160 citations · 350 across the 15 of their papers we have counts for
20 papers · 1 filter
Ministral 3
Alexander H. Liu, Kartik Khandelwal, Sandeep Subramanian +116
We introduce the Ministral 3 series, a family of parameter-efficient dense language models designed for compute and memory constrained applications, available in three model sizes:…
Robust Multi-Objective Preference Alignment with Online DPO
Raghav Gupta, Ryan Sullivan, Yunxuan Li +2
Multi-objective preference alignment of large language models (LLMs) is critical for developing AI systems that are more configurable, personalizable, helpful, and safe. However, o…
MoDE: Effective Multi-task Parameter Efficient Fine-Tuning with a Mixture of Dyadic Experts
Lin Ning, Harsh Lara, Meiqi Guo +1
Parameter-efficient fine-tuning techniques like Low-Rank Adaptation (LoRA) have revolutionized the adaptation of large language models (LLMs) to diverse tasks. Recent efforts have…
Rel-A.I.: An Interaction-Centered Approach To Measuring Human-LM Reliance
Kaitlyn Zhou, Jena D. Hwang, Xiang Ren +3
The ability to communicate uncertainty, risk, and limitation is crucial for the safety of large language models. However, current evaluations of these abilities rely on simple cali…
Improve Mathematical Reasoning in Language Models by Automated Process Supervision
Liangchen Luo, Yinxiao Liu, Rosanne Liu +9
Complex multi-step reasoning tasks, such as solving mathematical problems or generating code, remain a significant hurdle for even the most advanced large language models (LLMs). V…
Granular Change Accuracy: A More Accurate Performance Metric for Dialogue State Tracking
Taha Aksu, Nancy F. Chen
Current metrics for evaluating Dialogue State Tracking (DST) systems exhibit three primary limitations. They: i) erroneously presume a uniform distribution of slots throughout the…