5 papers
MASH: Modeling Abstention via Selective Help-Seeking
Mustafa Omer Gul, Claire Cardie, Tanya Goyal
LLMs cannot reliably recognize their parametric knowledge boundaries and often hallucinate answers to outside-of-boundary questions. In this paper, we introduce MASH (Modeling Abst…
BabyLM Turns 4 and Goes Multilingual: Call for Papers for the 2026 BabyLM Workshop
Leshem Choshen, Ryan Cotterell, Mustafa Omer Gul +7
The goal of the BabyLM is to stimulate new research connections between cognitive modeling and language model pretraining. We invite contributions in this vein to the BabyLM Worksh…
Updating Parametric Knowledge with Context Distillation Retains Post-Training Capabilities
Shankar Padmanabhan, Mustafa Omer Gul, Tanya Goyal
Post-training endows pretrained LLMs with a variety of desirable skills, including instruction-following, reasoning, and others. However, these post-trained LLMs only encode knowle…
Retrospective Learning from Interactions
Zizhao Chen, Mustafa Omer Gul, Yiwei Chen +3
Multi-turn interactions between large language models (LLMs) and users naturally include implicit feedback signals. If an LLM responds in an unexpected way to an instruction, the u…
BabyLM Turns 3: Call for papers for the 2025 BabyLM workshop
Lucas Charpentier, Leshem Choshen, Ryan Cotterell +11
BabyLM aims to dissolve the boundaries between cognitive modeling and language modeling. We call for both workshop papers and for researchers to join the 3rd BabyLM competition. As…