Sentiment Analysis through LLM Negotiations: Conclusion and References

This paper is available on arxiv under CC 4.0 license. Authors: (1) Xiaofei Sun, Zhejiang University & xiaofei_sun@zju.edu.cn; (2) Xiaoya Li, Shannon.AI, Bytedance & xiaoya_li@shannonai.com; (3) Shengyu Zhang, Zhejiang University & sy_zhang@zju.edu.cn; (4) Shuhe Wang, Peking University & wangshuhe@stu.pku.edu.cn; (5) Fei Wu, Zhejiang University & wufei@zju.edu.cn; (6) Jiwei Li, Zhejiang University & jiwei_li@zju.edu.cn; (7) Tianwei Zhang, Nanyang Technological University & tianwei.zhang@ntu.edu.sg; (8) Guoyin Wang, Bytedance & guoyin.wang@bytedance.com. Table of Links Abstract and Introduction Related Work LLM Negotiation for Sentiment Analysis Experiments Ablation Studies Conclusion and References 6 Conclusion In this paper, we investigate the limitations of singular LLM-based sentiment analysis methods and introduce a novel role-flipping multi-LLM negotiation method to enhance both the accuracy and interpretability of sentiment categorizations. Empirical findings on multiple benchmarks show the superiority of our approach compared to traditional ICL and many supervised methods. Future work could explore optimizing the framework for speed and resource consumption, adapting the underlying principles to other NLP tasks, and designing explicit negotiation modules that identify and mitigate the impact of biases and decoding errors present in individual LLMs. References Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al. 2023. Palm 2 technical report. arXiv preprint arXiv:2305.10403. Stefano Baccianella, Andrea Esuli, and Fabrizio Sebastiani. 2010. Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In International Conference on Language Resources and Evaluation. Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al. 2023. 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Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola. 2022b. Automatic chain of thought prompting in large language models. arXiv preprint arXiv:2210.03493. This paper is available on arxiv under CC 4.0 license. Authors: (1) Xiaofei Sun, Zhejiang University & xiaofei_sun@zju.edu.cn; (2) Xiaoya Li, Shannon.AI, Bytedance & xiaoya_li@shannonai.com; (3) Shengyu Zhang, Zhejiang University & sy_zhang@zju.edu.cn; (4) Shuhe Wang, Peking University & wangshuhe@stu.pku.edu.cn; (5) Fei Wu, Zhejiang University & wufei@zju.edu.cn; (6) Jiwei Li, Zhejiang University & jiwei_li@zju.edu.cn; (7) Tianwei Zhang, Nanyang Technological University & tianwei.zhang@ntu.edu.sg; (8) Guoyin Wang, Bytedance & guoyin.wang@bytedance.com. This paper is available on arxiv under CC 4.0 license. Authors: (1) Xiaofei Sun, Zhejiang University & xiaofei_sun@zju.edu.cn; (2) Xiaoya Li, Shannon.AI, Bytedance & xiaoya_li@shannonai.com; (3) Shengyu Zhang, Zhejiang University & sy_zhang@zju.edu.cn; (4) Shuhe Wang, Peking University & wangshuhe@stu.pku.edu.cn; (5) Fei Wu, Zhejiang University & wufei@zju.edu.cn; (6) Jiwei Li, Zhejiang University & jiwei_li@zju.edu.cn; (7) Tianwei Zhang, Nanyang Technological University & tianwei.zhang@ntu.edu.sg; (8) Guoyin Wang, Bytedance & guoyin.wang@bytedance.com. Table of Links Abstract and Introduction Related Work LLM Negotiation for Sentiment Analysis Experiments Ablation Studies Conclusion and References Abstract and Introduction Abstract and Introduction Related Work Related Work LLM Negotiation for Sentiment Analysis LLM Negotiation for Sentiment Analysis Experiments Experiments Ablation Studies Ablation Studies Conclusion and References Conclusion and References 6 Conclusion In this paper, we investigate the limitations of singular LLM-based sentiment analysis methods and introduce a novel role-flipping multi-LLM negotiation method to enhance both the accuracy and interpretability of sentiment categorizations. Empirical findings on multiple benchmarks show the superiority of our approach compared to traditional ICL and many supervised methods. Future work could explore optimizing the framework for speed and resource consumption, adapting the underlying principles to other NLP tasks, and designing explicit negotiation modules that identify and mitigate the impact of biases and decoding errors present in individual LLMs. References Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al. 2023. Palm 2 technical report. arXiv preprint arXiv:2305.10403. Stefano Baccianella, Andrea Esuli, and Fabrizio Sebastiani. 2010. Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In International Conference on Language Resources and Evaluation. Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al. 2023. Qwen technical report. arXiv preprint arXiv:2309.16609. Rushlene Kaur Bakshi, Navneet Kaur, Ravneet Kaur, and Gurpreet Kaur. 2016. Opinion mining and sentiment analysis. 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), pages 452–455. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901. Justin Chih-Yao Chen, Swarnadeep Saha, and Mohit Bansal. 2023a. Reconcile: Round-table conference improves reasoning via consensus among diverse llms. arXiv preprint arXiv:2309.13007. Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou. 2023b. Teaching large language models to self-debug. ArXiv, abs/2304.05128. Alexis Chevalier, Alexander Wettig, Anirudh Ajith, and Danqi Chen. 2023. Adapting language models to compress contexts. arXiv preprint 2305.14788. Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2022. Palm: Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311. Kevin Clark, Minh-Thang Luong, Quoc V Le, and Christopher D Manning. 2020. Electra: Pre-training text encoders as discriminators rather than generators. arXiv preprint arXiv:2003.10555. Junqi Dai, Hang Yan, Tianxiang Sun, Pengfei Liu, and Xipeng Qiu. 2021. Does syntax matter? a strong baseline for aspect-based sentiment analysis with roberta. In North American Chapter of the Association for Computational Linguistics. Z Dai, Z Yang, Y Yang, J Carbonell, Q Le, and R Transformer-XL Salakhutdinov. 2019. Attentive language models beyond a fixed-length context. 2019. arXiv preprint arXiv:1901.02860. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. 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