content.raw: The most recent LLaMA-3-70B Instruct showcases the beast performance in zero-shot-learning mode in Target-Sentiment-Analsys (TSA) 🔥🚀  In particular we experiment with sentence-level analysis, with sentences fetched from the WikiArticles that were formed into RuSentNE-2023 dataset. 
content.type: text
content.value: The most recent LLaMA-3-70B Instruct showcases the beast performance in zero-shot-learning mode in Target-Sentiment-Analsys (TSA) 🔥🚀  In particular we experiment with sentence-level analysis, with sentences fetched from the WikiArticles that were formed into RuSentNE-2023 dataset. 
content.type: new_line
content.type: new_line
content.raw: The key takeaways out of LLaMA-3-70B performance on original (🇷🇺) texts and translated into English are as follows:
content.type: text
content.value: The key takeaways out of LLaMA-3-70B performance on original (🇷🇺) texts and translated into English are as follows:
content.type: new_line
content.raw: 1. Outperforms all ChatGPT-4 and all predecessors on non-english-texts (🇷🇺) 
content.type: text
content.value: 1. Outperforms all ChatGPT-4 and all predecessors on non-english-texts (🇷🇺) 
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content.raw: 2. Surpasses all ChatGPT-3.5 / nearly performs as good as ChatGPT-4 on english texts 🥳
content.type: text
content.value: 2. Surpasses all ChatGPT-3.5 / nearly performs as good as ChatGPT-4 on english texts 🥳
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content.type: new_line
content.raw: Benchmark: 
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content.value: Benchmark: 
content.href: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
content.raw: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
content.type: link
content.type: new_line
content.raw: Model: 
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content.value: Model: 
content.raw: https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct
content.id: meta-llama/Meta-Llama-3-70B-Instruct
content.type: model
content.type: resource
content.url: https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct
content.type: new_line
content.raw: Dataset: 
content.type: text
content.value: Dataset: 
content.href: https://github.com/dialogue-evaluation/RuSentNE-evaluation
content.raw: https://github.com/dialogue-evaluation/RuSentNE-evaluation
content.type: link
content.type: new_line
content.raw: Related paper: 
content.type: text
content.value: Related paper: 
content.label: Large Language Models in Targeted Sentiment Analysis (2404.12342)
content.raw: https://huggingface.co/papers/2404.12342
content.id: 2404.12342
content.type: paper
content.type: resource
content.url: https://huggingface.co/papers/2404.12342
content.type: new_line
content.raw: Collection: 
content.type: text
content.value: Collection: 
content.href: https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101
content.raw: https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101
content.type: link
rawContent: The most recent LLaMA-3-70B Instruct showcases the beast performance in zero-shot-learning mode in Target-Sentiment-Analsys (TSA) 🔥🚀  In particular we experiment with sentence-level analysis, with sentences fetched from the WikiArticles that were formed into RuSentNE-2023 dataset. 
  
  The key takeaways out of LLaMA-3-70B performance on original (🇷🇺) texts and translated into English are as follows:
  1. Outperforms all ChatGPT-4 and all predecessors on non-english-texts (🇷🇺) 
  2. Surpasses all ChatGPT-3.5 / nearly performs as good as ChatGPT-4 on english texts 🥳
  
  Benchmark: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
  Model: https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct
  Dataset: https://github.com/dialogue-evaluation/RuSentNE-evaluation
  Related paper: https://huggingface.co/papers/2404.12342
  Collection: https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101
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reactions.users: victor-huang kristaller486 Taylor658 Kirkalish osanseviero Rohith04 den0620