1 citations · 1 across the 6 of their papers we have counts for
4 papers · 1 filter
Efficient Response Generation Strategy Selection for Fine-Tuning Large Language Models Through Self-Aligned Perplexity
Xuan Ren, Qi Chen, Lingqiao Liu
Fine-tuning large language models (LLMs) typically relies on producing large sets of input-output pairs. Yet for a given question, there can be many valid outputs. In practice, the…
I Learn Better If You Speak My Language: Understanding the Superior Performance of Fine-Tuning Large Language Models with LLM-Generated Responses
Xuan Ren, Biao Wu, Lingqiao Liu
This paper explores an intriguing observation: fine-tuning a large language model (LLM) with responses generated by a LLM often yields better results than using responses generated…
You Can Generate It Again: Data-to-Text Generation with Verification and Correction Prompting
Xuan Ren, Zeyu Zhang, Lingqiao Liu
Small language models like T5 excel in generating high-quality text for data-to-text tasks, offering adaptability and cost-efficiency compared to Large Language Models (LLMs). Howe…
Out-of-Distribution Generalization in Text Classification: Past, Present, and Future
Linyi Yang, Yaoxiao Song, Xuan Ren +6
Machine learning (ML) systems in natural language processing (NLP) face significant challenges in generalizing to out-of-distribution (OOD) data, where the test distribution differ…