title: TinyEmo: Scaling down Emotional Reasoning via Metric Projection
summary: This paper introduces TinyEmo, a family of small multi-modal language models
  for emotional reasoning and classification. Our approach features: (1) a
  synthetic emotional instruct dataset for both pre-training and fine-tuning
  stages, (2) a Metric Projector that delegates classification from the language
  model allowing for more efficient training and inference, (3) a multi-modal
  large language model (MM-LLM) for emotional reasoning, and (4) a semi-automated
  framework for bias detection. TinyEmo is able to perform emotion classification
  and emotional reasoning, all while using substantially fewer parameters than
  comparable models. This efficiency allows us to freely incorporate more diverse
  emotional datasets, enabling strong performance on classification tasks, with
  our smallest model (700M parameters) outperforming larger state-of-the-art
  models based on general-purpose MM-LLMs with over 7B parameters. Additionally,
  the Metric Projector allows for interpretability and indirect bias detection in
  large models without additional training, offering an approach to understand
  and improve AI systems.
    We release code, models, and dataset at https://github.com/ggcr/TinyEmo
paper_authors._id: 6707742995f90bdb8e9b3ed9
paper_authors.name: Cristian Gutierrez
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-10T08:00:04.297Z
paper_authors._id: 64da082a79c87e13ca232042
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64da082a79c87e13ca232042/DKGEIffK3kLj7SQ20yYiK.png
paper_authors.fullname: Cristian Gutiérrez
paper_authors.type: user
paper_authors.user: ggcristian
paper_title: TinyEmo: Scaling down Emotional Reasoning via Metric Projection
paper_summary: This paper introduces TinyEmo, a family of small multi-modal language models
  for emotional reasoning and classification. Our approach features: (1) a
  synthetic emotional instruct dataset for both pre-training and fine-tuning
  stages, (2) a Metric Projector that delegates classification from the language
  model allowing for more efficient training and inference, (3) a multi-modal
  large language model (MM-LLM) for emotional reasoning, and (4) a semi-automated
  framework for bias detection. TinyEmo is able to perform emotion classification
  and emotional reasoning, all while using substantially fewer parameters than
  comparable models. This efficiency allows us to freely incorporate more diverse
  emotional datasets, enabling strong performance on classification tasks, with
  our smallest model (700M parameters) outperforming larger state-of-the-art
  models based on general-purpose MM-LLMs with over 7B parameters. Additionally,
  the Metric Projector allows for interpretability and indirect bias detection in
  large models without additional training, offering an approach to understand
  and improve AI systems.
    We release code, models, and dataset at https://github.com/ggcr/TinyEmo
paper_ai_keywords: multi-modal language models emotional reasoning emotional classification synthetic emotional instruct dataset Metric Projector multi-modal large language model (MM-LLM) semi-automated framework bias detection interpretability indirect bias detection