ProMediate introduces a theory-grounded simulation testbed and socio-cognitive metrics to evaluate proactive AI mediator agents in multi-party multi-topic negotiations, with experiments showing a socially intelligent mediator improves consensus change and intervention speed over a generic baseline.
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A hypernetwork generates meta-gating parameters for SwiGLU blocks to let LLMs adapt their nonlinearity to arbitrary textual conditions, outperforming finetuning and meta-learning baselines with reasonable generalization to unseen cases.
PeReGrINE is a graph-based benchmark that restructures Amazon Reviews 2023 with temporal cutoffs and introduces dissonance analysis to measure how well retrieval-conditioned models match user style and product consensus.
Emotional perturbations induced via activation steering systematically alter strategic choices made by small language model agents in cooperative and competitive game templates, yet the resulting behaviors remain unstable and only partially aligned with human patterns.
ToxPrune prunes toxic subwords from BPE tokenizers in LLMs to mitigate toxic dialogue responses and improve diversity on both toxic and non-toxic models.
RECAP is an inference-time framework using cognitive appraisal theory to enhance emotional alignment and transparency in medical dialogue systems across model scales.
Introduces PAS and FAS task abstractions plus the LLM-S^3 benchmark to evaluate LLMs on generating sociodemographic survey responses across 11 real datasets and multiple models.
citing papers explorer
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ProMediate: A Socio-cognitive framework for evaluating proactive agents in multi-party negotiation
ProMediate introduces a theory-grounded simulation testbed and socio-cognitive metrics to evaluate proactive AI mediator agents in multi-party multi-topic negotiations, with experiments showing a socially intelligent mediator improves consensus change and intervention speed over a generic baseline.
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Learn-to-learn on Arbitrary Textual Conditioning: A Hypernetwork-Driven Meta-Gated LLM
A hypernetwork generates meta-gating parameters for SwiGLU blocks to let LLMs adapt their nonlinearity to arbitrary textual conditions, outperforming finetuning and meta-learning baselines with reasonable generalization to unseen cases.
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PeReGrINE: Evaluating Personalized Review Fidelity with User Item Graph Context
PeReGrINE is a graph-based benchmark that restructures Amazon Reviews 2023 with temporal cutoffs and introduces dissonance analysis to measure how well retrieval-conditioned models match user style and product consensus.
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On Emotion-Sensitive Decision Making of Small Language Model Agents
Emotional perturbations induced via activation steering systematically alter strategic choices made by small language model agents in cooperative and competitive game templates, yet the resulting behaviors remain unstable and only partially aligned with human patterns.
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Toxic Subword Pruning for Dialogue Response Generation on Large Language Models
ToxPrune prunes toxic subwords from BPE tokenizers in LLMs to mitigate toxic dialogue responses and improve diversity on both toxic and non-toxic models.
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RECAP: Transparent Inference-Time Emotion Alignment for Medical Dialogue Systems
RECAP is an inference-time framework using cognitive appraisal theory to enhance emotional alignment and transparency in medical dialogue systems across model scales.
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Large Language Models as Virtual Survey Respondents: Evaluating Sociodemographic Response Generation
Introduces PAS and FAS task abstractions plus the LLM-S^3 benchmark to evaluate LLMs on generating sociodemographic survey responses across 11 real datasets and multiple models.
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