Large language models (LLMs) are widely used tools that assist us by answering various questions. Humans implicitly use contrast as a natural way to think about and seek explanations (i.e., "Why A and not B?"). Explainability is a challenging aspect of LLMs, as we do not truly understand how good the LLM answers are. The challenge is understanding to what extent LLMs can generate effective contrastive self-explanations for users. We introduce the Contrastive Self-Explanation Method (CoSEM) to narrow the gap between LLMs and explainability. It generates contrastive self- explanations and evaluates them through automation and a user study on generality, usefulness, readability, and relevance. Our results indicate that LLMs are capable of generating effective contrastive self-explanations. Lexical analysis of contrastive explanation indicates that explanations are not less general than the text those explain, and semantic analysis shows that more complex models generalize self-explanations more consistently. Although it is challenging to evaluate contrast in self-explanations semantically, user study shows that some models (Llama3-8B) help understand the contrast. Moreover, task selection affects how readable users find the explanations, where more self-explanations on general topics (movie reviews) are more readable than more specific topics (medical diagnoses). Lastly, some models, such as Llama3-8B, excel at generating contrastive self-explanations that contain relevant information regarding input text.

Spending time in front of screens has become an inescapable activity, which might be interrupted by unrelated external causes. While automatic approaches to identify mind-wandering (MW) have already been investigated, past research was done with self-reports or physiological data. This work explores automated detection utilizing solely facial expressions from Mementos data, which comes in the form of webcam recordings, where participants react to music videos. The recordings are annotated with labels indicating perceived MW. Video responses are turned into time series by first extracting facial characteristics, which are encoded with Facial Action Coding System (FACS). Temporal information is represented with 170 temporal features. Classification is conducted with support vector machines (SVM) through a data-level approach and an algorithm-level approach, first by synthesizing data and second by adding class weights to SVM. Both approaches are evaluated with metric scores insensitive to imbalanced data. On average,
results show that detection performs marginally better than by chance. However, the evaluation metric values vary across multiple classification runs, thus the prospect of using the Mementos dataset for automatic MW detection based on only facial expressions is not promising.