2 citations · 3 across the 17 of their papers we have counts for
5 papers · 1 filter
Language Model Probabilities are Not Calibrated in Numeric Contexts
Charles Lovering, Michael Krumdick, Viet Dac Lai +5
Some statements have one well-defined continuation (e.g., "the Eiffel Tower is in [Paris]"), whereas others have a natural distribution over multiple options (e.g., "the weighted c…
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…
Greed is All You Need: An Evaluation of Tokenizer Inference Methods
Omri Uzan, Craig W. Schmidt, Chris Tanner +1
While subword tokenizers such as BPE and WordPiece are typically used to build vocabularies for NLP models, the method of decoding text into a sequence of tokens from these vocabul…
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…