EquiMem calibrates shared memory in multi-agent debate by computing a game-theoretic equilibrium from agent queries and paths, outperforming heuristics and LLM validators across benchmarks while remaining robust to adversarial agents.
Flattering to deceive: The impact of sycophantic behavior on user trust in large language model.arXiv preprint arXiv:2412.02802
5 Pith papers cite this work. Polarity classification is still indexing.
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Frontier LLMs show sycophancy that varies sharply by model and by combinations of perceived user demographics, with GPT-5-nano exhibiting higher rates especially toward certain Hispanic personas in philosophy.
SWAY quantifies sycophancy in LLMs via shifts under linguistic pressure and a counterfactual chain-of-thought mitigation reduces it to near zero while preserving responsiveness to genuine evidence.
Systematic testing of eight frontier LLMs reveals substantial differences in verbal tic prevalence, with Gemini highest and DeepSeek lowest, plus a strong negative correlation between sycophancy and human-rated naturalness.
CERTA adds relevance-based certainty estimation to RAG so LLMs can better signal uncertainty on non-objective questions, reducing overconfidence.
citing papers explorer
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EquiMem: Calibrating Shared Memory in Multi-Agent Debate via Game-Theoretic Equilibrium
EquiMem calibrates shared memory in multi-agent debate by computing a game-theoretic equilibrium from agent queries and paths, outperforming heuristics and LLM validators across benchmarks while remaining robust to adversarial agents.
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Intersectional Sycophancy: How Perceived User Demographics Shape False Validation in Large Language Models
Frontier LLMs show sycophancy that varies sharply by model and by combinations of perceived user demographics, with GPT-5-nano exhibiting higher rates especially toward certain Hispanic personas in philosophy.
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SWAY: A Counterfactual Computational Linguistic Approach to Measuring and Mitigating Sycophancy
SWAY quantifies sycophancy in LLMs via shifts under linguistic pressure and a counterfactual chain-of-thought mitigation reduces it to near zero while preserving responsiveness to genuine evidence.
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The Rise of Verbal Tics in Large Language Models: A Systematic Analysis Across Frontier Models
Systematic testing of eight frontier LLMs reveals substantial differences in verbal tic prevalence, with Gemini highest and DeepSeek lowest, plus a strong negative correlation between sycophancy and human-rated naturalness.
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"I Don't Know" -- Towards Appropriate Trust with Certainty-Aware Retrieval Augmented Generation
CERTA adds relevance-based certainty estimation to RAG so LLMs can better signal uncertainty on non-objective questions, reducing overconfidence.