@inproceedings{reddy-etal-2025-simulating,
    title = "Simulating Emotional Intelligence in {LLM}s through Behavioral Conditioning and Analogical Retrieval",
    author = "Reddy, G.Sai Linisha  and
      Kankhara, Mounil Hiren  and
      Maheshwari, Mridul  and
      Bansal, Swayam  and
      Kapoor, Rishit  and
      M, Himesh Reddy  and
      Kumar, Bagesh",
    editor = "Rambelli, Giulia  and
      Ilievski, Filip  and
      Bolognesi, Marianna  and
      Sommerauer, Pia",
    booktitle = "Proceedings of the 2nd Workshop on Analogical Abstraction in Cognition, Perception, and Language (Analogy-Angle II)",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.analogyangle-1.7/",
    pages = "81--91",
    ISBN = "979-8-89176-274-9",
    abstract = "Human emotional expression emerges from a complex interplay of verbal, para-verbal, and non-verbal cues. This paper presents a dual-path framework for emotionally grounded text generation in large language models by integrating behavioral metadata with analogical retrieval. We introduce the MECC (Multimodal Emotionally Conditioned Corpus), a dataset of 1,764 question-answer pairs collected via structured interviews and annotated across 15 emotion categories with tone, response time, and body language. A LLaMA-3.1{--}8B{--}Instruct model is fine-tuned on MECC using behavior-encoded prompts, and inference is supported by a metadata-filtered Retrieval-Augmented Generation (RAG) pipeline. Detailed emotion-level analysis reveals trade-offs between emotional fidelity and semantic diversity, emphasizing the need for nuanced evaluation. This study contributes a richly annotated multimodal emotion corpus, a metadata-driven RAG architecture, a well-structured framework for building emotionally aware language models.Our code is available at https://github.com/MetaResearcher/Framework"
}