The prevalent use of large language models (LLMs) in various domains has drawn attention to the issue of "hallucination," which refers to instances where LLMs generate factually inaccurate or ungrounded information. Existing techniques for hallucination detection in language assistants rely on intricate fuzzy, specific free-language-based chain of thought (CoT) techniques or parameter-based methods that suffer from interpretability issues. Additionally, the methods that identify hallucinations post-generation could not prevent their occurrence and suffer from inconsistent performance due to the influence of the instruction format and model style. In this paper, we introduce a novel pre-detection self-evaluation technique, referred to as SELF-FAMILIARITY, which focuses on evaluating the model's familiarity with the concepts present in the input instruction and withholding the generation of response in case of unfamiliar concepts. This approach emulates the human ability to refrain from responding to unfamiliar topics, thus reducing hallucinations. We validate SELF-FAMILIARITY across four different large language models, demonstrating consistently superior performance compared to existing techniques. Our findings propose a significant shift towards preemptive strategies for hallucination mitigation in LLM assistants, promising improvements in reliability, applicability, and interpretability.

A supervised text classification model relies on labelled datasets to train the model (Sebastiani, 2002). From an experimental perspective, the design and evaluation of classification models typically rely on data pertaining to fixed periods of time. Recent research demonstrates that such models, while showing competitive performance in their experimental environment, underperform when they need to classify new data that is distant in time from that observed during training (Alkhalifa and Zubiaga, 2022). This deterioration of performance has been demonstrated for different classification tasks, including topic classification (Rocha, Mourão, Pereira, Gonçalves, and Meira, 2008), sentiment classification (Lukes and Søgaard, 2018), hate speech detection (Florio, Basile, Polignano, Basile, and Patti, 2020), stance detection (Alkhalifa, Kochkina, and Zubiaga, 2021) and political ideology detection (Röttger and Pierrehumbert, 2021). This performance drop can happen for multiple reasons, including among others the evolution in language use (Smith, 2004) or the evolution of public opinion (Bonilla and Mo, 2019) and its extent may vary (Alkhalifa et al., 2021). This poses an important challenge and limitation on such models when one plans to continue using the model over a long period of time to classify new, incoming data, as can be the case with a stream of user-generated contents (Cheng, Chen, Lee, and Li, 2021).