Small language models (SLMs) enable scalable tool-augmented multi-agent systems where multiple SLMs handle subtasks orchestrated by a powerful coordinator.However, they struggle with tool-use tasks, particularly in selecting appropriate tools and identifying correct parameters.A common failure mode is schema misalignment: models hallucinate plausible tool names that are absent from the provided tool schema, due to different naming conventions internalized during pretraining.Rather than training models to adapt to unfamiliar schemas, we propose adapting schemas to align with models' pretrained knowledge.We introduce PA-Tool (Pretraining-Aligned Tool Schema Generation), a training-free method that leverages peakedness, a signal used in contamination detection that indicates pretraining familiarity, to rename tool components.By generating multiple candidates and selecting the candidate with the highest peakedness, PA-Tool identifies pretrainingaligned naming patterns.Experiments on Meta-Tool and RoTBench show improvements of up to 17%, with schema misalignment errors reduced by 80%.PA-Tool enables small models to substantially improve tool-use accuracy without retraining, showing that schema-level interventions can unlock the tool-use potential of resource-efficient models.Our code is available at https://github.com/holi-lab/P A-Tool.

The importance of benchmarks for assessing the values of language models has been pronounced due to the growing need of more authentic, human-aligned responses.However, existing benchmarks rely on human or machine annotations that are vulnerable to value-related biases.Furthermore, the tested scenarios often diverge from real-world contexts in which models are commonly used to generate text and express values.To address these issues, we propose the Value Portrait benchmark, a reliable framework for evaluating LLMs' value orientations with two key characteristics.First, the benchmark consists of items that capture real-life user-LLM interactions, enhancing the relevance of assessment results to real-world LLM usage.Second, each item is rated by human subjects based on its similarity to their own thoughts, and correlations between these ratings and the subjects' actual value scores are derived.This psychometrically validated approach ensures that items strongly correlated with specific values serve as reliable items for assessing those values.Through evaluating 44 LLMs with our benchmark, we find that these models prioritize Benevolence, Security, and Self-Direction values while placing less emphasis on Tradition, Power, and Achievement values.Also, our analysis reveals biases in how LLMs perceive various demographic groups, deviating from real human data. 1

Visual persuasion, which uses visual elements to influence cognition and behaviors, is crucial in fields such as advertising and politicalcommunication. With recent advancements in artificial intelligence, there is growing potential to develop persuasive systems that automatically generate persuasive images tailored to individuals. However, a significant bottleneck in this area is the lack of comprehensivedatasets that connect the persuasiveness of images with the personal information about those who evaluated the images. To address this gap and facilitate technological advancements in personalized visual persuasion, we release the Personalized Visual Persuasion (PVP) dataset, comprising 28,454 persuasive images across 596 messages and 9 persuasion strategies. Importantly, the PVP dataset provides persuasiveness scores of images evaluated by 2,521 human annotators, along with their demographic and psychological characteristics (personality traits and values). We demonstrate the utility of our dataset by developing a persuasive image generator and an automated evaluator, and establish benchmark baselines. Our experiments reveal that incorporating psychological characteristics enhances the generation and evaluation of persuasive images, providing valuable insights for personalized visual persuasion.