A . M . P at S ci H al2025: Automated Hallucination Detection in Scientific Content via LLM s and Prompt Engineering (2025)

Abstract: This paper presents our system developed for SciHal2025: Hallucination Detection for Scientific Content. The primary goal of this task is to detect hallucinated claims based on the corresponding reference. Our methodology leverages strategic prompt engineering to enhance LLMs’ ability to accurately distinguish between factual assertions and hallucinations in scientific contexts. Moreover, we discovered that aggregating the fine-grained classification results from the more complex subtask (subtask 2) into the simplified label set required for the simpler subtask (subtask 1) significantly improved performance compared to direct classification for subtask 1. This work contributes to the development of more reliable AI-powered research tools by providing a systematic framework for hallucination detection in scientific content.

## introduction nowadays, the rapid advancement of generative ai has revolutionized academic research practices, introducing ai-powered research assistants capable of synthesizing information and responding to complex scientific queries @xcite . these systems leverage large language models (llms) such as llama @xcite and deepseek @xcite to generate highly accurate answers in a very fast time. although these tools offer efficiency in knowledge synthesis, they face a critical challenge: models generate text that sounds correct but is actually false or made up, this problem is called hallucination @xcite . hallucinations in scientific content are particularly problematic as they can propagate misinformation, undermine research integrity, and lead to flawed scientific conclusions. given these problems, the scihal2025 tasks focus on the detection of hallucination from the claim that is extracted from the answer of llm. in this paper, we present a methodology that combines stateof-the-art language models with advanced prompt engineering techniques to identify and classify different types of hallucination. ## system overview in this section, we describe the system in detail. we first noticed that the claim and reference contained quite a few encoding errors, so we used the ftfy library to fix these encoding errors. we then performed prompt engineering on large language models to make predictions. in addition, we discovered a simple, efficient two-step method that yields better results. ## experimental setup we used large language models through apis, which allow us to make predictions quickly and test multiple methods without the need for powerful hardware. however, sometimes due to network errors, we have to find specific patterns to re-predict, which costs a bit of money. for gemini models, we cast the output to json format so that the output has a specific format and we can process the output more easily. also, we leave the temperature coefficient as 0 so that the model gives the highest probability result. gemini's api documents are available at google ai studio foot_2 . for openai models, we cannot set the temperature coefficient category precision recall f1-score or cast the output, but instead we can adjust the reasoning_effort coefficient to "high" to make the model think more carefully before giving the final answer. openai api documentation can be found at openai platform foot_3 . for all models, i set my lucky random seed to 13 so that each run of the models gives the same results. ## results based on ## conclusion in this paper, we introduced a good system to detect various types of hallucinations produced by llms. the key point of our system is to design an optimal prompt with the following components: role and task definition, class explanation, examples, and output concepts so that the model can understand the concept and make accurate predictions. in addition, we introduce a two-step method to make efficient and fast predictions for both subtasks. in summary, the a.m.p system was competitive with the other systems submitted for evaluation.