3 papers
cs.CL2026
Language models can learn implicit multi-hop reasoning, but only if they have lots of training data
Yuekun Yao, Yupei Du, Dawei Zhu +2
Implicit reasoning is the ability of a language model to solve multi-hop reasoning tasks in a single forward pass, without chain of thought. We investigate this capability using GP…
cs.CL2025
Position: Pause Recycling LoRAs and Prioritize Mechanisms to Uncover Limits and Effectiveness
Mei-Yen Chen, Thi Thu Uyen Hoang, Michael Hahn +1
Merging or routing low-rank adapters (LoRAs) has emerged as a popular solution for enhancing large language models, particularly when data access is restricted by regulatory or dom…
cs.CL2025
Emergent Stack Representations in Modeling Counter Languages Using Transformers
Utkarsh Tiwari, Aviral Gupta, Michael Hahn
Transformer architectures are the backbone of most modern language models, but understanding the inner workings of these models still largely remains an open problem. One way that…