S murf C at at S em E val-2024 Task 6: Leveraging Synthetic Data for Hallucination Detection (2024)

Abstract: In this paper, we present our novel systems developed for the SemEval-2024 hallucination detection task. Our investigation spans a range of strategies to compare model predictions with reference standards, encompassing diverse baselines, the refinement of pre-trained encoders through supervised learning, and an ensemble approaches utilizing several high-performing models. Through these explorations, we introduce three distinct methods that exhibit strong performance metrics. To amplify our training data, we generate additional training samples from unlabelled training subset. Furthermore, we provide a detailed comparative analysis of our approaches. Notably, our premier method achieved a commendable 9th place in the competition’s model-agnostic track and 20th place in model-aware track, highlighting its effectiveness and potential.

## introduction large language models are proficient in generating human-like text across various styles. however, even the most advanced models can produce hallucinations, leading users to question their reliability. there are two primary types of hallucinations: factuality hallucinations, which involve the generation of content that deviates from actual facts, and faithfulness hallucinations, when the model fails to solve tasks correctly following specific instructions @xcite . the semeval 2024 shared-task on hallucinations and related observable overgeneration mistakes @xcite has integrated both types into three tasks. the definition modeling task (dm) focused on fact-related hallucinations by challenging models to generate contextually relevant word definitions. both the machine translation (mt) and paraphrase generation (pg) tasks included faithfulness hallucinations, with models asked to produce translations or paraphrases for given sentences. evaluation labelled datasets for these tasks were provided and the training dataset consisted only of source sentences and model generations, without corresponding labels. motivated by the lack of annotated resources and the efficacy of other language models trained on synthetic data, we developed two synthetic datasets that replicate the targeted domain. first, we collected data through a proprietary gpt-4 model @xcite , but our methods trained on the achieved data did not yield the desired results as prompt engineering made maintaining the domain challenging. as a second approach, we trained llama2-7b @xcite adapters using a small set of annotated examples and applied them to the unlabeled training data. this method proved to be a more effective form of in-domain data augmentation. while the competition was run on two tracks, we focus mainly on the model-agnostic track. in our methods we utilized the most effective models with varied sizes and architectures, which we had evaluated beforehand. our experiments involved fine-tuning a pre-trained embedding model, repurposing it to function as a binary classifier across a number of open-source datasets, including our synthetic sets. we also experimented with a promising method for evaluating paraphrases by modifying its design and fine-tuning the model on different data. finally, we tested different combinations of the highest-performing approaches in an ensemble setting. generated synthetic data and code published on github foot_0 . ## related work in the field of text representation, the e5 @xcite family represents a group of cuttingedge sentence embedding models trained through contrastive methods. the e5-mistral foot_1 model, a powerful embedding model that has been finetuned on a selection of annotated data, is currently recognized as the leading open-source model by the multitask text embedding benchmark @xcite . vectara's hallucination_detection_model foot_2 is a fine-tuned deberta focused on summarization datasets that includes annotations for factual consistency. trueteacher @xcite is a family of models and an associated dataset designed for evaluating factual consistency. the dataset was created by first fine-tuning various-sized t5 models on summarization tasks. these models were then employed to generate hypotheses, which were subsequently automatically annotated using a 540b large language model (llm). this annotated dataset was then utilized to train multiple models to assess factual consistency. the mutual implication score (mis) @xcite is a metric devised for evaluating the quality of text style transfer and paraphrasing systems, grounding its assessment on content similarity between the prediction and the reference text. it leverages a roberta-nli @xcite ) model that has been fine-tuned and incorporates it into an architecture that processes two input texts sequentially in both forward and reverse directions. the final hidden states from these two passes are merged and forwarded to a classification head to determine the mis score. initially, the mis metric was trained using the quora question pairs dataset (qqp) @xcite . simcse (similarity-based contrastive selfsupervised learning) @xcite is a selfsupervised learning method for text embeddings. it is used for creating embeddings of text data that are semantically meaningful and can be used in various downstream tasks. it involves training a neural network to maximize the similarity between embeddings of similar sentences and minimize the similarity between embeddings of dissimilar sentences. labse (language-agnostic bert sentence embedding) @xcite is a method for generating multilingual sentence embeddings using the bert architecture. other metrics for evaluating content preservation, such as bleu (bilingual evaluation understudy) @xcite , chrf (character n-gram f-score) @xcite , meteor (metric for evaluation of translation with explicit ordering) @xcite , and bleurt (bilingual evaluation understudy for ranking and tuning) @xcite , also stand out. bleu utilizes a modified unigram precision score, chrf evaluates the quality of machine translation by comparing character n-grams in candidate translations against reference translations to compute an f-score, meteor calculates the harmonic mean of precision and recall at the single-word level, and bleurt employs a fine-tuned bert model in a cross-encoder setup, using synthetic data to assess semantic similarity. ## results the comparative analysis of the performance across all baselines, our proposed methods, and the leading approaches derived from the official rankings is collated in ## conclusion we conducted a comparative analysis involving six baseline models (mis, e5-mistral, vectara, trueteacher, gpt-4, and the official baseline from the participant kit) alongside four sophisticated approaches (voting and normalized averaging in ensembling, as well as the refined mis and sft e5-mistral). of all methods evaluated, ensembling demonstrated the highest performance. nonetheless, the refined mis and the sft e5-mistral exhibited only a minor shortfall in performance when compared to these leading methodologies. indeed, there appear to be several avenues for enhancing our synthetic data to potentially exceed the performance of other methods: read the source sentence and the paraphrased hypothesis and answer whether there are any hallucinations or related observable overgeneration errors for the paraphrasing task. before answering, think step by step and write why you chose the answer you did. answer the last string with 'the hypothesis is correct' if there are no hallucinations or misgenerations. otherwise, answer with 'the hypothesis is false'. example 1: source sentence: "the european parliament does not approve the budget." paraphrased hypothesis: "the budget cannot be adopted against the will of the european parliament." the hypothesis is false example 2: source sentence: "everyone is capable of enjoying a good education in a society." paraphrased hypothesis: "we must create a society where everyone is able to enjoy a good education." the hypothesis is correct