Reporting Under Pressure: Separating Factual and Tonal Sycophancy in LLM Statistical Analysis

arXiv:2609.27756 · cs.AI, cs.CL · Submitted 2026-08-15 · Read on arXiv

cs.AI, cs.CL

Submitted: 2026-08-15

Updated: 2026-08-15

Terminology

• Sycophancy — 16× in this paper · explained in 4 episodes
• LLM judge — 4× in this paper · explained in 2 episodes
• Cosine Similarity — 3× in this paper · explained in 3 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Commentary — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• ChatGPT Reads Your Tone and Responds Accordingly -- Until It Does Not -- Emotional Framing Induces Bias in LLM Outputs
• Large Language Model Hacking: Quantifying the Hidden Risks of Using LLMs for Text Annotation
• What Counts as AI Sycophancy? A Taxonomy and Expert Survey of a Fragmented Construct
• ELEPHANT: Measuring and understanding social sycophancy in LLMs
• When Wording Steers the Evaluation: Framing Bias in LLM judges

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