Byun at S em E val-2024 Task 6: Text Classification on Hallucinating Text with Simple Data Augmentation (2024)

Abstract: This paper aims to classify sentences to see if it is hallucinating, meaning the generative language model has output text that has very little to do with the user’s input, or not. This classification task is part of the Semeval 2024’s task on Hallucinations and Related Observable Over-generation Mistakes, AKA SHROOM, which aims to improve awkward-sounding texts generated by AI. This paper will first go over the first attempt at creating predictions, then show the actual scores achieved after submitting the first attempt results to Semeval, then finally go over potential improvements to be made.

## introduction how ai and large language models are able to understand and generate human language is elusive, to say the least. the underlying ingenuity behind the architecture of such models, involves technology and methods that are considered a black box like neural networks, named after the fact that the inner workings are impossible to grasp and properly digest for even experts @xcite . but language models are far from perfect, to the point where sometimes, text generated by complex natural language generation models are considered to be hallucinating. the term hallucination here refers to text that has been generated or processed to solve tasks like machine translation or natural language generation, that are easily subject to the issue of being grammatically correct but being untethered from the user's input or the source material @xcite . this paper attempts to classify these hallucinating texts using different models and methods, in order to see which can get the best results in terms of accuracy. ## task description semeval's task 6, hereinafter denoted as shroom, asks participants to successfully classify hallucination texts from non-hallucinating text, where each data has been annotated by 5 different annotators, where a majority vote is done to categorize each data point. going over the json input data presented in figure 1 , the "hyp" row refers to the text that has been generated/processed by a model, so the output. the model here could refer to something like bert, and can be seen as the "model" value. "src" refers to the user input or source material the model is working with in order to produce the output, and the "tgt" is the expected result that the model should be aiming for. in figure 1 , the model's "task" is "dm", or definition modeling. the model is expected to provide the definition for the word asked on the input. in this case input asks for the meaning of surmounting. the output of the model is "hyp", and the correct answer is "tgt". this output is put on a majority vote by 5 people, and finally the data in figure 1 was labeled as hallucinating, where the probability of 0.6 because 3 out of 5 people voted in favor of hallucinating. ## results refer to ## conclusion and limitations some potential improvements that could be employed are the following.