TM-TREK at SemEval-2024 Task 8: Towards LLM-Based Automatic Boundary Detection for Human-Machine Mixed Text

AbstractWith the increasing prevalence of text gener- ated by large language models (LLMs), there is a growing concern about distinguishing be- tween LLM-generated and human-written texts in order to prevent the misuse of LLMs, such as the dissemination of misleading information and academic dishonesty. Previous research has primarily focused on classifying text as ei- ther entirely human-written or LLM-generated, neglecting the detection of mixed texts that con- tain both types of content. This paper explores LLMs’ ability to identify boundaries in human- written and machine-generated mixed texts. We approach this task by transforming it into a to- ken classification problem and regard the label turning point as the boundary. Notably, our ensemble model of LLMs achieved first place in the ‘Human-Machine Mixed Text Detection’ sub-task of the SemEval’24 Competition Task 8. Additionally, we investigate factors that in- fluence the capability of LLMs in detecting boundaries within mixed texts, including the incorporation of extra layers on top of LLMs, combination of segmentation loss, and the im- pact of pretraining. Our findings aim to provide valuable insights for future research in this area.