method for resolving morphed speech in e-commerce live streams, improving accuracy in detecting misleading claims. The approach uses large language models to enhance speech transcription for better consumer protection.

Keywords:

Large Language Models e-commerce live streaming morph resolution

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Area of Science:

Natural Language Processing
Speech Recognition
E-commerce Technology

Background:

E-commerce live streaming in China is a significant retail channel.
Hosts may use subtle speech "morphs" to evade moderation and make unsubstantiated claims, posing consumer risks.

Purpose of the Study:

To introduce and evaluate the Live Auditory Morph Resolution (LiveAMR) task.
To develop a framework for restoring morphed speech transcriptions to their true forms.

Main Methods:

Propose an LLM-enhanced training framework leveraging explanation knowledge (morph-type labels, LLM corrections, rationales).
Mine knowledge from a frozen large language model without fine-tuning it.
Fine-tune a lightweight T5 model using concatenated annotations and original sentences.

Main Results:

Achieve substantial gains over baselines on both in-domain and out-of-domain test sets.
Improve F0.5 score by up to 7 pp in-domain (to 0.943) and 5 pp out-of-domain (to 0.799) compared to a T5 baseline.
Demonstrate efficient and accurate morph resolution using structured LLM-derived signals.

Conclusions:

Structured LLM-derived signals can be effectively mined and injected into smaller models.
The proposed method offers an efficient and accurate solution for auditory morph resolution in e-commerce live streams.
This approach enhances consumer protection by improving the detection of misleading claims.