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This is an announcement of <span class="x_outlook-search-highlight" style="color:rgb(7,7,6)"><span class="x_outlook-search-highlight">Xiaotian</span></span> <span class="x_outlook-search-highlight" style="color:rgb(7,7,6)"><span class="x_outlook-search-highlight">Duan</span></span>'s
Candidacy Exam.</div>
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Candidate: <span class="x_outlook-search-highlight" style="color:rgb(7,7,6)"><span class="x_outlook-search-highlight">Xiaotian</span></span> <span class="x_outlook-search-highlight" style="color:rgb(7,7,6)"><span class="x_outlook-search-highlight">Duan</span></span></div>
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Date: Tuesday, March 05, 2024</div>
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Time: 10 am CST</div>
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Location: JCL 298</div>
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Title: Retrieval-Based Parameter Activation for Large Language Model Knowledge Editing</div>
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Abstract: Knowledge editing in Large Language Models (LLMs) aims to make targeted changes to specific data points to fix inaccuracies or biases in these models without mistakenly altering other knowledge or skills. The research of knowledge editing focuses
on three key aspects: generalization (the model’s ability to apply edited information across various contexts), locality (precise edits without affecting unrelated information), and scalability (maintaining performance efficiency and stability with increasing
edits). We propose two novel contributions: (1) a new knowledge editing benchmark designed to overcome the limitations of existing benchmarks, which lack the material for fine-tuning methods and comprehensive evaluations, and (2) a novel external-memory-based
approach for knowledge editing that utilizes an embedding model with a vector store. Preliminary results show that compact embedding models can effectively differentiate the edited and unrelated facts, maintaining high accuracy even when scaled to thousands
of edits. Integrating this approach with parameter-efficient fine-tuning strategies, this research aims to address all three critical aspects of knowledge editing: generalization, locality, and scalability.</div>
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Advisors: Rick Stevens</div>
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Committee Members: Rick Stevens, Fangfang Xia, and Ben Zhao</div>
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