This Candidate is . 基于提示词的情感提取与推荐信
This Candidate is [MASK]. Prompt-based Sentiment Extraction and Reference Letters
AI总结：
本文提出基于提示词的情感提取方法，利用预训练LLMs直接从文本提取情感，无需微调或标注数据。应用于推荐信语料库发现，情感得分与就业市场表现正相关，情感分散有负面影响，且性别化情感差异对女性不利。
摘要：
我提出了一种相对简单的方法来部署预训练大型语言模型（LLMs），以便从文本数据中提取情感和其他有用特征。我将该方法称为基于提示词的情感提取（prompt-based sentiment extraction），与经济学和金融学中使用的其他方法相比，它具有多重优势。特别是，它直接接受文本输入（无需预处理），并产生具有概率解释的情感得分。与其他基于LLM的方法不同，它不需要任何微调或标注数据。我将基于提示词的策略应用于手工收集的机密推荐信（RLs）语料库。我表明，RLs的情感内容清晰地反映在就业市场结果中。无论选择何种成功衡量标准，RLs中平均情感较高的候选人表现明显更好。此外，我表明推荐信作者之间的情感分散对就业市场候选人的表现产生负面影响。我将我的情感提取方法与其他常用的情感分析方法进行了比较：`词袋'方法、微调语言模型和查询高级聊天机器人。没有其他方法能够完全重现基于提示词的情感提取所获得的结果。最后，我对方法稍作修改以获得`性别化'情感得分（如Eberhardt et al., 2023）。我表明，为女性候选人撰写的RLs强调`苦干'性格特征，而男性候选人的信件强调`杰出'特征。这些性别差异对女性在就业市场的结果产生负面影响。
英文摘要：
I propose a relatively simple way to deploy pre-trained large language models (LLMs) in order to extract sentiment and other useful features from text data. The method, which I refer to as prompt-based sentiment extraction, offers multiple advantages over other methods used in economics and finance. In particular, it accepts the text input as is (without pre-processing) and produces a sentiment score that has a probability interpretation. Unlike other LLM-based approaches, it does not require any fine-tuning or labeled data. I apply my prompt-based strategy to a hand-collected corpus of confidential reference letters (RLs). I show that the sentiment contents of RLs are clearly reflected in job market outcomes. Candidates with higher average sentiment in their RLs perform markedly better regardless of the measure of success chosen. Moreover, I show that sentiment dispersion among letter writers negatively affects the job market candidate's performance. I compare my sentiment extraction approach to other commonly used methods for sentiment analysis: `bag-of-words' approaches, fine-tuned language models, and querying advanced chatbots. No other method can fully reproduce the results obtained by prompt-based sentiment extraction. Finally, I slightly modify the method to obtain `gendered' sentiment scores (as in Eberhardt et al., 2023). I show that RLs written for female candidates emphasize `grindstone' personality traits, whereas male candidates' letters emphasize `standout' traits. These gender differences negatively affect women's job market outcomes.
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。
