Understanding the Interplay between LLMs' Utilisation of Parametric and Contextual Knowledge: A keynote at ECIR 2025

• URL: http://arxiv.org/abs/2603.09654v1
• Date: Tue, 10 Mar 2026 13:30:21 GMT
• Title: Understanding the Interplay between LLMs' Utilisation of Parametric and Contextual Knowledge: A keynote at ECIR 2025
• Abstract summary: Language Models (LMs) acquire parametric knowledge from their training process, embedding it within their weights.<n>When using these language models for knowledge-intensive language understanding tasks, LMs have to integrate relevant context.<n>Conflicting knowledge can also already be present in the LM's parameters, termed intra-memory conflict.
• Score: 41.44725874619931
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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tasks, LMs have to integrate relevant context, mitigating their inherent weaknesses, such as incomplete or outdated knowledge.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.

Related papers

• Where Knowledge Collides: A Mechanistic Study of Intra-Memory Knowledge Conflict in Language Models [8.965740058804197]
  In language models (LMs) intra-memory knowledge conflict largely arises when inconsistent information about the same event is encoded within the model's parametric knowledge.<n>We use mechanistic interpretability methods to identify where and how conflicting knowledge from pre-training data is encoded within LMs.<n>Our findings contribute to a growing body of evidence that specific internal components of a language model are responsible for encoding conflicting knowledge from pre-training.
  arXiv  Detail & Related papers  (2026-01-14T12:45:52Z)
• DYNAMICQA: Tracing Internal Knowledge Conflicts in Language Models [42.776896363518844]
  We study the effect of intra-memory conflict on an LM's ability to accept relevant context. We utilize two knowledge conflict measures and a novel dataset containing inherently conflicting data, DynamicQA. We verify that LMs exhibit a greater degree of intra-memory conflict with dynamic facts compared to facts that have a single truth value.
  arXiv  Detail & Related papers  (2024-07-24T06:06:07Z)
• Large Language Models are Limited in Out-of-Context Knowledge Reasoning [65.72847298578071]
  Large Language Models (LLMs) possess extensive knowledge and strong capabilities in performing in-context reasoning. This paper focuses on a significant aspect of out-of-context reasoning: Out-of-Context Knowledge Reasoning (OCKR), which is to combine multiple knowledge to infer new knowledge.
  arXiv  Detail & Related papers  (2024-06-11T15:58:59Z)
• Evaluating the External and Parametric Knowledge Fusion of Large Language Models [72.40026897037814]
  We develop a systematic pipeline for data construction and knowledge infusion to simulate knowledge fusion scenarios. Our investigation reveals that enhancing parametric knowledge within LLMs can significantly bolster their capability for knowledge integration. Our findings aim to steer future explorations on harmonizing external and parametric knowledge within LLMs.
  arXiv  Detail & Related papers  (2024-05-29T11:48:27Z)
• LLMs' Reading Comprehension Is Affected by Parametric Knowledge and Struggles with Hypothetical Statements [59.71218039095155]
  Task of reading comprehension (RC) provides a primary means to assess language models' natural language understanding (NLU) capabilities.<n>If the context aligns with the models' internal knowledge, it is hard to discern whether the models' answers stem from context comprehension or from internal information.<n>To address this issue, we suggest to use RC on imaginary data, based on fictitious facts and entities.
  arXiv  Detail & Related papers  (2024-04-09T13:08:56Z)
• Untangle the KNOT: Interweaving Conflicting Knowledge and Reasoning Skills in Large Language Models [51.72963030032491]
  Knowledge documents for large language models (LLMs) may conflict with the memory of LLMs due to outdated or incorrect knowledge. We construct a new dataset, dubbed KNOT, for knowledge conflict resolution examination in the form of question answering.
  arXiv  Detail & Related papers  (2024-04-04T16:40:11Z)
• A Comprehensive Study of Knowledge Editing for Large Language Models [82.65729336401027]
  Large Language Models (LLMs) have shown extraordinary capabilities in understanding and generating text that closely mirrors human communication. This paper defines the knowledge editing problem and provides a comprehensive review of cutting-edge approaches. We introduce a new benchmark, KnowEdit, for a comprehensive empirical evaluation of representative knowledge editing approaches.
  arXiv  Detail & Related papers  (2024-01-02T16:54:58Z)
• Online Continual Knowledge Learning for Language Models [3.654507524092343]
  Large Language Models (LLMs) serve as repositories of extensive world knowledge, enabling them to perform tasks such as question-answering and fact-checking. Online Continual Knowledge Learning (OCKL) aims to manage the dynamic nature of world knowledge in LMs under real-time constraints.
  arXiv  Detail & Related papers  (2023-11-16T07:31:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.