Large Language Models for Simultaneous Named Entity Extraction and
  Spelling Correction
Edward Whittaker Ikuo Kitagishi
Language Models (LMs) such as BERT, have been shown to perform well on the
task of identifying Named Entities (NE) in text. A BERT LM is typically used as
a classifier to classify individual tokens in the input text, or to classify
spans of tokens, as belonging to one of a set of possible NE categories.
  In this paper, we hypothesise that decoder-only Large Language Models (LLMs)
can also be used generatively to extract both the NE, as well as potentially
recover the correct surface form of the NE, where any spelling errors that were
present in the input text get automatically corrected.
  We fine-tune two BERT LMs as baselines, as well as eight open-source LLMs, on
the task of producing NEs from text that was obtained by applying Optical
Character Recognition (OCR) to images of Japanese shop receipts; in this work,
we do not attempt to find or evaluate the location of NEs in the text.
  We show that the best fine-tuned LLM performs as well as, or slightly better
than, the best fine-tuned BERT LM, although the differences are not
significant. However, the best LLM is also shown to correct OCR errors in some
cases, as initially hypothesised.