迈向用于情感检测与推理的生成式方法
Towards a Generative Approach for Emotion Detection and Reasoning
AI总结：
本文提出将情感分析建模为生成式QA任务，通过LLM先生成背景知识再逐步推理，实现零样本情感检测与情感推理的联合处理。
摘要：
大型语言模型（LLM）已通过思维链（CoT）提示技术在数学和常识推理任务中展现出令人瞩目的性能。但是，它们能否通过在输入提示后拼接“让我们一步一步地思考”来进行情感推理呢？本文研究了这一问题，并介绍了一种利用LLM进行零样本情感检测和情感推理的新方法。现有最先进的零样本方法依赖文本蕴含模型来为输入文本选择最合适的情感标签。我们认为，这将模型严格限制在一组固定标签上，而在许多需要情感分析的应用中，这些标签可能并不合适或并不充分。相反，我们提出将情感分析问题构建为生成式问答（QA）任务。我们的方法采用两步流程：生成相关上下文或背景知识，以逐步回答情感检测问题。本文是首个使用生成式方法联合处理文本情感检测与情感推理任务的工作。我们在两个常用的情感检测数据集上评估了该方法，并发布了细粒度情感标签和解释，以用于情感推理系统的进一步训练和微调。
英文摘要：
Large language models (LLMs) have demonstrated impressive performance in mathematical and commonsense reasoning tasks using chain-of-thought (CoT) prompting techniques. But can they perform emotional reasoning by concatenating `Let's think step-by-step' to the input prompt? In this paper we investigate this question along with introducing a novel approach to zero-shot emotion detection and emotional reasoning using LLMs. Existing state of the art zero-shot approaches rely on textual entailment models to choose the most appropriate emotion label for an input text. We argue that this strongly restricts the model to a fixed set of labels which may not be suitable or sufficient for many applications where emotion analysis is required. Instead, we propose framing the problem of emotion analysis as a generative question-answering (QA) task. Our approach uses a two step methodology of generating relevant context or background knowledge to answer the emotion detection question step-by-step. Our paper is the first work on using a generative approach to jointly address the tasks of emotion detection and emotional reasoning for texts. We evaluate our approach on two popular emotion detection datasets and also release the fine-grained emotion labels and explanations for further training and fine-tuning of emotional reasoning systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
