F rench G ossip P rompts: Dataset For Prevention of Generating F rench Gossip Stories By LLM s (2024)

Abstract: The realm of Large Language Models (LLMs) is undergoing a continuous and dynamic transformation. These state-of-the-art LLMs showcase an impressive ability to craft narratives based on contextual cues, highlighting their skill in comprehending and producing text resembling human writing. However, there exists a potential risk: the potential inclination of LLMs to create gossips when prompted with specific contexts. These LLMs possess the capacity to generate stories rooted in the context provided by the prompts. Yet, this very capability carries a risk of generating gossips given the context as input. To mitigate this, we introduce a dataset named “French GossipPrompts” designed for identifying prompts that lead to the creation of gossipy content in the French language. This dataset employs binary classification, categorizing whether a given prompt generates gossip or not. The dataset comprises a total of 7253 individual prompts. We have developed classification models and achieved an accuracy of 89.95%.

## introduction large language models (llms) are pre-trained using extensive publicly accessible data. the primary objective is to refine these models to approximate human-level proficiency. llms excel in comprehension and can produce text resembling human language. these models demonstrate remarkable efficiency in storytelling. llms possess the ability to construct narratives by utilizing provided contextual cues. this remarkable aptitude for narrative generation holds substantial promise. for instance, marketing firms can leverage this feature to derive suggestions, concepts, and compelling storylines for their products. it's important to note that up to a certain threshold, models like chatgpt(openai, 2023), alongside † corresponding author other llms, are imbued with ethical considerations during their training. however, a potential risk exists due to the models' capacity to produce narratives based on user inputs. as an illustration, when prompted with "heiley bieber's involvement in a marital affair with her husband justin bieber," chatgpt's response demonstrates a focus on its intended purpose: "i'm here to provide assistance, not to create gossip." but for the prompt "generate a story on heiley bieber cheating on her husband justin bieber." generates a realistic story that heiley bieber cheated on justin bieber. this creates a potential risk of generating gossips which can be harmful as illustrated in figure 1 that llms can be manipulated by the prompters to generate gossip on the context given. motivation:according to ( spitale gpt-3 surpasses humans in spreading misinformation, particularly through language models (llms) that can be manipulated to generate realistic gossip stories. this poses a substantial risk of disseminating misleading information, with potential harm such as family issues arising from generating gossip about celebrities or individuals involved in affairs. the inherent risk of damaging reputations, especially for politicians and public figures, further highlights the threat associated with language models. to mitigate this, it is crucial to establish mechanisms for detecting malicious prompts emphasizing the need for responsible use of chat systems to prevent language models from being misused for harmful purposes. how can we prevent chatgpt from generating gossips? although openai has made significant ethical progress, it's not entirely foolproof. to tackle this, we propose a dataset with prompts that either generate gossip or not. by identifying the characteristics of gossip-related prompts, we can effectively stop llms from producing such content. the model trained on the dataset can be deployed in the chat systems and whenever prompter prompts that generate gossip prompt it can detect and stop producing content related to that particular prompt. our key contributions are as follows: deep learning aids in detecting false information. @xcite introduced a transformer model for spotting fake news. @xcite used contrastive learning with unlabeled data to improve fake news detectors. misinformation leverages social networks. @xcite created a graph neural network combining content and network gossip detection and generation of gossips from llms in the french language is still unexplored. ## experimental results and discussion frenchbert also stands out with a commendable accuracy of 88.41%, showcasing its effec-tiveness in comparison to roberta and bert. these results underscore the importance of accuracy in practical applications, and both gpt 3.5 and frenchbert demonstrate their proficiency in achieving high accuracy rates in the detection of french gossipprompts. overall, gpt-3.5 performed best interms of all metrics. error analysis: in cases of false positives, where non-gossip prompts are mistakenly identified as gossip, common factors are ambiguous language or sarcastic tones that the model struggles to interpret accurately. the system could also be sensitive to certain keywords or phrases that are typically associated with gossip but are used in a non-gossip context. conversely, false negatives, where gossip prompts are inaccurately classified as non-gossip, share some common characteristics. one key factor could be the subtlety of gossip instances where the gossip is in coded language, euphemisms, or indirect references that the model fails to decipher. gossip that involves less common names, places, or events not well represented in the training data could also lead to false negatives. also the prompts which generated gossip for one and did not for other are tough to classify and the accuracy is around 61.37%. once deployed within chat systems, the trained machine learning model continually monitors each prompt's content. this proactive approach ensures that llms refrain from generating gossip stories. ## conclusion and future work we present a novel dataset designed for detecting prompts that produce gossip stories or narratives in french language, consisting of 7253 prompts. these prompts were penned down by humans and labeled with binary values. the labels are based on narratives generated by the language models (llms) we employed. the outcomes indicate that utilizing this dataset for training can mitigate the generation of gossip, which is particularly important as llms continue to advance. future endeavors will involve expanding this approach to encompass additional languages such as dutch, german, and more. also, exploring with reinforcement learning with continuous training from the users would help chatgpt like systems.