TinyEmo：通过度量投影缩减情感推理

TinyEmo: Scaling down Emotional Reasoning via Metric Projection

October 9, 2024

作者: Cristian Gutierrez

cs.AI

摘要

本文介绍了TinyEmo，这是一组用于情感推理和分类的小型多模态语言模型。我们的方法包括：(1) 用于预训练和微调阶段的合成情感指导数据集，(2) 度量投影器，将分类从语言模型中分离出来，从而实现更高效的训练和推理，(3) 用于情感推理的多模态大型语言模型（MM-LLM），以及(4) 用于偏见检测的半自动化框架。TinyEmo能够进行情感分类和情感推理，同时使用的参数比可比模型要少得多。这种高效性使我们能够自由地整合更多不同的情感数据集，在分类任务上表现出色，即使是我们最小的模型（700M参数）也能胜过基于拥有超过7B参数的通用MM-LLM的更大型先进模型。此外，度量投影器允许在大型模型中进行解释和间接偏见检测，无需额外训练，提供了一种理解和改进AI系统的方法。 我们在https://github.com/ggcr/TinyEmo发布了代码、模型和数据集。

English

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