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20242026
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cs.LG2026

Convex Dataset Valuation for Post-Training

Siqi Zeng, Christopher Jung, Rui Li +7

Improving LLM performance on downstream tasks sometimes requires leveraging auxiliary datasets during post-training. In practice, however, developers face constraints on compute, l…

cs.LG2025

When LRP Diverges from Leave-One-Out in Transformers

Weiqiu You, Siqi Zeng, Yao-Hung Hubert Tsai +2

Leave-One-Out (LOO) provides an intuitive measure of feature importance but is computationally prohibitive. While Layer-Wise Relevance Propagation (LRP) offers a potentially effici…

cs.LG2025

MergeBench: A Benchmark for Merging Domain-Specialized LLMs

Yifei He, Siqi Zeng, Yuzheng Hu +3

Model merging provides a scalable alternative to multi-task training by combining specialized finetuned models through parameter arithmetic, enabling efficient deployment without t…

cs.LG2025

Task Vector Bases: A Unified and Scalable Framework for Compressed Task Arithmetic

Siqi Zeng, Yifei He, Meitong Liu +5

Task arithmetic, representing downstream tasks through linear operations on task vectors, has emerged as a simple yet powerful paradigm for transferring knowledge across diverse se…

cs.LG2024

Learning Structured Representations with Hyperbolic Embeddings

Aditya Sinha, Siqi Zeng, Makoto Yamada +1

Most real-world datasets consist of a natural hierarchy between classes or an inherent label structure that is either already available or can be constructed cheaply. However, most…

cs.LG2024

Learning Structured Representations by Embedding Class Hierarchy with Fast Optimal Transport

Siqi Zeng, Sixian Du, Makoto Yamada +1

To embed structured knowledge within labels into feature representations, prior work [Zeng et al., 2022] proposed to use the Cophenetic Correlation Coefficient (CPCC) as a regulari…