VISE is the first benchmark for sycophancy in Video-LLMs, with two training-free mitigation strategies based on key-frame selection and internal representation steering.
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10 Pith papers cite this work, alongside 8 external citations. Polarity classification is still indexing.
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LLM responses mirror venting with higher regulation and escalation; therapist personas lower escalation while preserving regulation, and lay raters miss escalation.
Showing LLM agents precomputed rankings of their peers' sycophancy improves multi-agent discussion accuracy by ~10.5 absolute points and reduces agreement with incorrect user stances.
Debiasing-DPO reduces bias to spurious social contexts by 84% and improves predictive accuracy by 52% on average for LLMs evaluating U.S. classroom transcripts.
Beacon is a new single-turn benchmark that measures latent sycophancy in LLMs, showing it decomposes into linguistic and affective sub-biases that scale with model capacity and can be modulated by prompt and activation interventions.
An AI agent representing outgroup views in ingroup chat directionally reduced intergroup anxiety and improved perspective-taking versus passive document exposure.
A pre-registered RCT found that one session with a belief-reframing AI chatbot produced significantly greater reductions in breakup distress than a survey-only control at 7 days, with a smaller effect persisting at 1 month.
Positive emotional prompts improve LLM accuracy and reduce toxicity but increase sycophantic agreement, while negative emotions show the reverse pattern.
Survey and chat data from CharacterAI users link companionship-focused AI use to lower well-being, with stronger ties for users who have small offline networks and engage intensively or disclosively.
Structured integration of LLMs in astronomy education, including a domain-specific tutor and documentation requirements, leads to improved AI literacy and reduced student reliance on AI over the semester.
citing papers explorer
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Flattery in Motion: Benchmarking and Analyzing Sycophancy in Video-LLMs
VISE is the first benchmark for sycophancy in Video-LLMs, with two training-free mitigation strategies based on key-frame selection and internal representation steering.
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When Support Escalates Distress: Regulation and Escalation in LLM Responses to Venting and Advice-Seeking
LLM responses mirror venting with higher regulation and escalation; therapist personas lower escalation while preserving regulation, and lay raters miss escalation.
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Too Polite to Disagree: Understanding Sycophancy Propagation in Multi-Agent Systems
Showing LLM agents precomputed rankings of their peers' sycophancy improves multi-agent discussion accuracy by ~10.5 absolute points and reduces agreement with incorrect user stances.
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Mitigating LLM biases toward spurious social contexts using direct preference optimization
Debiasing-DPO reduces bias to spurious social contexts by 84% and improves predictive accuracy by 52% on average for LLMs evaluating U.S. classroom transcripts.
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Beacon: Single-Turn Diagnosis and Mitigation of Latent Sycophancy in Large Language Models
Beacon is a new single-turn benchmark that measures latent sycophancy in LLMs, showing it decomposes into linguistic and affective sub-biases that scale with model capacity and can be modulated by prompt and activation interventions.
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GroupEnvoy: A Conversational Agent Speaking for the Outgroup to Foster Intergroup Relations
An AI agent representing outgroup views in ingroup chat directionally reduced intergroup anxiety and improved perspective-taking versus passive document exposure.
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Can AI Help You Get Over Your Breakup? One Session with a Belief-Reframing Chatbot Shows Sustained Distress Reduction
A pre-registered RCT found that one session with a belief-reframing AI chatbot produced significantly greater reductions in breakup distress than a survey-only control at 7 days, with a smaller effect persisting at 1 month.
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The Role of Emotional Stimuli and Intensity in Shaping Large Language Model Behavior
Positive emotional prompts improve LLM accuracy and reduce toxicity but increase sycophantic agreement, while negative emotions show the reverse pattern.
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The Rise of AI Companions: Interaction with AI Companions and Psychological Well-being
Survey and chat data from CharacterAI users link companionship-focused AI use to lower well-being, with stronger ties for users who have small offline networks and engage intensively or disclosively.
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Teaching Astronomy with Large Language Models
Structured integration of LLMs in astronomy education, including a domain-specific tutor and documentation requirements, leads to improved AI literacy and reduced student reliance on AI over the semester.