ChatEL: Entity Linking with Chatbots
Yifan Ding, Qingkai Zeng, Tim Weninger

TL;DR
ChatEL introduces a three-step prompting framework for Large Language Models to improve Entity Linking accuracy, outperforming traditional models and revealing potential label inaccuracies in datasets.
Contribution
The paper presents a novel three-step prompt-based framework, ChatEL, that enhances LLM-based entity linking performance across multiple datasets.
Findings
ChatEL improves average F1 by over 2% across 10 datasets.
Many ground truth labels are incorrect, with ChatEL's predictions often being correct.
The approach is open-source and easily applicable to various datasets.
Abstract
Entity Linking (EL) is an essential and challenging task in natural language processing that seeks to link some text representing an entity within a document or sentence with its corresponding entry in a dictionary or knowledge base. Most existing approaches focus on creating elaborate contextual models that look for clues the words surrounding the entity-text to help solve the linking problem. Although these fine-tuned language models tend to work, they can be unwieldy, difficult to train, and do not transfer well to other domains. Fortunately, Large Language Models (LLMs) like GPT provide a highly-advanced solution to the problems inherent in EL models, but simply naive prompts to LLMs do not work well. In the present work, we define ChatEL, which is a three-step framework to prompt LLMs to return accurate results. Overall the ChatEL framework improves the average F1 performance…
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Taxonomy
TopicsBlockchain Technology Applications and Security · Data Quality and Management · AI in Service Interactions
MethodsRefunds@Expedia|||How do I get a full refund from Expedia? · Cosine Annealing · Residual Connection · Linear Layer · Discriminative Fine-Tuning · Byte Pair Encoding · Linear Warmup With Cosine Annealing · Weight Decay · Dropout · Attention Dropout
