Fact editing in Large Language Models: in-weights vs in-context techniques

Vashchuk, Oleksandr

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dc.contributor.author	Vashchuk, Oleksandr
dc.date.accessioned	2024-08-23T11:31:28Z
dc.date.available	2024-08-23T11:31:28Z
dc.date.issued	2024
dc.identifier.citation	Vashchuk Oleksandr. Fact editing in Large Language Models: in-weights vs in-context techniques. Ukrainian Catholic University, Faculty of Applied Sciences, Department of Computer Sciences. Lviv 2024, 46 p.	uk
dc.identifier.uri	https://er.ucu.edu.ua/handle/1/4678
dc.language.iso	en	uk
dc.subject	in Large Language Models	uk
dc.subject	in-weights techniques	uk
dc.subject	in-context techniques	uk
dc.title	Fact editing in Large Language Models: in-weights vs in-context techniques	uk
dc.type	Preprint	uk
dc.status	Публікується вперше	uk
dc.description.abstracten	As Large Language Models (LLMs) have gained visibility for their ability to gen- erate human-like text, ensuring the accuracy and reliability of the information they produce has become crucial. Thus facts-editing approaches received wide attention due to the possibility of editing the model’s factual knowledge without investing resources to improve the dataset used for training the model, fine-tuning, or adap- tive tuning. Together with the development of fact-editing methods also improves understanding of facts storage and retrieval inside the LLMs. The main goal of the work is to study factual retrieval mechanisms for in-context and in-weights knowl- edge. This work concentrates on mechanistic interpretability for LLMs. Several experi- ments were conducted to understand the factual retrieval mechanisms in the model. As a result, important model components that contribute most during factual recall were identified.	uk