The workshop will take place on Thursday, October 29, 2026, in Budapest, Hungary. All times below are in Budapest local time (CET, UTC+1).

See the complete list of accepted papers.

Workshop Schedule

Each keynote has a 40-minute slot: approximately 30 minutes for the talk and 10 minutes for questions.

Time	Session
09:00–09:15	Opening Remarks
09:15–09:55	Keynote Talk 1: Dan Roth
09:55–10:05	SHROOM shared task on hallucination detection
10:05–10:30	Poster lightning round 1
10:30–11:00	Coffee Break
11:00–12:10	In-Person Poster Session 1
12:10–13:10	Lunch Break
13:10–13:50	Keynote Talk 2: Yingzhen Li
13:50–14:30	Poster lightning round 2
14:30–15:30	In-Person Poster Session 2
15:30–16:00	Coffee Break
16:00–16:40	Keynote Talk 3: Gintare Karolina Dziugaite
16:40–17:20	Keynote Talk 4: André Martins
17:20–17:25	Closing Remarks
17:25	End

Keynote: Yingzhen Li

Quantifying uncertainty in-context via probing questions

Time: 13:10–13:50 Budapest local time (CET, UTC+1).

Abstract: As large language models (LLMs) gain popularity in conducting prediction tasks in-context, understanding and estimating uncertainty in in-context learning becomes essential to ensuring reliability of prediction and decision-making. The recent hypothesis of in-context learning performing predictive Bayesian inference opens the avenue for Bayesian uncertainty estimation and decision making algorithms. This talk will discuss our recent efforts in decomposing uncertainty and decision making under uncertainty in-context. We first introduce a variational uncertainty decomposition framework for in-context learning without explicitly sampling from the latent parameter posterior, by optimising auxiliary question as probes to obtain an upper bound to the aleatoric uncertainty of an LLM’s in-context learning procedure. We then show the classical Thompson Sampling algorithm can be adapted in this auxiliary question probing framework, where different aggregation strategies for the probes result in different exploration-exploitation trade-off behaviours.