LLMs exhibit an accumulated message effect where conversation history saturated with positive or negative evaluations biases subsequent judgments, with larger shifts on uncertain items, a negativity asymmetry, and no increase with context length.
Interaction context often increases sycophancy in llms.arXiv preprint arXiv:2509.12517
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4representative citing papers
Frontier LLMs show sycophancy that varies sharply by model and by combinations of perceived user demographics, with GPT-5-nano exhibiting higher rates especially toward certain Hispanic personas in philosophy.
SWAY quantifies sycophancy in LLMs via shifts under linguistic pressure and a counterfactual chain-of-thought mitigation reduces it to near zero while preserving responsiveness to genuine evidence.
Reddit analysis shows users detect AI sycophancy through comparisons and consistency checks, apply mitigation prompts, and sometimes seek affirmative responses for support, indicating context-aware design is better than total elimination.
citing papers explorer
-
AMEL: Accumulated Message Effects on LLM Judgments
LLMs exhibit an accumulated message effect where conversation history saturated with positive or negative evaluations biases subsequent judgments, with larger shifts on uncertain items, a negativity asymmetry, and no increase with context length.
-
Intersectional Sycophancy: How Perceived User Demographics Shape False Validation in Large Language Models
Frontier LLMs show sycophancy that varies sharply by model and by combinations of perceived user demographics, with GPT-5-nano exhibiting higher rates especially toward certain Hispanic personas in philosophy.
-
SWAY: A Counterfactual Computational Linguistic Approach to Measuring and Mitigating Sycophancy
SWAY quantifies sycophancy in LLMs via shifts under linguistic pressure and a counterfactual chain-of-thought mitigation reduces it to near zero while preserving responsiveness to genuine evidence.
-
User Detection and Response Patterns of Sycophantic Behavior in Conversational AI
Reddit analysis shows users detect AI sycophancy through comparisons and consistency checks, apply mitigation prompts, and sometimes seek affirmative responses for support, indicating context-aware design is better than total elimination.