15 papers
On The Effectiveness-Fluency Trade-Off In LLM Conditioning: A Systematic Study
Iuri Macocco, Pau RodrÃguez, Arno Blaas +3
Controlling the output of Large Language Models (LLMs) is a central challenge for their reliable deployment, yet a clear understanding of the involved trade-offs remains elusive. C…
HyperTransport: Amortized Conditioning of T2I Generative Models
Valentino Maiorca, Eleonora Gualdoni, Xavier Suau +3
As foundation models grow in capability, the ability to efficiently and reliably control their behavior becomes critical. Fine-tuning these models can be costly, and while promptin…
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…
Attention to Mamba: A Recipe for Cross-Architecture Distillation
Abhinav Moudgil, Ningyuan Huang, Eeshan Gunesh Dhekane +3
State Space Models (SSMs) such as Mamba have become a popular alternative to Transformer models, due to their reduced memory consumption and higher throughput at generation compare…
The Design Space of Tri-Modal Masked Diffusion Models
Louis Bethune, Victor Turrisi, Bruno Kacper Mlodozeniec +21
Discrete diffusion models have emerged as strong alternatives to autoregressive language models, with recent work initializing and fine-tuning a base unimodal model for bimodal gen…
DSO: Direct Steering Optimization for Bias Mitigation
Lucas Monteiro Paes, Nivedha Sivakumar, Yinong Oliver Wang +4
Generative models are often deployed to make decisions on behalf of users, such as vision-language models (VLMs) identifying which person in a room is a doctor to help visually imp…