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Michael Kirchhof

14 accepted papers

2026

BED-LLM: Intelligent Information Gathering with LLMs and Bayesian Experimental Design

ICLR 2026poster

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 source using the framework of sequential Bayesian experimental design (BED). This enables LLMs to act as effective multi-tu…

Cited by 0SourceScholar
2026

Learning Unmasking Policies for Diffusion Language Models

ICML 2026oral

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 during inference. One critical design aspect of dLLMs is the \textit{sampling procedure} that selects which tokens to u…

Cited by 0SourceScholar
2026

Pretraining with hierarchical memories: separating long-tail and common knowledge

ICLR 2026poster

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 world knowledge into parameters is unnecessary, as only a fraction is used per prompt, and impractical for edge devices wit…

Cited by 0SourceScholar
2026

SelfReflect: Can LLMs Communicate Their Internal Answer Distribution?

ICLR 2026poster

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 generating a single answer and then hedging it, an LLM that is fully transparent to the user needs to be able to reflec…

Cited by 0SourcecodeScholar
2026

Trained on Tokens, Calibrated on Concepts: The Emergence of Semantic Calibration in LLMs

ICLR 2026poster

Large Language Models (LLMs) often lack meaningful confidence estimates for the semantic content of their outputs. While base LLMs are known to exhibit next-token calibration, it remains unclear whether they can assess confidence in the actual meaning of their responses beyond the token level. We fi…

Cited by 0SourceScholar
2025

Position: Uncertainty Quantification Needs Reassessment for Large Language Model Agents

ICML 2025poster

Large-language models (LLMs) and chatbot agents are known to provide wrong outputs at times, and it was recently found that this can never be fully prevented. Hence, uncertainty quantification plays a crucial role, aiming to quantify the level of ambiguity in either one overall number or two numbers…

Cited by 0SourcePDFScholar
2025

Revisiting Uncertainty Quantification Evaluation in Language Models: Spurious Interactions with Response Length Bias Results

ACL 2025short

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.g., negative sequence probabilities) correlate with task correctness functions (e.g., ROUGE-L). We show that mutual bia…

2025

Shielded Diffusion: Generating Novel and Diverse Images using Sparse Repellency

ICML 2025poster

The adoption of text-to-image diffusion models raises concerns over reliability, drawing scrutiny under the lens of various metrics like calibration, fairness, or compute efficiency. We focus in this work on two issues that arise when deploying these models: a lack of diversity when prompting images…

Cited by 0SourcePDFScholar
2024

Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks

NeurIPS 2024spotlight

Uncertainty quantification, once a singular task, has evolved into a spectrum of tasks, including abstained prediction, out-of-distribution detection, and aleatoric uncertainty quantification. The latest goal is disentanglement: the construction of multiple estimators that are each tailored to one a…

2023

Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs

ICML 2023poster

Contrastively trained encoders have recently been proven to invert the data-generating process: they encode each input, e.g., an image, into the true latent vector that generated the image (Zimmermann et al., 2021). However, real-world observations often have inherent ambiguities. For instance, imag…

2023

URL: A Representation Learning Benchmark for Transferable Uncertainty Estimates

NeurIPS 2023poster

Representation learning has significantly driven the field to develop pretrained models that can act as a valuable starting point when transferring to new datasets. With the rising demand for reliable machine learning and uncertainty quantification, there is a need for pretrained models that not onl…

2023

When are post-hoc conceptual explanations identifiable?

UAI 2023poster

Interest in understanding and factorizing learned embedding spaces through conceptual explanations is steadily growing. When no human concept labels are available, concept discovery methods search trained embedding spaces for interpretable concepts like object shape or color that can provide post-ho…

2022

A Non-Isotropic Probabilistic Take On Proxy-Based Deep Metric Learning

ECCV 2022poster

"Proxy-based Deep Metric Learning (DML) learns deep metric spaces by embedding images and class representatives (proxies) close to one another during training, as commonly measured by the angle between them. However, this disregards the embedding norm, which can carry additional beneficial context s…

2021

pRSL: Interpretable multi-label stacking by learning probabilistic rules

UAI 2021poster

A key task in multi-label classification is modeling the structure between the involved classes. Modeling this structure by probabilistic and interpretable means enables application in a broad variety of tasks such as zero-shot learning or learning from incomplete data. In this paper, we present the…