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

arXiv:2603.09654v1 Announce Type: new 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tasks, LMs have to integrate relevant context, mitigating their inherent weaknesses, such as incomplete or outdated knowledge.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

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Isabelle Augenstein · · 1 min read · 17 views
#cs.CL #cs.IR

arXiv:2603.09654v1 Announce Type: new 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tasks, LMs have to integrate relevant context, mitigating their inherent weaknesses, such as incomplete or outdated knowledge.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.

Executive Summary

This article presents a keynote address at ECIR 2025, focusing on the interplay between language models' (LMs) utilisation of parametric and contextual knowledge. The authors highlight the challenges of understanding and updating LMs' embedded knowledge without retraining, and the need to integrate contextual knowledge to mitigate their weaknesses. The talk introduces research on evaluating knowledge present in LMs, diagnostic tests for knowledge conflicts, and the characteristics of successfully used contextual knowledge. The authors aim to shed light on the interplay between parametric and contextual knowledge, which is crucial for effective language understanding tasks. This research has significant implications for the development and deployment of LMs in various applications, including language understanding, question answering, and text generation.

Key Points

  • ▸ Language models acquire parametric knowledge from their training process, embedding it within their weights.
  • ▸ The increasing scalability of LMs poses challenges for understanding their inner workings and updating their embedded knowledge.
  • ▸ LMS often ignore provided context due to potential conflicts with pre-existing knowledge learned during pre-training.

Merits

Strength

The authors provide a comprehensive overview of the interplay between parametric and contextual knowledge, highlighting the importance of understanding this issue for effective language understanding tasks.

Methodological contribution

The authors introduce research on evaluating knowledge present in LMs, diagnostic tests for knowledge conflicts, and the characteristics of successfully used contextual knowledge, which is a significant methodological contribution to the field.

Implications for applications

The research has significant implications for the development and deployment of LMs in various applications, including language understanding, question answering, and text generation.

Demerits

Limitation

The article is a keynote address and does not provide a comprehensive theoretical framework for understanding the interplay between parametric and contextual knowledge.

Scope

The research appears to focus primarily on evaluating knowledge present in LMs and diagnostic tests for knowledge conflicts, which may not be comprehensive enough to address the complexities of language understanding tasks.

Expert Commentary

The research presented in this article is a crucial step towards understanding the complexities of language models' knowledge acquisition processes. The authors' focus on the interplay between parametric and contextual knowledge is timely and relevant, given the increasing deployment of LMs in various applications. However, the article's limitations, such as its scope and theoretical framework, need to be addressed in future research. Furthermore, the implications of this research for policy and practice are significant, and it is essential to consider the potential consequences of LMs' limitations on their accuracy and reliability.

Recommendations

  • ✓ Recommendation 1: Future research should aim to develop a comprehensive theoretical framework for understanding the interplay between parametric and contextual knowledge.
  • ✓ Recommendation 2: The authors should consider extending their research to address the complexities of language understanding tasks, including the integration of contextual knowledge and the mitigation of knowledge conflicts.

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