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📢 This year I made decent amout of experiments on LLM reasoning capabilities in author opinion extraction. 
📢 This year I made decent amout of experiments on LLM reasoning capabilities in author opinion extraction. 
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However, they did not go further with:
However, they did not go further with:
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↗️ annoation of other sources of opinion causes: entities, out-of-context object (None).
↗️ annoation of other sources of opinion causes: entities, out-of-context object (None).
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📏 evaluation of factual statements that support the extracted sentiment.
📏 evaluation of factual statements that support the extracted sentiment.
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To address these limitations, so far we launch 🚀 RuOpinionNE-2024 competition on the Codalab platform: 
To address these limitations, so far we launch 🚀 RuOpinionNE-2024 competition on the Codalab platform: 
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📊 
📊 
link
https://codalab.lisn.upsaclay.fr/competitions/20244
https://codalab.lisn.upsaclay.fr/competitions/20244
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 The competition is aimed at extraction of opinion tuples (see attached images) from texts written in Russian.
 The competition is aimed at extraction of opinion tuples (see attached images) from texts written in Russian.
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It proceeds the past RuSentNE-2023 codalab competition findings:
It proceeds the past RuSentNE-2023 codalab competition findings:
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🔎 Past year competition: 
🔎 Past year competition: 
link
https://www.dialog-21.ru/media/5896/golubevaplusetal118.pdf
https://www.dialog-21.ru/media/5896/golubevaplusetal118.pdf
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🔎 LLM reasoning 🧠: 
🔎 LLM reasoning 🧠: 
link
https://arxiv.org/abs/2404.12342
https://arxiv.org/abs/2404.12342
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For those who interested to adopt Generative AI, the complete information about competition is below:
For those who interested to adopt Generative AI, the complete information about competition is below:
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📊 RuOpinionNE-2024: 
📊 RuOpinionNE-2024: 
link
https://codalab.lisn.upsaclay.fr/competitions/20244
https://codalab.lisn.upsaclay.fr/competitions/20244
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🗒 Task description: 
🗒 Task description: 
link
https://codalab.lisn.upsaclay.fr/competitions/20244#learn_the_details-overview
https://codalab.lisn.upsaclay.fr/competitions/20244#learn_the_details-overview
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🔔 To follow updates: 
🔔 To follow updates: 
link
https://t.me/RuOpinionNE2024
https://t.me/RuOpinionNE2024
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⏰ Stages Deadlines (might be extended)
⏰ Stages Deadlines (might be extended)
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📦 Submission details (bottom of the competition page)
📦 Submission details (bottom of the competition page)
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🙋 For questions you can contact 
🙋 For questions you can contact 
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@nicolay-r
nicolay-r
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: 
: 
link
https://nicolay-r.github.io/
https://nicolay-r.github.io/
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🧪  Most recent findings on LLM application: 
🧪  Most recent findings on LLM application: 
link
https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
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📢 This year I made decent amout of experiments on LLM reasoning capabilities in author opinion extraction. 
However, they did not go further with:
↗️ annoation of other sources of opinion causes: entities, out-of-context object (None).
📏 evaluation of factual statements that support the extracted sentiment.

To address these limitations, so far we launch 🚀 RuOpinionNE-2024 competition on the Codalab platform: 
📊 https://codalab.lisn.upsaclay.fr/competitions/20244

 The competition is aimed at extraction of opinion tuples (see attached images) from texts written in Russian.
It proceeds the past RuSentNE-2023 codalab competition findings:
🔎 Past year competition: https://www.dialog-21.ru/media/5896/golubevaplusetal118.pdf
🔎 LLM reasoning 🧠: https://arxiv.org/abs/2404.12342

For those who interested to adopt Generative AI, the complete information about competition is below:
📊 RuOpinionNE-2024: https://codalab.lisn.upsaclay.fr/competitions/20244
🗒 Task description: https://codalab.lisn.upsaclay.fr/competitions/20244#learn_the_details-overview
🔔 To follow updates: https://t.me/RuOpinionNE2024
⏰ Stages Deadlines (might be extended)
📦 Submission details (bottom of the competition page)

🙋 For questions you can contact @nicolay-r: https://nicolay-r.github.io/
🧪  Most recent findings on LLM application: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark

👀
John6666