arXiv AI
Beyond Mode Collapse: Generating Diverse Synthetic Expert Conversations via Generative Flow Networks
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arXiv AI
Jun 17
R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model?
arXiv:2602. 03300v2 Announce Type: replace-cross Abstract: In this work, we aim to develop effective data synthesis techniques that autonomously synthesize multimodal training data for enhancing MLLMs in solving complex real-world tasks.
arXiv AI
Jun 24
FlowPipe: LLM-Enhanced Conditional Generative Flow Networks for Data Preparation Pipeline Construction
arXiv:2606. 24679v1 Announce Type: cross Abstract: Data preparation pipelines improve data quality in machine learning by transforming raw tables into learning-ready data through sequential cleaning and feature transformation operators.
arXiv AI
Sep 1
Semantic Flow Regularization: Teaching LLMs to Generate Diverse Yet Coherent Responses
arXiv:2605.27971v2 Announce Type: replace-cross Abstract: When large language models are fine-tuned to generate persona- or tone-conditioned responses, their output diversity is severely limited--a f...
arXiv Machine Learning
Jun 19
The Significance of Style Diversity in Annotation-Free Synthetic Data Generation
arXiv:2606. 20400v1 Announce Type: new Abstract: Generating high-utility synthetic data for intent classification typically requires human-annotated seed data, which is often unavailable in fast-paced industrial settings.
arXiv AI
Jul 1
Learning by Surprise: Adaptive Mitigation of Model Collapse in Large Language Models
arXiv:2410. 12341v4 Announce Type: replace-cross Abstract: As AI-generated content increasingly populates the web, generative AI models are at growing risk of being trained on their own outputs, a process known as AI autophagy.
arXiv Computation and Language
Sep 28
Learning Natural Conversational Behavior in Tandem Speech-to-Speech Models with Randomized Guidance
The paper introduces a method called randomized intermediate guidance for training tandem speech-to-speech models, where a large language model (LLM) acts as a backend providing candidate responses while the user is speaking. Instead of simulating the backend’s guidance, the approach derives guidance directly from the conversation corpus, using target responses for informative guidance and randomly sampled responses to simulate irrelevant updates. Experiments on synthetic dialogues and 3.8k hours of real conversations show that this technique yields response quality comparable to LLM-generated baselines while improving natural turn‑taking and audio‑judge naturalness.