Computational value analysis has largely treated values as instances of predefined universal taxonomies, making it difficult to discover culturally specific value dimensions not fully represented in existing schemes. We formulate value analysis as an open-ended discovery problem grounded in the argumentative contexts in which values are expressed. To this end, we propose an LLM-assisted framework that discovers human value dimensions directly from text without relying on predefined taxonomies. It extracts Premise-Conclusion-Stance (P-C-S) argumentative edges under explicit argument tree constraints, expands each edge into structured value-semantic descriptions spanning goal, principle, tradeoff, and perspective dimensions, and induces coherent value clusters via co-association consensus clustering over the embedding representations. We evaluate the framework across ten languages using external label alignment, internal structural stability metrics, and human judgment. Results show that the framework is robust and transferable, with the induced value structures achieving low hallucination and strong alignment with human judgments while revealing culturally distinctive value orientations that both align with and meaningfully complement established psychological frameworks. By shifting computational value analysis from taxonomy-driven classification toward open-ended discovery, our framework offers a new approach to cross-cultural value research, with implications for culturally aware AI alignment.

The rapid development of large language models (LLMs) gives rise to ethical concerns about their performance, while opening new avenues for developing toxic language detection techniques. However, LLMs’ unethical output and their capability of detecting toxicity have primarily been tested on language data that do not demand complex meaning inference, such as the biased associations of ‘he’ with programmer and ‘she’ with household. Nowadays toxic language adopts a much more creative range of implicit forms, thanks to advanced censorship. In this study, we collect authentic toxic interactions that evade online censorship and that are verified by human annotators as inference-intensive. To evaluate and improve LLMs’ reasoning of the authentic implicit toxic language, we propose a new prompting method, Pragmatic Inference Chain (PIC), drawn on interdisciplinary findings from cognitive science and linguistics. The PIC prompting significantly improves the success rate of GPT-4o, Llama-3.1-70B-Instruct, DeepSeek-v2.5, and DeepSeek-v3 in identifying implicit toxic language, compared to five baseline prompts, such as CoT and rule-based baselines. In addition, it also facilitates the models to produce more explicit and coherent reasoning processes, hence can potentially be generalized to other inference-intensive tasks, e.g., understanding humour and metaphors.

With the different roles that AI is expected to play in human life, imbuing large language models (LLMs) with different personalities has attracted increasing research interest. While the “personification” enhances human experiences of interactivity and adaptability of LLMs, it gives rise to critical concerns about content safety, particularly regarding bias, sentiment, and toxicity of LLM generation. This study explores how assigning different personality traits to LLMs affects the toxicity and biases of their outputs. Leveraging the widely accepted HEXACO personality framework developed in social psychology, we design experimentally sound prompts to test three LLMs’ performance on three toxic and bias benchmarks. The findings demonstrate the sensitivity of all three models to HEXACO personality traits and, more importantly, a consistent variation in the biases, negative sentiment, and toxicity of their output. In particular, adjusting the levels of several personality traits can effectively reduce bias and toxicity in model performance, similar to humans’ correlations between personality traits and toxic behaviors. The findings highlight the additional need to examine content safety besides the efficiency of training or fine-tuning methods for LLM personification, they also suggest a potential for the adjustment of personalities to be a simple and low-cost method to conduct controlled text generation.