Ownership Bias in Language Models

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8 papers1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Authority Bias in Language Models: Source Deference and User Agreement Are Not Interchangeable
Sep 29, 2026Abhinav Rajeev Kumar, Paras ChopraLLM Sycophancy LLM Reliability
Lossfunk
When Models Defer to Wrong Answers: A Robustness Audit of Source-Attributed Cues in Multiple-Choice QA
Sep 8, 2026Manikandan Ravikiran, Siddharth VohraMultiple-Choice Question Answering LLM Reliability
Indian Institute of Technology Mandi, India · Carnegie Mellon University Amazon Web Services AI Native Pittsburgh, PA, USA
Authority Bias in Conversational Search Engines for Academic Paper Recommendation
Aug 31, 2026Uthman Jinadu, Parsa Ghazvinian, Anjila Budathoki +3Language Model Bias Evaluation Large Language Model-Based Recommendation
Georgia State University · University of Tennessee, Knoxville
The Authority Expectancy Effect in Multi-User Conflict
Aug 8, 2026Eunna LeeSocial Bias in Language Models LLM Decision-Making
Independent Researcher
A Mechanistic View of Authority Hierarchy in LLM Sycophancy
Jul 1, 2026Emil Joswin, Srujananjali Medicherla, Priyanka Mary MammenLLM Sycophancy LLM Interpretability
Independent Research · University of Massachusetts Amherst
Large Language Models Are Overconfident in Their Own Responses
Jun 2, 2026Mario Sanz-Guerrero, Manuel Mager, Katharina von der WenseConfidence Estimation in Language Models LLM Prompting
∇Johannes Gutenberg University Mainz, Germany · ♠University of Colorado Boulder, USA
When Names Change Verdicts: Intervention Consistency Reveals Systematic Bias in LLM Decision-Making
Mar 19, 2026Abhinaba Basu, Pavan ChakrabortyOwnership Bias in Language Models Language Model Bias Evaluation
Indian Institute of Information Technology, Allahabad (IIITA) · National Institute of Electronics and Information Technology (NIELIT)
Good Arguments Against the People Pleasers: How Reasoning Mitigates (Yet Masks) LLM Sycophancy
Mar 17, 2026Zhaoxin Feng, Zheng Chen, Jianfei Ma +3Ownership Bias in Language Models LLM Sycophancy
The Hong Kong Polytechnic University · The Hong Kong University of Science and Technology

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Authority Bias in Language Models: Source Deference and User Agreement Are Not Interchangeable

Abhinav Rajeev Kumar, Paras Chopra

Lossfunk

LLM Sycophancy LLM Reliability Ownership Bias in Language Models

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Language models tend to agree with whatever a user asserts, and post-training increasingly targets this sycophancy so that models evaluate claims on their merits rather than deferring to the user. Yet the same models are far more compliant when a wrong answer is attributed to a verified source, which is how retrieval results, tool outputs, and grounded-search content often present information. We measure this gap across five open-weight families and three closed APIs. A single verified-source note endorsing a wrong answer flips 45-88% of baseline-correct responses in seven of eight models, and compliance rises with how authoritative the note sounds. Source deference and user agreement are not behaviorally interchangeable inside the model: on matched items with the same wrong answer, causal interventions can selectively suppress one without equally affecting the other. In three open-weight families, removing a fitted source direction lowers source compliance by 65-80 percentage points while removing a user or assistant direction has far smaller effects, and removing the user direction shows the reverse preference. A separately fitted intervention derived from source-versus-user cue activations moves compliance in both directions while leaving the prompt text unchanged. An authority direction fitted on trivia also transfers to PIQA and multi-turn SYCON dialogues without refitting, and removing it lowers wrong-source compliance by tens of percentage points in four of five families with no detected change in MMLU-Pro or GSM8K accuracy at our evaluation sizes. Source deference and user agreement therefore need separate evaluation.