1 citations · 2 across the 12 of their papers we have counts for
7 papers · 1 filter
RRM: Robust Reward Model Training Mitigates Reward Hacking
Tianqi Liu, Wei Xiong, Jie Ren +15
Reward models (RMs) play a pivotal role in aligning large language models (LLMs) with human preferences. However, traditional RM training, which relies on response pairs tied to sp…
Multilingual Fine-Grained News Headline Hallucination Detection
Jiaming Shen, Tianqi Liu, Jialu Liu +4
The popularity of automated news headline generation has surged with advancements in pre-trained language models. However, these models often suffer from the ``hallucination'' prob…
Boosting Reward Model with Preference-Conditional Multi-Aspect Synthetic Data Generation
Jiaming Shen, Ran Xu, Yennie Jun +6
Reward models (RMs) are crucial for aligning large language models (LLMs) with human preferences. They are trained using preference datasets where each example consists of one inpu…
PLaD: Preference-based Large Language Model Distillation with Pseudo-Preference Pairs
Rongzhi Zhang, Jiaming Shen, Tianqi Liu +7
Large Language Models (LLMs) have exhibited impressive capabilities in various tasks, yet their vast parameter sizes restrict their applicability in resource-constrained settings.…
LiPO: Listwise Preference Optimization through Learning-to-Rank
Tianqi Liu, Zhen Qin, Junru Wu +9
Aligning language models (LMs) with curated human feedback is critical to control their behaviors in real-world applications. Several recent policy optimization methods, such as DP…
Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning
Yue Yu, Jiaming Shen, Tianqi Liu +5
Large language models (LLMs) have shown remarkable capabilities in various natural language understanding tasks. With only a few demonstration examples, these LLMs can quickly adap…
Predicting Text Preference Via Structured Comparative Reasoning
Jing Nathan Yan, Tianqi Liu, Justin T Chiu +9
Comparative reasoning plays a crucial role in text preference prediction; however, large language models (LLMs) often demonstrate inconsistencies in their reasoning. While approach…