基于推理注入的大型方面基于情感分析
Large-Scale Aspect-Based Sentiment Analysis with Reasoning-Infused LLMs
AI总结：
Arctic-ABSA通过引入推理注入技术，提升了大型多语言方面情感分析模型的准确性和泛化能力，实现了在多个领域和语言上的高性能表现。
摘要：
我们介绍了Arctic-ABSA，一组强大的模型用于现实中的基于方面的情感分析（ABSA）。我们的模型针对商业需求进行了定制，使用大量的公开数据和精心生成的合成数据进行训练，最终得到的数据集比SemEval14大20倍。我们通过扩展情感类别数量，从标准的三个（积极、消极、中性）扩展到五个，增加混合和未知类别，同时联合预测整体文本情感并支持多种语言。我们通过在Chain-of-Thought（CoT）示例上进行微调来实验推理注入，并引入了一种新的推理预训练技术，用于仅编码器的模型，显著提高了下游微调和泛化能力。我们的39500万参数编码器和80亿参数解码器在准确性上比GPT-4o和Claude 3.5 Sonnet高10个百分点，并在SemEval14基准上设定了新的最佳结果。一个单一的多语言模型在六种语言上保持87-91%的准确性，而不影响英语性能。我们发布了ABSA-mix，一个大规模基准，汇集了17个公开的ABSA数据集，涵盖92个领域。
英文摘要：
We introduce Arctic-ABSA, a collection of powerful models for real-life aspect-based sentiment analysis (ABSA). Our models are tailored to commercial needs, trained on a large corpus of public data alongside carefully generated synthetic data, resulting in a dataset 20 times larger than SemEval14. We extend typical ABSA models by expanding the number of sentiment classes from the standard three (positive, negative, neutral) to five, adding mixed and unknown classes, while also jointly predicting overall text sentiment and supporting multiple languages. We experiment with reasoning injection by fine-tuning on Chain-of-Thought (CoT) examples and introduce a novel reasoning pretraining technique for encoder-only models that significantly improves downstream fine-tuning and generalization. Our 395M-parameter encoder and 8B-parameter decoder achieve up to 10 percentage points higher accuracy than GPT-4o and Claude 3.5 Sonnet, while setting new state-of-the-art results on the SemEval14 benchmark. A single multilingual model maintains 87-91% accuracy across six languages without degrading English performance. We release ABSA-mix, a large-scale benchmark aggregating 17 public ABSA datasets across 92 domains.
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