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Estimate Emotion Probability Vectors Using LLMs: Acknowledgements and References
by Writings, Papers and Blogs on Text ModelsMay 10th, 2024
Too Long; Didn't Read
This paper shows how LLMs (Large Language Models) [5, 2] may be used to estimate a summary of the emotional state associated with a piece of text.
This paper is available on arxiv under CC 4.0 license.
Authors:
(1) D.Sinclair, Imense Ltd, and email: [email protected];
(2) W.T.Pye, Warwick University, and email: [email protected].
Table of Links
- Abstract and Introduction
- Interrogating the LLM with an Emotion Eliciting Tail Prompt
- PCA analysis of the Emotion of Amazon reviews
- Future Work
- Conclusions
- Acknowledgments and References
6. Acknowledgements
The authors acknowledge the extraordinary generosity of Meta in releasing model weights in a reasonable way for their LlaMa2 series of pre-trained Large Language Models.
7. References
[1] Open AI. Chatgpt-4 technical report. 2023. URL https://arxiv.org/pdf/2303.08774.pdf.
[2] Meta GenAI, Thomas Scialom, and Hugo Touvron. Llama 2: Open foundation and fine-tuned chat models. 2023. URL https://arxiv.org/pdf/2307.09288.pdf.
[3] Rosalind W. Picard. Affective computing. MIT Press, 1997.
[4] J Strabismus. The jedi religion: Is love the force? Amazon Kindle, 2013.
[5] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. CoRR, abs/1706.03762, 2017. URL http://arxiv.org/abs/1706.03762.
[6] Wenxuan Zhang, Yue Deng, Bing Liu, Sinno Jialin Pan, and Lidong Bing. Sentiment analysis in the era of large language models: A reality check, 2023.
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