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20232026
most citedPrompting Techniques for Reducing Social Bias in LLMs through System 1 and System 2 Cognitive Processes

2 citations · 5 across the 13 of their papers we have counts for

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cs.CL2026

Token-Level Diagnosis of Sycophancy in LLMs with Attribution-Guided Steering

Hieu Nguyen, Mahammed Kamruzzaman, Anshuman Chhabra +1

Sycophancy refers to the tendency for large language models (LLMs) to match user beliefs at the cost of factual correctness, thereby undermining model reliability. Prior work on ev…

cs.CL2026

Demographic Prompting at Scale: When More Attributes Hurt LLM--Human Agreement

Mahammed Kamruzzaman, Shrabon Kumar Das, Gene Louis Kim

We investigate how annotator demographic attributes, supplied as prompt cues, shape the alignment between large language model (LLM) predictions and human annotations across five t…