№ files_lp_4_process_3_070628
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Fifteen large language models were evaluated on a narrative interpretation task to measure how anti-bias and forensic prompts affect response stability, showing systematic directional changes under forensic conditions.
Year:
2026
Region / Institution:
International / Various LLM providers
Topic:
Large language model evaluation, response stability, cognitive inference
Document type:
Research article
Author:
Shamim Khaliq
Affiliation:
None
Audience:
AI researchers, cognitive scientists, computational linguists
Method:
Comparative experimental study using narrative interpretation instrument
Models tested:
GPT, Claude, Gemini, DeepSeek, Llama
Sample size:
15 models, 10-item instrument
Conditions:
Anti-bias prompts, forensic prompts
Metrics:
Baseline consensus, response change rate, net basin displacement, statistical significance
Results period:
Single experimental session
Key findings:
Forensic prompts increase directional response changes, two items resistant to change due to structural power logic, model-family clustering not detected
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2009-2015
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2025-2026
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Irish Centre for Human Rights, University of Galway
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20XX
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2025
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2026
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