用大模型+嵌入向量实现无监督反向词典，效果优于有监督方法。
GEAR: A Simple GENERATE, EMBED, AVERAGE AND RANK Approach for Unsupervised Reverse Dictionary
- 结合大模型与嵌入模型生成候选词，再通过平均排序选出最佳答案。
- 在多个标准数据集上超越有监督基线，且更少过拟合。
- 适合做文本编码器评测或无障碍写作辅助系统开发。
反向词典（RD）任务是根据文本描述或词义定义，找出最相关的词或词组。有效的RD方法可应用于无障碍支持、翻译或写作辅助系统。此外，它也常用于评估不同粒度的文本编码器性能，因该任务需处理词、定义和句子级嵌入。本文提出一种简单高效的无监督RD方法，结合大语言模型与嵌入模型。尽管结构简单，该方法在多个经典数据集上表现优于有监督基线，且过拟合更少。我们还在不同词典上进行了实验，分析了风格、语域和目标受众对系统性能的影响。结果表明，未调优的嵌入模型整体表现远低于仅使用大模型的基线（技术性词典除外），但在融合方法中对性能提升至关重要。
原文摘要 · Abstract (English)
Reverse Dictionary (RD) is the task of obtaining the most relevant word or set of words given a textual description or dictionary definition. Effective RD methods have applications in accessibility, translation or writing support systems. Moreover, in NLP research we find RD to be used to benchmark text encoders at various granularities, as it often requires word, definition and sentence embeddings. In this paper, we propose a simple approach to RD that leverages LLMs in combination with embedding models. Despite its simplicity, this approach outperforms supervised baselines in well studied RD datasets, while also showing less over-fitting. We also conduct a number of experiments on different dictionaries and analyze how different styles, registers and target audiences impact the quality of RD systems. We conclude that, on average, untuned embeddings alone fare way below an LLM-only baseline (although they are competitive in highly technical dictionaries), but are crucial for boosting performance in combined methods.
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