1 citations · 2 across the 9 of their papers we have counts for
4 papers · 1 filter
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…
Take the essence and discard the dross: A Rethinking on Data Selection for Fine-Tuning Large Language Models
Ziche Liu, Rui Ke, Yajiao Liu +2
Data selection for fine-tuning large language models (LLMs) aims to choose a high-quality subset from existing datasets, allowing the trained model to outperform baselines trained…
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…
Unsupervised Mutual Learning of Discourse Parsing and Topic Segmentation in Dialogue
Jiahui Xu, Feng Jiang, Anningzhe Gao +2
In dialogue systems, discourse plays a crucial role in managing conversational focus and coordinating interactions. It consists of two key structures: rhetorical structure and topi…