arXiv (All)
open access
How Does Alignment Tuning Shape Representations of Sycophancy and Related Cue-Induced Biases in LLMs?
Modern LLMs are alarmingly susceptible to surprisingly simple immaterial changes of input prompts: a casual hint, an incorrectly labeled few-shot example, or a fake prior assistant turn often flips an originally correct answer. We study where this susceptibility, spanning sycophancy and related cue-
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Visual Harmony and Complexity Shape Subjective Judgments More than Detectable Overt Attention: Evidence from Eye-Tracking and Facial Coding.#1080512
- Not Your Typical Sycophant: The Elusive Nature of Sycophancy in Large Language Models#798737
- Not Your Typical Sycophant: The Elusive Nature of Sycophancy in Large Language Models#797323
- Flattery in Motion: Benchmarking and Analyzing Sycophancy in Video-LLMs#183258
- Load Minimization Theory: Empirical Validation of the Sycophancy Trap via Monte Carlo Simulation with Realistic Dialogues#95701
Record · ID 820558
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive