This report describes our PromptShots submissions to a shared task on Evaluating the Rationales of Amateur Investors (ERAI). We participated in both pairwise comparison and unsupervised ranking tasks. For pairwise comparison, we employed instruction-based models based on T5-small and OpenAI InstructGPT language models. Surprisingly, we observed OpenAI InstructGPT language model few-shot trained on Chinese data works best in our submissions, ranking 3rd on the maximal loss (ML) pairwise accuracy. This model works better than training on the Google translated English data by a large margin, where the English few-shot trained InstructGPT model even performs worse than an instruction-based T5-small model finetuned on the English data. However, all instruction-based submissions do not perform well on the maximal potential profit (MPP) pairwise accuracy where there are more data and learning signals. The Chinese few-shot trained InstructGPT model still performs best in our setting. For unsupervised ranking, we utilized many language models, including many financial-specific ones, and Bayesian lexicons unsupervised-learned on both Chinese and English words using a method-of-moments estimator. All our submissions rank best in the MPP ranking, from 1st to 3rd. However, they all do not perform well for ML scoring. Therefore, both MPP and ML scores need different treatments since we treated MPP and ML using the same formula. Our only difference is the treatment of market sentiment lexicons.

该报告描述了作者参与了ERAI竞赛，并以很大的兴趣使用基于T5-small和OpenAI InstructGPT语言模型的指令模型进行了两组比较。并使用贝叶斯词汇无监督学习的语言模型进行了无监督的排名。在使用市场情绪词汇时，我们需要对ML和MPP打分进行不同的处理。

FinNLP-2022 ERAI 任务的 PromptShots：两两比较和无监督排名