5 citations · 8 across the 12 of their papers we have counts for
3 papers · 1 filter
Unlocking the Working Memory of Large Language Models for Latent Reasoning
Lukas Aichberger, Sepp Hochreiter
To improve the reasoning capabilities of large language models, test-time compute is typically scaled by generating intermediate tokens before the final answer. However, this coupl…
Annotations Mitigate Post-Training Mode Collapse
Jacob Mitchell Springer, Madhu Advani, Lukas Aichberger +7
Post-training (via supervised fine-tuning) improves instruction-following, but often induces semantic mode collapse by biasing models toward low-entropy fine-tuning data at the exp…
Uncertainty Quantification for LLM Function-Calling
Zihuiwen Ye, Lukas Aichberger, Michael Kirchhof +5
Large Language Models (LLMs) are increasingly deployed to autonomously solve real-world tasks. A key ingredient for this is the LLM Function-Calling paradigm, a widely used approac…