1 citations · 2 across the 10 of their papers we have counts for
11 papers
RLTLDR: Self-improvement by Internalizing Self-generated Feedback
Michael Kirchhof, Eleonora Gualdoni, Andrew Szot +6
The common paradigm of reinforcement learning with verifiable rewards (RLVR) is to let agents make multiple attempts at a task, and optimize towards the successful ones. This becom…
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…
Expanding LLM Agent Boundaries with Strategy-Guided Exploration
Andrew Szot, Michael Kirchhof, Omar Attia +1
Reinforcement learning (RL) has demonstrated notable success in post-training large language models (LLMs) as agents for tasks such as computer use, tool calling, and coding. Howev…
LaCy: What Small Language Models Can and Should Learn is Not Just a Question of Loss
Szilvia Ujváry, Louis Béthune, Pierre Ablin +3
Language models have consistently grown to compress more world knowledge into their parameters, but the knowledge that can be pretrained into them is upper-bounded by their paramet…
Learning Unmasking Policies for Diffusion Language Models
Metod Jazbec, Theo X. Olausson, Louis Béthune +6
Diffusion (Large) Language Models (dLLMs) now match the downstream performance of their autoregressive counterparts on many tasks, while holding the promise of being more efficient…
Trained on Tokens, Calibrated on Concepts: The Emergence of Semantic Calibration in LLMs
Preetum Nakkiran, Arwen Bradley, Adam Goliński +3
Large Language Models (LLMs) often lack meaningful confidence estimates for their outputs. While base LLMs are known to exhibit next-token calibration, it remains unclear whether t…
Pretraining with hierarchical memories: separating long-tail and common knowledge
Hadi Pouransari, David Grangier, C Thomas +2
The impressive performance gains of modern language models currently rely on scaling parameters: larger models store more world knowledge and reason better. Yet compressing all wor…
BED-LLM: Intelligent Information Gathering with LLMs and Bayesian Experimental Design
Deepro Choudhury, Sinead Williamson, Adam Goliński +5
We propose a general-purpose approach for improving the ability of large language models (LLMs) to intelligently and adaptively gather information from a user or other external sou…
The Geometries of Truth Are Orthogonal Across Tasks
Waiss Azizian, Michael Kirchhof, Eugene Ndiaye +4
Large Language Models (LLMs) have demonstrated impressive generalization capabilities across various tasks, but their claim to practical relevance is still mired by concerns on the…
SelfReflect: Can LLMs Communicate Their Internal Answer Distribution?
Michael Kirchhof, Luca Füger, Adam Goliński +4
The common approach to communicate a large language model's (LLM) uncertainty is to add a percentage number or a hedging word to its response. But is this all we can do? Instead of…
Revisiting Uncertainty Quantification Evaluation in Language Models: Spurious Interactions with Response Length Bias Results
Andrea Santilli, Adam Golinski, Michael Kirchhof +5
Uncertainty Quantification (UQ) in Language Models (LMs) is key to improving their safety and reliability. Evaluations often use metrics like AUROC to assess how well UQ methods (e…