Chameleons and Guardians: Unveiling the Divergence in Personality Plasticity and Cognitive Resistance across LLMs (2026.findings-acl)
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| Challenge: | Existing studies on LLMs argue for its immutability, but prior studies have not found that personality-inducing contexts can be intentionally reshaped. |
| Approach: | They propose a personality-inducing framework that reshapes LLMs via multi-agent collaboration . they paraphrase MBTI questions to create semantically equivalent but expressively diverse inducing contexts . |
| Outcome: | Experiments on worldwide mainstream LLMs show that PIF transforms their original personalities into desired target personalities. |
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| Challenge: | Existing studies have examined whether large language models adapt their perceived personalities in response to user interactions. |
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Pranav Bhandari, Nicolas Fay, Michael J Wise, Amitava Datta, Stephanie Meek, Usman Naseem, Mehwish Nasim
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| Challenge: | Role-playing agents lack a deep understanding of complex human psychological mechanisms. |
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Can LLMs Express Personality Across Cultures? Introducing CulturalPersonas for Evaluating Trait Alignment (2025.findings-emnlp)
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| Challenge: | Recent studies have explored personality evaluation of LLMs, but they largely overlook the interplay between culture and personality. |
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| Challenge: | Personality-aware LLMs exhibit implicit personalities in their generation, but reliably controlling or aligning these traits to meet specific needs remains an open challenge. |
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Forget What You Know about LLMs Evaluations - LLMs are Like a Chameleon (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) excel on public benchmarks, but high scores may mask overreliance on dataset-specific surface cues rather than true language understanding. |
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| Challenge: | Existing studies have shown that LLMs reproduce training artifacts, exploit spurious correlations, and fail when faced with distribution shifts. |
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The Model Agreed, But Didn’t Learn: Diagnosing Surface Compliance in Large Language Models (2026.findings-acl)
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| Challenge: | Large Language Models internalize vast world knowledge as parametric memory, yet inherit the staleness and errors of their source corpora. |
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| Challenge: | Personal style is often subtle and implicit, making it difficult to specify through prompts yet essential for user-aligned generation. |
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Born Pragmatic, Trained to Hallucinate? Quantifying the Origins of Contextual Bias in LLMs via the PaCE Benchmark (2026.findings-acl)
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| Challenge: | Large language models excel at capturing communicative intent, but they have a side effect: pragmatic hallucination. |
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