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Adam Golinski

9 accepted papers

2026

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

Deepro Choudhury, Sinead Williamson, Adam Golinski, Ning Miao, Freddie Bickford Smith, Michael Kirchhof +2
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…

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2026

SelfReflect: Can LLMs Communicate Their Internal Answer Distribution?

Michael Kirchhof, Luca Füger, Adam Golinski, Eeshan Gunesh Dhekane, Arno Blaas, Seong Joon Oh +1
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 0Source ↗code ↗Scholar ↗
2026

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

Preetum Nakkiran, Arwen Bradley, Adam Golinski, Eugene Ndiaye, Michael Kirchhof, Sinead Williamson
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…

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2025

Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for LLMs

Yinong Oliver Wang, Nivedha Sivakumar, Falaah Arif Khan, Katherine Metcalf, Adam Golinski, Natalie Mackraz +3
ICML 2025poster

The recent rapid adoption of large language models (LLMs) highlights the critical need for benchmarking their fairness. Conventional fairness metrics, which focus on discrete accuracy-based evaluations (i.e., prediction correctness), fail to capture the implicit impact of model uncertainty (e.g., hi…

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2025

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

Andrea Santilli, Adam Golinski, Michael Kirchhof, Federico Danieli, Arno Blaas, Miao Xiong +2
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…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2023

The Role of Entropy and Reconstruction in Multi-View Self-Supervised Learning

Borja Rodríguez Gálvez, Arno Blaas, Pau Rodriguez, Adam Golinski, Xavier Suau, Jason Ramapuram +2
ICML 2023poster

The mechanisms behind the success of multi-view self-supervised learning (MVSSL) are not yet fully understood. Contrastive MVSSL methods have been studied through the lens of InfoNCE, a lower bound of the Mutual Information (MI). However, the relation between other MVSSL methods and MI remains uncle…

Cited by 13Source ↗PDF ↗code ↗Scholar ↗
2022

LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood

Piotr Tempczyk, Rafał Michaluk, Lukasz Garncarek, Przemysław Spurek, Jacek Tabor, Adam Golinski
ICML 2022oral

Most of the existing methods for estimating the local intrinsic dimension of a data distribution do not scale well to high dimensional data. Many of them rely on a non-parametric nearest neighbours approach which suffers from the curse of dimensionality. We attempt to address that challenge by propo…

Cited by 28Source ↗PDF ↗code ↗Scholar ↗
2018

Faithful Inversion of Generative Models for Effective Amortized Inference

Stefan Webb, Adam Golinski, Rob Zinkov, Siddharth N, Tom Rainforth, Yee Whye Teh +1
NeurIPS 2018poster

Inference amortization methods share information across multiple posterior-inference problems, allowing each to be carried out more efficiently. Generally, they require the inversion of the dependency structure in the generative model, as the modeller must learn a mapping from observations to distri…

Cited by 57Source ↗PDF ↗Scholar ↗