Linear and spherical interpolation of ANCE and QRACDR parameters yields a single dense retriever that recovers ad-hoc effectiveness while retaining conversational skill and improving zero-shot generalization.
InEuropean Conference on Computer Vision
2 Pith papers cite this work. Polarity classification is still indexing.
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CDGLT achieves SOTA on MET-Meme for multimodal metaphor identification by using SLERP-based concept drift and prompt-adapted LayerNorm tuning with reduced compute.
citing papers explorer
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Improving Ad-hoc Search Effectiveness for Conversational Information Retrieval via Model Merging
Linear and spherical interpolation of ANCE and QRACDR parameters yields a single dense retriever that recovers ad-hoc effectiveness while retaining conversational skill and improving zero-shot generalization.
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Concept Drift Guided LayerNorm Tuning for Efficient Multimodal Metaphor Identification
CDGLT achieves SOTA on MET-Meme for multimodal metaphor identification by using SLERP-based concept drift and prompt-adapted LayerNorm tuning with reduced compute.