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🚨 Key takeaway of a quick mastering Sentiment Analysis nowadays. Trough the questionare 📝 of the past RuOpinoinNE-2024 competition we got insights and participants model preference chocies. Our main conclusion:
✨ The submissions of the top performed models exploit Few-shot learning for LLM.
Takeaway note comparing with the prior RuSentNE-2023 competition:
🧠 Reasoning in steps requires more actions for tweaking. Most recent solutions empowered with Chain-of-Thouhgt are tend to think too much. Earlier we might see improvements for the Flan-T5 (2.8B) in fine-tuned mode but not among the zero-shot approaches.
nicolay-r/flan-t5-tsa-thor-xl
Related materials:
https://github.com/dialogue-evaluation/RuOpinionNE-2024
RuSentNE-2023: Evaluating Entity-Oriented Sentiment Analysis on Russian News Texts (2305.17679)
Large Language Models in Targeted Sentiment Analysis (2404.12342)
✨ The submissions of the top performed models exploit Few-shot learning for LLM.
Takeaway note comparing with the prior RuSentNE-2023 competition:
🧠 Reasoning in steps requires more actions for tweaking. Most recent solutions empowered with Chain-of-Thouhgt are tend to think too much. Earlier we might see improvements for the Flan-T5 (2.8B) in fine-tuned mode but not among the zero-shot approaches.
nicolay-r/flan-t5-tsa-thor-xl
Related materials:
https://github.com/dialogue-evaluation/RuOpinionNE-2024
RuSentNE-2023: Evaluating Entity-Oriented Sentiment Analysis on Russian News Texts (2305.17679)
Large Language Models in Targeted Sentiment Analysis (2404.12342)