2 citations · 3 across the 17 of their papers we have counts for
6 papers · 1 filter
On Finding Inconsistencies in Documents
Charles J. Lovering, Seth Ebner, Brandon Smock +5
Professionals in academia, law, and finance audit their documents because inconsistencies can result in monetary, reputational, and scientific costs. Language models (LMs) have the…
Entropy-Driven Pre-Tokenization for Byte-Pair Encoding
Yifan Hu, Frank Liang, Dachuan Zhao +4
Byte-Pair Encoding (BPE) has become a widely adopted subword tokenization method in modern language models due to its simplicity and strong empirical performance across downstream…
BLEUBERI: BLEU is a surprisingly effective reward for instruction following
Yapei Chang, Yekyung Kim, Michael Krumdick +4
Reward models are central to aligning LLMs with human preferences, but they are costly to train, requiring large-scale human-labeled preference data and powerful pretrained LLM bac…
Boundless Byte Pair Encoding: Breaking the Pre-tokenization Barrier
Craig W. Schmidt, Varshini Reddy, Chris Tanner +1
Pre-tokenization, the initial step in many modern tokenization pipelines, segments text into smaller units called pretokens, typically splitting on whitespace and punctuation. Whil…
No Free Labels: Limitations of LLM-as-a-Judge Without Human Grounding
Michael Krumdick, Charles Lovering, Varshini Reddy +2
Reliable evaluation of large language models (LLMs) is critical as their deployment rapidly expands, particularly in high-stakes domains such as business and finance. The LLM-as-a-…
How Much is Enough? The Diminishing Returns of Tokenization Training Data
Varshini Reddy, Craig W. Schmidt, Yuval Pinter +1
Tokenization, a crucial initial step in natural language processing, is governed by several key parameters, such as the tokenization algorithm, vocabulary size, pre-tokenization st…