Large Language Models for Named Entity Extraction and Spelling Correction

核心概念

Large language models can be fine-tuned to extract named entities and correct spelling errors simultaneously, showing promising results in text processing tasks.

摘要

The study explores the use of large language models for extracting named entities and correcting spelling errors in text data. Results show that fine-tuned language models can outperform traditional methods in these tasks.

Language models like BERT are utilized for Named Entity extraction from OCR text. The study focuses on NE categories like shop names, addresses, dates, etc. Large Language Models (LLMs) are hypothesized to generatively extract NEs and correct spelling errors simultaneously. Fine-tuned LLMs show potential in improving NE extraction accuracy compared to BERT LMs. The best-performing LLM demonstrates the ability to correct OCR errors in some cases. Different approaches for supervised fine-tuning of LLMs are explored, with a focus on LoRA method optimization. Model formulation as question answering by LM text completion is detailed. The prompt format used for fine-tuning is crucial for optimal performance. Evaluation criteria include precision, recall, and weighted F-measure calculations for each NE category. Results indicate that LLMs can offer competitive performance compared to BERT LMs in NE extraction tasks.

統計資料

A BERT LM gave an 𝐹 (𝑡𝑒𝑠𝑡) 𝑓 𝑖𝑛𝑎𝑙 of 84.6.
The best performing LLM was rinna/youri-7b with an 𝐹 (𝑡𝑒𝑠𝑡) 𝑓 𝑖𝑛𝑎𝑙 of 85.6.
cyberagent/open-calm-small had a precision of 70.1% on the test set.
line-corporation/japanese-large-lm-3.6b achieved a recall of 100% for the date NE category.

引述

"Language Models such as BERT have been shown to perform well on identifying Named Entities in text."
"In this paper, we hypothesize that decoder-only Large Language Models can be used generatively to extract both the NE and potentially recover the correct surface form."
"We show that the best fine-tuned LLM performs as well as or slightly better than the best fine-tuned BERT LM."

從以下內容提煉的關鍵洞見

Large Language Models for Simultaneous Named Entity Extraction and Spelling Correction

by Edward Whitt... 於 arxiv.org 03-04-2024

https://arxiv.org/pdf/2403.00528.pdf

深入探究

How does the performance of large language models compare to traditional methods in named entity extraction?

Large Language Models (LLMs) have shown promising results in Named Entity Extraction (NEE) tasks compared to traditional methods. LLMs, such as BERT, can be fine-tuned for NEE and have been found to perform well by classifying individual tokens or spans of tokens into specific NE categories. In contrast, traditional methods often rely on handcrafted rules or features to identify entities in text.
One advantage of LLMs is their ability to capture complex patterns and relationships within the text data through pre-training on vast amounts of text. This allows them to generalize better across different domains and languages without requiring extensive manual feature engineering. Additionally, LLMs can adapt quickly to new tasks through fine-tuning on smaller datasets, making them versatile for various NLP applications.
However, it's essential to consider factors like model size, computational resources required for training and inference, as well as interpretability when comparing LLMs with traditional methods. While LLMs offer state-of-the-art performance in many NLP tasks including NEE, they may not always be the most efficient choice depending on the specific requirements of a project.

How do OCR errors impact the effectiveness of language models in spell correction?

OCR errors can significantly impact the effectiveness of language models in spell correction tasks. When Optical Character Recognition (OCR) is used to convert images containing text into machine-readable format, errors such as misrecognitions or substitutions may occur due to variations in fonts, image quality issues, or noise present in the input data.
For language models trained on clean textual data but applied to OCR-generated text with errors, these inaccuracies can lead to incorrect predictions during spell correction processes. The presence of OCR errors introduces noise into the input text that may hinder an LM's ability to accurately correct misspelled words or phrases.
In scenarios where OCR errors are prevalent, fine-tuning a language model specifically on data with synthetic OCR corruptions could help improve its robustness against such inaccuracies. By exposing the LM during training to similar error patterns encountered during OCR processing, it can learn more effectively how to handle and rectify these types of mistakes.

How might different prompts impact the performance of fine-tuned language models?

The choice of prompt plays a crucial role in determining how well a fine-tuned language model performs on a given task. The prompt serves as an instruction guiding the LM towards generating appropriate responses based on context provided before it.
Different prompts can influence several aspects affecting an LM's performance:

Task-specific Guidance: A well-crafted prompt should provide clear instructions relevant to the target task while ensuring consistency across different inputs.

Information Completeness: Prompts need sufficient information from which an LM generates accurate responses; incomplete prompts might lead
-to ambiguity resulting inaccurate outputs.

3 .Prompt Structure: The structure and format should align with tokenization schemes used by specific LMs; inconsistencies could affect token alignment
leading erroneous interpretations.
4 .Domain Adaptation: Domain-specific prompts tailored towards unique characteristics ensure better adaptation improving overall accuracy
5 .Fine-Tuning Efficiency: Optimal prompts facilitate faster convergence during fine-tuning process enhancing efficiency
By carefully designing prompts that cater directly -to task requirements while considering nuances related -to specific Language Models' capabilities , one can enhance overall performance significantly