Papers by Sanjeevan Selvaganapathy

2 papers
Activation-Space Personality Steering: Hybrid Layer Selection for Stable Trait Control in LLMs (2026.eacl-long)

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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.
Approach: They propose a pipeline that extracts hidden state activations from transformer layers using the Big Five Personality Traits framework.
Outcome: The proposed model extracts hidden state activations from transformer layers using the Big Five personality traits (Openness, Conscientiousness, Extraversion, Agreeableness and Neuroticism)
Confident, Calibrated, or Complicit: Safety Alignment and Ideological Bias in LLM Hate Speech Detection (2026.acl-long)

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Challenge: censored models outperform uncensoreed counterparts in accuracy and robustness, achieving 69.0% accuracy versus 64.1% strict accuracy.
Approach: They examine how large language models with minimal safety alignment compare with more heavily aligned counterparts when deployed using political personas.
Outcome: The proposed model outperforms uncensored models in accuracy and robustness, while uncensors are more malleable to ideological framing.

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