237 citations · 1.3k across the 60 of their papers we have counts for
6 papers · 1 filter
A Systematic Examination of Preference Learning through the Lens of Instruction-Following
Joongwon Kim, Anirudh Goyal, Aston Zhang +6
Preference learning is a widely adopted post-training technique that aligns large language models (LLMs) to human preferences and improves specific downstream task capabilities. In…
Self-Generated Critiques Boost Reward Modeling for Language Models
Yue Yu, Zhengxing Chen, Aston Zhang +10
Reward modeling is crucial for aligning large language models (LLMs) with human preferences, especially in reinforcement learning from human feedback (RLHF). However, current rewar…
LLäMmlein: Transparent, Compact and Competitive German-Only Language Models from Scratch
Jan Pfister, Julia Wunderle, Andreas Hotho
We create two German-only decoder models, LLäMmlein 120M and 1B, transparently from scratch and publish them, along with the training data, for the German NLP research community to…
Law of the Weakest Link: Cross Capabilities of Large Language Models
Ming Zhong, Aston Zhang, Xuewei Wang +14
The development and evaluation of Large Language Models (LLMs) have largely focused on individual capabilities. However, this overlooks the intersection of multiple abilities acros…
Caution for the Environment: Multimodal LLM Agents are Susceptible to Environmental Distractions
Xinbei Ma, Yiting Wang, Yao Yao +4
This paper investigates the faithfulness of multimodal large language model (MLLM) agents in a graphical user interface (GUI) environment, aiming to address the research question o…
The Llama 3 Herd of Models
Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri +556
Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models th…