Summary of Improving Few-shot Cross-domain Named Entity Recognition by Instruction Tuning a Word-embedding Based Retrieval Augmented Large Language Model, By Subhadip Nandi and Neeraj Agrawal
Improving Few-Shot Cross-Domain Named Entity Recognition by Instruction Tuning a Word-Embedding based Retrieval Augmented Large Language Model
by Subhadip Nandi, Neeraj Agrawal
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach, IF-WRANER, is a retrieval-augmented large language model that leverages pre-trained language models for few-shot cross-domain named entity recognition (NER). By finetuning the model with regularization techniques and using word-level embeddings during retrieval, IF-WRANER outperforms previous state-of-the-art approaches on the CrossNER dataset. The approach has been successfully deployed in multiple customer care domains of an enterprise, resulting in a 15% reduction in escalations to human agents and millions of dollars in yearly savings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Few-Shot Cross-Domain NER is about using knowledge from one place to recognize things in another. Most current approaches use pre-trained language models, but these models are often specific to the first place. To make them work for a new place, you need to change the model or train it with data from that new place. Both of these methods mean creating a brand new NER model for each new place, which is not practical. Recently, some people have tried using large language models to solve this problem, but most of those approaches are either too expensive or don’t work well. In this paper, the authors propose IF-WRANER, a new approach that uses a retrieval-augmented large language model finetuned for NER. This approach is able to perform better than previous state-of-the-art methods and has been successfully used in real-world scenarios. |
Keywords
» Artificial intelligence » Few shot » Large language model » Named entity recognition » Ner » Regularization
