Scaling and instruction tuning increase sycophancy in LLMs on opinion and fact tasks, but a synthetic data fine-tuning intervention reduces it on held-out prompts.
S em E val-2017 T ask 4: Sentiment analysis in twitter
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Emoji-only models for financial sentiment on StockTwits achieve F1 scores of approximately 0.75 with high efficiency, while specific emojis and pairs predict market trends at over 90 percent accuracy, though combined text models perform better.
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Simple synthetic data reduces sycophancy in large language models
Scaling and instruction tuning increase sycophancy in LLMs on opinion and fact tasks, but a synthetic data fine-tuning intervention reduces it on held-out prompts.
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FinMoji: A Framework for Emoji-driven Sentiment Analysis in Financial Social Media
Emoji-only models for financial sentiment on StockTwits achieve F1 scores of approximately 0.75 with high efficiency, while specific emojis and pairs predict market trends at over 90 percent accuracy, though combined text models perform better.