2 citations · 3 across the 15 of their papers we have counts for
14 papers · 1 filter
Joint Optimization for Greedy Longest-match Tokenization
Adhiraj Singh, Deepanshu Mody, Ghina Al Shdaifat +4
Recent work has shown that subword vocabularies can be trained to optimize compression for a specific inference rule rather than relying on greedy heuristics such as Byte Pair Enco…
Tokenization with Split Trees
Craig W. Schmidt, Michael Krumdick, Adam Wiemerslage +4
We introduce Tokenization with Split Trees (ToaST), a subword tokenization method that directly optimizes compression under a new recursive inference procedure. ToaST greedily spli…
FrontierFinance: A Long-Horizon Computer-Use Benchmark of Real-World Financial Tasks
Michael Krumdick, Varshini Reddy, Shivani Chaudhary +8
As concerns surrounding AI-driven labor displacement intensify in knowledge-intensive sectors, existing benchmarks fail to measure performance on tasks that define practical profes…
The Effect of Scripts and Formats on LLM Numeracy
Varshini Reddy, Craig W. Schmidt, Seth Ebner +3
Large language models (LLMs) have achieved impressive proficiency in basic arithmetic, rivaling human-level performance on standard numerical tasks. However, little attention has b…
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…
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…
An Analysis of Multilingual FActScore
Kim Trong Vu, Michael Krumdick, Varshini Reddy +2
FActScore has gained popularity as a metric to estimate the factuality of long-form texts generated by Large Language Models (LLMs) in English. However, there has not been any work…
SEC-QA: A Systematic Evaluation Corpus for Financial QA
Viet Dac Lai, Michael Krumdick, Charles Lovering +3
The financial domain frequently deals with large numbers of long documents that are essential for daily operations. Significant effort is put towards automating financial data anal…
Tokenization Is More Than Compression
Craig W. Schmidt, Varshini Reddy, Haoran Zhang +4
Tokenization is a foundational step in natural language processing (NLP) tasks, bridging raw text and language models. Existing tokenization approaches like Byte-Pair Encoding (BPE…
DocFinQA: A Long-Context Financial Reasoning Dataset
Varshini Reddy, Rik Koncel-Kedziorski, Viet Dac Lai +3
For large language models (LLMs) to be effective in the financial domain -- where each decision can have a significant impact -- it is necessary to investigate realistic tasks and…
BizBench: A Quantitative Reasoning Benchmark for Business and Finance
Rik Koncel-Kedziorski, Michael Krumdick, Viet Lai +3
Answering questions within business and finance requires reasoning, precision, and a wide-breadth of technical knowledge. Together, these requirements make this domain difficult fo…