43 citations · 46 across the 7 of their papers we have counts for
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cs.CL2024★ 1 cited
Large Language Model Evaluation via Matrix Nuclear-Norm
Yahan Li, Tingyu Xia, Yi Chang +1
As large language models (LLMs) continue to evolve, efficient evaluation metrics are vital for assessing their ability to compress information and reduce redundancy. While traditio…
cs.CL2024★ 2 cited
Rethinking Data Selection at Scale: Random Selection is Almost All You Need
Tingyu Xia, Bowen Yu, Kai Dang +5
Supervised fine-tuning (SFT) is crucial for aligning Large Language Models (LLMs) with human instructions. The primary goal during SFT is to select a small yet representative subse…
cs.CL2024
Language Models can Evaluate Themselves via Probability Discrepancy
Tingyu Xia, Bowen Yu, Yuan Wu +2
In this paper, we initiate our discussion by demonstrating how Large Language Models (LLMs), when tasked with responding to queries, display a more even probability distribution in…