Measuring and Mitigating Silent Narrative Switching in Large Language Models through Epistemic Mediation
By: Iurii Lukianchuk, Lev Boiko and Inna Kondius
References
- S. Santurkar, E. Durmus, F. Ladhak, C. Lee, P. Liang, and T. Hashimoto, “Whose opinions do language models reflect?” in Proc. 40th Int. Conf. Machine Learning (ICML), Mar. 2023, pp. 29977–30004. https://doi.org/10.48550/arXiv.2303.17548
- E. Kasneci et al., “ChatGPT for good? On opportunities and challenges of large language models for education,” Learning and Individual Differences, vol. 103, Apr. 2023, Art. no. 102274. https://doi.org/10.1016/j.lindif.2023.102274
- S. Wollny, J. Schneider, D. Di Mitri, J. Weidlich, M. Rittberger, and H. Drachsler, “Are we there yet? A systematic literature review on chatbots in education,” Frontiers in Artificial Intelligence, vol. 4, July 2021, Art. no. 654924. https://doi.org/10.3389/frai.2021.654924
- L. P. Argyle, E. C. Busby, N. Fulda, J. R. Gubler, C. Rytting, and D. Wingate, “Out of one, many: Using language models to simulate human samples,” Political Analysis, vol. 31, no. 3, pp. 337–351, Feb. 2023. https://doi.org/10.1017/pan.2023.2
- M. Sharma et al., “Towards understanding sycophancy in language models,” in Proc. Int. Conf. Learning Representations (ICLR), 2024. https://doi.org/10.48550/arXiv.2310.13548
- A. Salinas and F. Morstatter, “The butterfly effect of altering prompts,” in Findings of the ACL, Bangkok, Thailand, Aug. 2024, pp. 4629–4651. https://doi.org/10.18653/v1/2024.findings-acl.275
- F. Perez and I. Ribeiro, “Ignore previous prompt: Attack techniques for language models,” arXiv:2211.09527, Nov. 2022. https://doi.org/10.48550/arXiv.2211.09527
- L. Ouyang et al., “Training language models to follow instructions with human feedback,” in Adv. Neural Inf. Process. Syst. (NeurIPS), vol. 35, New Orleans, USA, Nov.–Dec. 2022, pp. 27730–27744. https://doi.org/10.52202/068431-2011
- Y. Bai et al., “Constitutional AI: Harmlessness from AI feedback,” arXiv:2212.08073, Dec. 2022. https://doi.org/10.48550/arXiv.2212.08073
- T. Sorensen et al., “A roadmap to pluralistic alignment,” in Proc. 41st Int. Conf. Machine Learning (ICML), 2024.
- S. Feng et al., “Modular pluralism: Pluralistic alignment via multi-LLM collaboration,” arXiv:2406.15951, Oct. 2024. https://doi.org/10.48550/arXiv.2406.15951
- G. V. Aher, R. I. Arriaga, and A. T. Kalai, “Using large language models to simulate multiple humans,” in Proc. ICML, 2023, pp. 337–371.
- J. S. Park, J. C. O’Brien, C. J. Cai, M. R. Morris, P. Liang, and M. S. Bernstein, “Generative agents: Interactive simulacra of human behavior,” in Proc. ACM UIST, Oct. 2023, pp. 1–22. https://doi.org/10.1145/3586183.3606763
- F. Wu, E. Black, and V. Chandrasekaran, “Generative monoculture in large language models,” 2025. [Online]. Available: https://openreview.net/pdf?id=yZ7sn9pyqb
- L. Weidinger et al., “Taxonomy of risks posed by language models,” in Proc. ACM Conf. Fairness, Accountability, and Transparency (FAccT), June 2022, pp. 214–229. https://doi.org/10.1145/3531146.3533088
- X. Wang et al., “Self-consistency improves chain of thought reasoning in language models,” in Proc. Int. Conf. Learning Representations (ICLR), Mar. 2023. https://doi.org/10.48550/arXiv.2203.11171
- Y. Chang et al., “A survey on evaluation of large language models,” ACM Trans. Intell. Syst. Technol., vol. 15, no. 3, Mar. 2024, Art. no. 39. https://doi.org/10.1145/3641289
- Y. Liu, D. Iter, Y. Xu, S. Wang, R. Xu, and C. Zhu, “G-Eval: NLG evaluation using GPT-4 with better human alignment,” in Proc. EMNLP, Singapore, Dec. 2023, pp. 2511–2522. https://doi.org/10.18653/v1/2023.emnlp-main.153
- S. M. T. I. Tonmoy et al., “A comprehensive survey of hallucination mitigation techniques in large language models,” ACM Comput. Surv., vol. 56, no. 9, Jan. 2024, Art. no. 220. https://doi.org/10.13140/RG.2.2.11724.39045
- S. Gehman, S. Gururangan, M. Sap, Y. Choi, and N. A. Smith, “RealToxicityPrompts: Evaluating neural toxic degeneration in language models,” in Findings of the ACL: EMNLP, Nov. 2020, pp. 3356–3369. https://doi.org/10.18653/v1/2020.findings-emnlp.301
- J. Cohen, Statistical Power Analysis for the Behavioral Sciences, 2nd ed. Hillsdale, NJ: Lawrence Erlbaum, 1988.
- V. Sanh, L. Debut, J. Chaumond, and T. Wolf, “DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter,” arXiv:1910.01108, Mar. 2019. https://doi.org/10.48550/arXiv.1910.01108
- D. Amodei, C. Olah, J. Steinhardt, P. Christiano, J. Schulman, and D. Mané, “Concrete problems in AI safety,” arXiv:1606.06565, July 2016. https://doi.org/10.48550/arXiv.1606.06565
DOI: https://doi.org/10.2478/acss-2026-0017 | Journal eISSN: 2255-8691 | Journal ISSN: 2255-8683 (formerly 2255-8691)
Language: English
Page range: 206 - 213
Submitted on: Sep 4, 2026
Accepted on: Oct 19, 2026
Published online: Nov 1, 2026
Published in print: Jan 1, 2026
Published by: Riga Technical University
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open
Keywords:
Related subjects:
© 2026 Iurii Lukianchuk, Lev Boiko, Inna Kondius, published by Riga Technical University
This work is licensed under the Creative Commons Attribution 4.0 License.