Eungu Lee
Author directory2026
No Matter Who You Are: Addressivity and Sycophancy in Large Language Models
Seungjong Sun | Hyunmin Jun | Minsik Yoon | Eungu Lee | Choongwon Kang | Young Seok Woo | Jang Hyun Kim
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
Seungjong Sun | Hyunmin Jun | Minsik Yoon | Eungu Lee | Choongwon Kang | Young Seok Woo | Jang Hyun Kim
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
Sycophancy, where an LLM becomes overly agreeable, deferential, or flattering toward the user, has been identified as a major failure mode. While post-training has been implicated as a cause, how it internally gives rise to sycophantic behavior remains insufficiently understood. Post-training transforms the model’s output from text completion into utterances directed toward an addressee, thereby giving the output addressivity. We hypothesize that the accompanying pressure to follow the addressee’s instructions and align with their preferences links this addressivity to sycophancy. To test this, we use Sparse Autoencoders to extract addressivity directions from pre-trained and post-trained models and examine their relationship with sycophancy. Steering models with addressivity directions can amplify or suppress sycophancy, and this effect is observed only in post-trained models. Furthermore, comparing the internal mechanisms of both model types confirms that the association between addressivity and sycophancy is strengthened through post-training. Finally, through a controlled post-training experiment, we find that models are sycophantic not toward a specific addressee but toward any recognized addressee, suggesting that the presence of an addressee, rather than the addressee’s identity, is what elicits sycophancy.
2024
Kiss up, Kick down: Exploring Behavioral Changes in Multi-modal Large Language Models with Assigned Visual Personas
Seungjong Sun | Eungu Lee | Seo Yeon Baek | Seunghyun Hwang | Wonbyung Lee | Dongyan Nan | Bernard J Jansen | Jang Hyun Kim
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Seungjong Sun | Eungu Lee | Seo Yeon Baek | Seunghyun Hwang | Wonbyung Lee | Dongyan Nan | Bernard J Jansen | Jang Hyun Kim
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
This study is the first to explore whether multi-modal large language models (LLMs) can align their behaviors with visual personas, addressing a significant gap in the literature that predominantly focuses on text-based personas. We developed a novel dataset of 5K fictional avatar images for assignment as visual personas to LLMs, and analyzed their negotiation behaviors based on the visual traits depicted in these images, with a particular focus on aggressiveness. The results indicate that LLMs assess the aggressiveness of images in a manner similar to humans and output more aggressive negotiation behaviors when prompted with an aggressive visual persona. Interestingly, the LLM exhibited more aggressive negotiation behaviors when the opponent’s image appeared less aggressive than their own, and less aggressive behaviors when the opponent’s image appeared more aggressive.