Can Large Language Models Generate Effective Datasets for Emotion Recognition in Conversations?
This addresses data scarcity and bias issues for researchers and practitioners in emotion recognition, though it is incremental as it builds on existing LLM capabilities for data generation.
The paper tackled the scarcity and bias in emotion recognition in conversations (ERC) datasets by using a small, resource-efficient large language model to generate six novel datasets, which when used to train ERC classifiers led to statistically significant performance improvements on existing benchmarks.
Emotion recognition in conversations (ERC) focuses on identifying emotion shifts within interactions, representing a significant step toward advancing machine intelligence. However, ERC data remains scarce, and existing datasets face numerous challenges due to their highly biased sources and the inherent subjectivity of soft labels. Even though Large Language Models (LLMs) have demonstrated their quality in many affective tasks, they are typically expensive to train, and their application to ERC tasks--particularly in data generation--remains limited. To address these challenges, we employ a small, resource-efficient, and general-purpose LLM to synthesize ERC datasets with diverse properties, supplementing the three most widely used ERC benchmarks. We generate six novel datasets, with two tailored to enhance each benchmark. We evaluate the utility of these datasets to (1) supplement existing datasets for ERC classification, and (2) analyze the effects of label imbalance in ERC. Our experimental results indicate that ERC classifier models trained on the generated datasets exhibit strong robustness and consistently achieve statistically significant performance improvements on existing ERC benchmarks.