1 citations · 1 across the 9 of their papers we have counts for
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Towards User Simulators That Stay On Task And Still Sound Natural
Mustafa Omer Gul, Philippe Laban, Tanya Goyal +1
Users increasingly rely on AI systems for extended, multi-turn tasks, yet evidence indicates that LLM-based system performance degrades over prolonged interaction. To evaluate and…
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…
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 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…
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…
CoGen: Learning from Feedback with Coupled Comprehension and Generation
Mustafa Omer Gul, Yoav Artzi
Systems with both language comprehension and generation capabilities can benefit from the tight connection between the two. This work studies coupling comprehension and generation…
CREPE: Can Vision-Language Foundation Models Reason Compositionally?
Zixian Ma, Jerry Hong, Mustafa Omer Gul +3
A fundamental characteristic common to both human vision and natural language is their compositional nature. Yet, despite the performance gains contributed by large vision and lang…