Understanding the Interplay between LLMs' Utilisation of Parametric and Contextual Knowledge

Add to your list(s) Download to your calendar using vCal

• Prof Isabelle Augenstein (University of Copenhagen)
• Friday 01 May 2026, 12:00-13:00
• FW11 Hybrid (In-Person + Online). Here is the Google Meet Link: https://meet.google.com/cru-hcuo-rhu.

If you have a question about this talk, please contact Suchir Salhan.

Language Models (LMs) acquire parametric knowledge from their training process, embedding it within their weights. The increasing scalability of LMs, however, poses significant challenges for understanding a model’s inner workings and further for updating or correcting this embedded knowledge without the significant cost of retraining. Moreover, when using these language models for knowledge-intensive language understanding tasks, LMs have to integrate relevant context, mitigating their inherent weaknesses, such as incomplete or outdated knowledge. Nevertheless, studies indicate that LMs often ignore the provided context as it can be in conflict with the pre-existing LM’s memory learned during pre-training. Conflicting knowledge can also already be present in the LM’s parameters, termed intra-memory conflict. This underscores the importance of understanding the interplay between how a language model uses its parametric knowledge and the retrieved contextual knowledge.

In this talk, I will aim to shed light on this important issue by presenting our research on evaluating the knowledge present in LMs, diagnostic tests that can reveal knowledge conflicts, as well as on understanding the characteristics of successfully used contextual knowledge.

Bio: Isabelle Augenstein is a Professor at the University of Copenhagen, Department of Computer Science, where she heads the Natural Language Processing section. Her main research interests are fair and accountable NLP , including challenges such as explainability, factuality and bias detection. Prior to starting a faculty position, she was a postdoctoral researcher at University College London, and before that a PhD student at the University of Sheffield. In October 2022, Isabelle Augenstein became Denmark’s youngest ever female full professor. She currently holds a prestigious ERC Starting Grant on ‘Explainable and Robust Automatic Fact Checking’, and her research has been recognised by a Karen Spärck Jones Award, as well as a Hartmann Diploma Prize. She is a member of the Royal Danish Academy of Sciences and Letters, and co-leads the Danish Pioneer Centre for AI.

This talk is part of the NLIP Seminar Series series.

This talk is included in these lists:

• All Talks (aka the CURE list)
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge Forum of Science and Humanities
• Cambridge Language Sciences
• Cambridge talks
• Chris Davis' list
• Computer Education Research
• Computing Education Research
• Department of Computer Science and Technology talks and seminars
• FW11 Hybrid (In-Person + Online). Here is the Google Meet Link: https://meet.google.com/cru-hcuo-rhu
• Graduate-Seminars
• Guy Emerson's list
• Interested Talks
• Language Sciences for Graduate Students
• NLIP Seminar Series
• PMRFPS's
• Simon Baker's List
• Trust & Technology Initiative - interesting events
• bld31
• ndk22's list
• ob366-ai4er
• rp587
• yk449

Note that ex-directory lists are not shown.