7 citations · 31 across the 35 of their papers we have counts for
15 papers · 1 filter
Re-evaluating Automatic LLM System Ranking for Alignment with Human Preference
Mingqi Gao, Yixin Liu, Xinyu Hu +3
Evaluating and ranking the capabilities of different LLMs is crucial for understanding their performance and alignment with human preferences. Due to the high cost and time-consumi…
Understanding Reference Policies in Direct Preference Optimization
Yixin Liu, Pengfei Liu, Arman Cohan
Direct Preference Optimization (DPO) has become a widely used training method for the instruction fine-tuning of large language models (LLMs). In this work, we explore an under-inv…
Step-Back Profiling: Distilling User History for Personalized Scientific Writing
Xiangru Tang, Xingyao Zhang, Yanjun Shao +6
Large language models (LLM) excel at a variety of natural language processing tasks, yet they struggle to generate personalized content for individuals, particularly in real-world…
Unveiling the Spectrum of Data Contamination in Language Models: A Survey from Detection to Remediation
Chunyuan Deng, Yilun Zhao, Yuzhao Heng +4
Data contamination has garnered increased attention in the era of large language models (LLMs) due to the reliance on extensive internet-derived training corpora. The issue of trai…
MIMIR: A Streamlined Platform for Personalized Agent Tuning in Domain Expertise
Chunyuan Deng, Xiangru Tang, Yilun Zhao +5
Recently, large language models (LLMs) have evolved into interactive agents, proficient in planning, tool use, and task execution across a wide variety of tasks. However, without s…
On the Benefits of Fine-Grained Loss Truncation: A Case Study on Factuality in Summarization
Lorenzo Jaime Yu Flores, Arman Cohan
Text summarization and simplification are among the most widely used applications of AI. However, models developed for such tasks are often prone to hallucination, which can result…