Research and Analysis on Text Interaction Methods Based on Large Language Models

Shangze Yu

doi:10.62051/rs49my32

Authors

Shangze Yu

DOI:

https://doi.org/10.62051/rs49my32

Keywords:

Large language model; Natural language processing; Affective computing; Reinforcement learning; Transformer.

Abstract

Large Language Models (LLMs), as a cutting-edge technology in the field of artificial intelligence, have revolutionized human-computer interaction paradigms by integrating natural language processing, Transformer architecture, and reinforcement learning techniques, thereby achieving in-depth understanding and generation of linguistic logic and emotions. Their technological core lies in the Transformer architecture trained on massive datasets, which can capture complex semantics and contextual associations, optimize generation quality through reinforcement learning, and enhance interpersonal-like interaction via affective computing. This paper systematically reviews the technical framework and application scenarios of LLMs: in fields such as intelligent customer service, educational assessment, and game narration, these models demonstrate application values such as multi-turn dialogue, personalized learning path planning, and dynamic plot generation. Meanwhile, it delves into the challenges faced by technological development, including ethical risks arising from biases in training data, deployment cost issues due to high computational power requirements, and deficiencies in the controllability of generated content. In response to these issues, collaborative solutions such as multimodal data fusion, lightweight model deployment, and interdisciplinary ethical governance are proposed. Research indicates that LLMs are at a critical stage of transitioning from technological breakthroughs to large-scale applications, and their sustainable development necessitates the construction of a technological governance framework to achieve social value balance, on top of algorithm optimization and computational power enhancement. Future research should focus on enhancing model interpretability, exploring green computing pathways, and promoting the virtuous development of technology through human-machine collaboration mechanisms.

Downloads

Download data is not yet available.

References

[1] T. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. Ziegler, J. Wu, C. Winter, ... & D. Amodei. Language models are few-shot learners. arXiv preprint arXiv:2005.14165. (2020).

[2] J. Wei, X. Wang, D. Schuurmans, M. Bosma, B. Ichter, F. Xia, E. H. Chi, Q. V. Le, & D. Zhou. Chain-of-thought prompting elicits reasoning in large language models. arXiv preprint arXiv:2201.11903. (2022).

[3] L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, J. Schulman, J. Hilton, F. Kelton, L. Miller, M. Simens, A. Askell, P. Welinder, P. Christiano, J. Leike, & R. Lowe. Training language models to follow instructions with human feedback. arXiv preprint arXiv:2203.02155. (2022).

[4] L. Reynolds, & K. McDonell. Prompt programming for large language models: Beyond the few-shot paradigm. arXiv preprint arXiv:2112.07870. (2021).

[5] J. Devlin, M.-W. Chang, K. Lee, & K. Toutanova. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019).

[6] A. Radford, K. Narasimhan, T. Salimans, & I. Sutskever. Improving Language Understanding by Generative Pre-Training. (2018)

[7] D. Chen, & C. D. Manning. A Fast and Accurate Dependency Parser using Neural Networks. (2014).

[8] G. Lample, M. Ballesteros, S. Subramanian, K. Kawakami, & C. Dyer. Neural Architectures for Named Entity Recognition. (2016).

[9] S. Mascarenhas, J. Dias, R. Prada, & A. Paiva. A dimensional model for cultural emotion expression in virtual agents. Journal on Multimodal User Interfaces, 3(1-2), 29-38. (2010).

[10] R. Aylett, J. Dias, & A. Paiva. An affectively driven planner for synthetic characters. IEEE Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS), 2(1), 2-10. (2006).

[11] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, & I. Polosukhin. Attention is All You Need. Advances in Neural Information Processing Systems, 30, 5998–6008. (2017).

[12] Z. Dai, Z. Yang, Y. Yang, J. Carbonell, Q. V. Le, & R. Salakhutdinov. Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), 2978–2988. (2019).

[13] N. Kitaev, Ł. Kaiser, & A. Levskaya. Reformer: The Efficient Transformer. In Proceedings of the 37th International Conference on Machine Learning (ICML), 7001–7011. (2020).

[14] V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, & D. Hassabis. Human-level control through deep reinforcement learning. Nature, 518(7540), 529–533.(2015).

[15] V. Mnih, A. P. Badia,, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, & K. Kavukcuoglu. Asynchronous Methods for Deep Reinforcement Learning. In International Conference on Machine Learning (pp. 1928–1937). (2016).

[16] T. Haarnoja, A. Zhou,, P. Abbeel, & S. Levine.Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor. In International Conference on Machine Learning (pp. 1861–1870). (2018).