Interest in leveraging Large Language Models (LLMs) for emotional support systems motivates the need to understand how these models comprehend and represent emotions internally. While recent works show the presence of emotion concepts in the hidden state representations, it’s unclear if the model has a robust representation that is consistent across different datasets. In this paper, we present a unified view to understand emotion representation in LLMs, experimenting with diverse datasets and prompts. We then evaluate the reasoning ability of the models on a complex emotion identification task. We find that LLMs have a common emotion representation in the later layers of the model, and the vectors capturing the direction of emotions extracted from these representations can be interchanged among datasets with minimal impact on performance. Our analysis of reasoning with Chain of Thought (CoT) prompting shows the limits of emotion comprehension. Therefore, despite LLMs implicitly having emotion representations, they are not equally skilled at reasoning with them in complex scenarios. This motivates the need for further research to find new approaches.

A NOTAM or NOtice To AirMen is a crucial notice for different aviation stakeholders, particularly flight crews. It delivers essential notifications about abnormal conditions of Aviation System components such as changes to facilities, hazards, service, procedure that are not known far enough in advance to be publicized through other means. NOTAM messages are short, contain acronyms, and look cryptic in most of the cases. Writing and understanding these messages put heavy cognitive load on its end users. In this work, we take up the task of translating NOTAMs into English natural language using LLMs. Since NOTAMs do not adhere to English grammar rules and have their own decoding rules, large language models (LLMs) cannot translate them without effective prompting. In this paper, we develop a framework to come up with effective prompts to achieve the translations. Our approach uses context-aware semantic prompting techniques, paired with domain-specific rules, to improve the accuracy and clarity of translations. The framework is evaluated using comprehensive experiments (6 LLMs of varying sizes, and with 5 different prompting setups for each) and eight evaluation metrics measuring different aspects of the translation. The results demonstrate that our methodology can produce clear translations that accurately convey the information contained in NOTAMs.