Large Language Models (LLMs) have demonstrated remarkable capability in simulating human behaviors, personality, and language. Such synthetic agents with personalities are considered as cost-effective proxies for real users to facilitate crowd-sourcing efforts like annotations, surveys, and A/B testing. Accordingly, it is imperative to validate knowledge awareness of these LLM persona agents when they are customized for further usage. Currently, there is no established way for such evaluation and appropriate mitigation. In this work, we propose a generic evaluation approach to validate LLM based persona agents for correctness, relevance, and diversity in the context of self-awareness and domain knowledge.We evaluate the efficacy of this framework using three LLMs ( Llama, GPT-4o, and Gemma) for domains such as air travel, gaming, and fitness. We also experiment with advanced prompting strategies such as ReAct and Reflexion. We find that though GPT-4o and Llama demonstrate comparable performance, they fail some of basic consistency checks under certain perturbations.

This paper outlines our approach to the StanceEval 2024- Arabic Stance Evaluation shared task. The goal of the task was to identify the stance, one out of three (Favor, Against or None) towards tweets based on three topics, namely- COVID-19 Vaccine, Digital Transformation and Women Empowerment. Our approach consists of fine-tuning BERT-based models efficiently for both, Single-Task Learning as well as Multi-Task Learning, the details of which are discussed. Finally, an ensemble was implemented on the best-performing models to maximize overall performance. We achieved a macro F1 score of 78.02% in this shared task. Our codebase is available publicly.

This paper outlines our approach for the ERC subtask of the SemEval 2024 EdiREF Shared Task. In this sub-task, an emotion had to be assigned to an utterance which was the part of a dialogue. The utterance had to be classified into one of the following classes- disgust, contempt, anger, neutral, joy, sadness, fear, surprise. Our proposed system makes use of an ensemble of language specific RoBERTA and BERT models to tackle the problem. A weighted F1-score of 44% was achieved by our system in this task. We conducted comprehensive ablations and suggested directions of future work. Our codebase is available publicly.