F i RC - NLP at S em E val-2025 Task 3: Exploring Prompting Approaches for Detecting Hallucinations in LLM s (2025)

Abstract: This paper presents a system description forthe SemEval Mu-SHROOM task, focusing ondetecting hallucination spans in the outputsof instruction-tuned Large Language Models(LLMs) across 14 languages. We comparetwo distinct approaches: Prompt-Based Ap-proach (PBA), which leverages the capabilityof LLMs to detect hallucination spans usingdifferent prompting strategies, and the Fine-Tuning-Based Approach (FBA), which fine-tunes pre-trained Language Models (LMs) toextract hallucination spans in a supervised man-ner. Our experiments reveal that PBA, espe-cially when incorporating explicit references orexternal knowledge, outperforms FBA. How-ever, the effectiveness of PBA varies across lan-guages, likely due to differences in languagerepresentation within LLMs

## introduction large language models (llms) have brought advancements to many areas of nlp, including natural language understanding, natural language generation, and reasoning tasks @xcite @xcite . broadly speaking, llms such as @xcite and @xcite are based on transformer models and are trained on vast amounts of internet text to understand and generate human language in a coherent and contextually relevant manner @xcite . the increasing scale of training data and model capacity has enabled llms to exhibit emergent capabilities such as chain-of-thought reasoning, instruction following, and in-context learning @xcite @xcite . currently, natural language generation (nlg) faces a major challenge: large language models (llms) can produce text that is fluent and coherent † these authors contributed equally to this work. but contains factual inaccuracies or statements ungrounded in reality-a phenomenon known as hallucination @xcite . these hallucinations are difficult to detect automatically because existing evaluation methods primarily measure fluency rather than accuracy. detecting hallucinations is often the first step in ensuring that a model's output is consistent with known facts and in preventing the generation of misleading or false information @xcite . in many nlg applications, such as question-answering and translation tasks, the correctness of the model's output is crucial for its utility. the semeval mu-shroom task focuses on detecting hallucination spans in the outputs of instruction-tuned llms across 14 languages @xcite . the main goal of the task is to determine which spans of a given text produced by an llm are part of a hallucination. the organizers provide the llm output as a string of characters, a list of tokens, and a list of logits. participants are required to compute the probability of hallucination for each character in the llmgenerated text. submissions are evaluated using two approaches: (1) the intersection-over-union of characters marked as hallucinations in the gold reference and the predicted output, and (2) the correlation between the probability assigned by the participants' system that a character is part of a hallucination and the empirical probabilities observed from annotators. we explore two distinct approaches to address the mu-shroom task: the prompt-based approach (pba) and the fine-tuning-based approach (fba). in the prompt-based approach, we experiment with different strategies. first, we explore prompting an instruction-tuned model without a reference by providing only the question and the model's answer. for this, we use @xcite to identify hallucination spans by providing the input text and the model's output in the second strategy, which we refer to as the dual-prompt approach, we break down the prompt into two phases. in the first phase, we prompt the model to answer the question explicitly. in the second phase, we use the output from the first phase as a reference answer and prompt the model to identify hallucinations by comparing this reference with the model's original output. in the third approach, we incorporate external knowledge to create a reference context and prompt the model to answer the question based on this reference. for the fine-tuning-based approach, we experiment with an encoder model by framing the task as a token classification task. we describe the details in section 3.1. in summary, our experiment shows that: ## background hallucination is one of the main limitations of nlg models, where the generated text sounds fluent and coherent but contains factual inaccuracies or statements ungrounded in reality @xcite . hallucinations in nlg models can take two forms: intrinsic hallucinations and extrinsic hallucinations @xcite . in the case of intrinsic hallucinations, there is a contradiction between the source text and the generated text. since this contradiction appears in one or more spans, it is possible to verify where the hallucination occurred. in contrast, extrinsic hallucinations do not exhibit an observable contradiction between the source and the generated text, making it impossible to pinpoint the hallucination. in the extrinsic case, there is no available evidence in the input text to determine the correctness or incorrectness of the generated text. ## system description in this section, we describe our proposed system. we employ two distinct approaches: the prompt-based approach (pba) and the fine-tuning-based approach (fba). in the prompt-based method, we use an llm as a proxy model and apply a standard prompt to identify hallucination spans by providing pairs of input text and model output. in the finetuning approach, we fine-tune an encoder model by framing the task as a token classification problem. ## analysis and conclusion in this section, we compare the strengths and limitations of the four proposed approaches.