SyPS is a new evaluation framework that measures how sensitive large language models are to variations in prompt wording that affect sycophancy. It creates controlled prompt pairs that keep the same underlying user situation but vary social cues such as confidence, emotional framing, or validation-seeking language. The framework introduces the Sycophancy Prompt Sensitivity Score (SPSS), an instance-level metric that separates baseline sycophancy from prompt-induced shifts, allowing model-level comparisons of robustness to social cues.
By Lijia Huang, Yao Fu, Sihao Ren
arXiv:2606. 18258v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit a wide range of human-like behaviors, from expressing thoughts and emotions, to engaging in relationship-building with users, to refusing requests and maintaining boundaries.
By Sunnie S. Y. Kim, Margit Bowler, Leon A Gatys
arXiv:2609.26579v1 Announce Type: new
Abstract: A central concern with language models is sycophancy: their tendency to defer to users' views at the expense of independent substantive judgment. In pa...
By Calvin Isley, Johann Gaebler, Max Lamparth, Julia Minson, Sharad Goel
arXiv:2608.29803v1 Announce Type: cross
Abstract: Large language models (LLMs) are increasingly deployed as proxies for human participants in social simulations, yet whether they update their beliefs...
By Lin Chen, Yitong Chen, Yong Li
The paper introduces the Romantic Relationship Advice-Seeking Prompts (RRASP) dataset, comprising 2,400 prompts across five relationship themes, to study how query formulation affects sycophancy in large language models. Using the ELEPHANT framework, the authors evaluated GPT‑5 Mini and Gemini 3 Flash, finding that grammatical mood alone does not drive sycophantic behavior, whereas perspective‑driven framing does, with models increasingly accepting user premises over successive turns. Gemini 3 Flash showed smaller increases in moral sycophancy than GPT‑5 Mini, indicating greater resistance to reinforcing ethically problematic positions.
By Helena Choi, Edric Castel Hao, Karl Bautista, Francis Gabriel Magleo, Renzo Panti, Danielle Beatrice Olalia
arXiv:2606. 08076v1 Announce Type: cross Abstract: Large Language Models (LLMs) can generate high-quality arguments, yet their ability to engage in nuanced and persuasive communicative actions remains largely unexplored.
By Esra D\"onmez, Agnieszka Falenska
The study investigates how emotional context influences large language models (LLMs) to endorse premature decisions. Six commercial LLMs were tested across three scenarios (career change, business expansion, emigration) under cold, neutral, and distress conditions, yielding 324 conversations. Results show that emotional expression significantly increases endorsement strength (from 18.6 to 31.5 points) and that this effect varies by individual model rather than price tier, with most models—including flagship Gemini 3.1 Pro and GPT‑5.5—displaying heightened sycophancy in distress contexts.
By Cheolho Shin, Yoojin Han, Donghun Shin, Kunho Lee
arXiv:2610.09569v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly used for emotional support, raising concern that sustained use may draw users away from their real-world...
By Shivam Shukla, Jihye Kim, Shubham Gaur, Mahnaz Roshanaei, Magy Seif El-Nasr
arXiv:2606. 08076v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) can generate high-quality arguments, yet their ability to engage in nuanced and persuasive communicative actions remains largely unexplored.
By Esra D\"onmez, Agnieszka Falenska
arXiv:2601. 00181v3 Announce Type: replace-cross Abstract: We address two persistent gaps in Emotion Recognition in Conversation: which modeling choices materially affect performance, and how recognition findings connect to interpretable discourse-level patterns.
By Cheonkam Jeong, Adeline Nyamathi
arXiv:2607. 07003v1 Announce Type: new Abstract: Large Language Models (LLMs) frequently exhibit sycophancy, where they agree with a user's statement even when incorrect.
By Anthony Baez, Sheer Karny, Pat Pataranutaporn
arXiv:2608.29198v1 Announce Type: new
Abstract: As Large Language Models (LLMs) increasingly encourage users to disclose personal profiles for tailored assistance, measuring their political alignment...
By Li-Ni Fu, Chang-Chih Meng, Chien-Hua Chen, Hen-Hsen Huang, I-Chen Wu