Simulating Emotional Intelligence in LLMs through Behavioral Conditioning and Analogical Retrieval

AbstractHuman 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