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Tokenization Matters! Degrading Large Language Models through Challenging Their Tokenization
Our empirical results reveal that our ADT is highly effective on challenging the tokenization of leading LLMs, including GPT-4o, Llama-3, Qwen2. 5-max and so on, thus degrading these LLMs' capabilities.
P-ICL: Point In-Context Learning for Named Entity Recognition with Large Language Models
To obtain optimal point entities for prompting LLMs, we also proposed a point entity selection method based on K-Means clustering.
Improving Recall of Large Language Models: A Model Collaboration Approach for Relational Triple Extraction
The framework includes an evaluation model that can extract related entity pairs with high precision.
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