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Found 5 papers, 0 papers with code
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.
SED: Self-Evaluation Decoding Enhances Large Language Models for Better Generation
Existing Large Language Models (LLMs) generate text through unidirectional autoregressive decoding methods to respond to various user queries.
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.
ToNER: Type-oriented Named Entity Recognition with Generative Language Model
In recent years, the fine-tuned generative models have been proven more powerful than the previous tagging-based or span-based models on named entity recognition (NER) task.
Reason from Fallacy: Enhancing Large Language Models' Logical Reasoning through Logical Fallacy Understanding
Large Language Models (LLMs) have demonstrated good performance in many reasoning tasks, but they still struggle with some complicated reasoning tasks including logical reasoning.
