1 citations · 1 across the 8 of their papers we have counts for
8 papers
Emo-LiPO: Listwise Preference Optimization for Fine-Grained Emotion Intensity Control in LLM-based Text-to-Speech
Yihang Lin, Li Zhou, Congwei Cao +4
Large language model (LLM)-based text-to-speech (TTS) systems enable prompt-conditioned emotional control but struggle with fine-grained emotion intensity due to the semantic -- ac…
AudioRAG: A Challenging Benchmark for Audio Reasoning and Information Retrieval
Jingru Lin, Chen Zhang, Tianrui Wang +1
Due to recent advancements in Large Audio-Language Models (LALMs) that demonstrate remarkable performance across a range of sound-, speech- and music-related tasks, there is a grow…
RAGCap-Bench: Benchmarking Capabilities of LLMs in Agentic Retrieval Augmented Generation Systems
Jingru Lin, Chen Zhang, Stephen Y. Liu +1
Retrieval-Augmented Generation (RAG) mitigates key limitations of Large Language Models (LLMs)-such as factual errors, outdated knowledge, and hallucinations-by dynamically retriev…
Aligning Language Models Using Follow-up Likelihood as Reward Signal
Chen Zhang, Dading Chong, Feng Jiang +4
In natural human-to-human conversations, participants often receive feedback signals from one another based on their follow-up reactions. These reactions can include verbal respons…
DynaThink: Fast or Slow? A Dynamic Decision-Making Framework for Large Language Models
Jiabao Pan, Yan Zhang, Chen Zhang +3
Large language models (LLMs) have demonstrated emergent capabilities across diverse reasoning tasks via popular Chains-of-Thought (COT) prompting. However, such a simple and fast C…
RefXVC: Cross-Lingual Voice Conversion with Enhanced Reference Leveraging
Mingyang Zhang, Yi Zhou, Yi Ren +3
This paper proposes RefXVC, a method for cross-lingual voice conversion (XVC) that leverages reference information to improve conversion performance. Previous XVC works generally t…
TS-Align: A Teacher-Student Collaborative Framework for Scalable Iterative Finetuning of Large Language Models
Chen Zhang, Chengguang Tang, Dading Chong +4
Mainstream approaches to aligning large language models (LLMs) heavily rely on human preference data, particularly when models require periodic updates. The standard process for it…
Unveiling the Achilles' Heel of NLG Evaluators: A Unified Adversarial Framework Driven by Large Language Models
Yiming Chen, Chen Zhang, Danqing Luo +3
The automatic evaluation of natural language generation (NLG) systems presents a long-lasting challenge. Recent studies have highlighted various neural metrics that align well with…