96 citations · 131 across the 9 of their papers we have counts for
7 papers · 1 filter
HREF: Human Response-Guided Evaluation of Instruction Following in Language Models
Xinxi Lyu, Yizhong Wang, Hannaneh Hajishirzi +1
Evaluating the capability of Large Language Models (LLMs) in following instructions has heavily relied on a powerful LLM as the judge, introducing unresolved biases that deviate th…
Tulu 3: Pushing Frontiers in Open Language Model Post-Training
Nathan Lambert, Jacob Morrison, Valentina Pyatkin +20
Language model post-training is applied to refine behaviors and unlock new skills across a wide range of recent language models, but open recipes for applying these techniques lag…
Scalable Data Ablation Approximations for Language Models through Modular Training and Merging
Clara Na, Ian Magnusson, Ananya Harsh Jha +4
Training data compositions for Large Language Models (LLMs) can significantly affect their downstream performance. However, a thorough data ablation study exploring large sets of c…
Merge to Learn: Efficiently Adding Skills to Language Models with Model Merging
Jacob Morrison, Noah A. Smith, Hannaneh Hajishirzi +3
Adapting general-purpose language models to new skills is currently an expensive process that must be repeated as new instruction datasets targeting new skills are created, or can…
Hybrid Preferences: Learning to Route Instances for Human vs. AI Feedback
Lester James V. Miranda, Yizhong Wang, Yanai Elazar +6
Learning from human feedback has enabled the alignment of language models (LMs) with human preferences. However, collecting human preferences is expensive and time-consuming, with…
The Art of Saying No: Contextual Noncompliance in Language Models
Faeze Brahman, Sachin Kumar, Vidhisha Balachandran +11
Chat-based language models are designed to be helpful, yet they should not comply with every user request. While most existing work primarily focuses on refusal of "unsafe" queries…