This work evaluates the non-English and unstructured text compression performance of Large Language Models (LLMs) by comparing them with traditional baselines on datasets from eight most widely spoken languages. Experimental results show that the evaluated LLM (LLaMA-3.2-1B) was considerably outperformed by the baselines, particularly on non-English datasets, where its performance relative to the best baseline was more than three times worse than on English datasets on average. It also compressed unstructured English data up to more than twofold less effectively than plain English data. Traditional methods, however, remained largely dataset-agnostic. Surprisingly, the LLM achieved worse compression ratios on some datasets than others despite modeling them more accurately. Overall, the outcomes and substantially higher compression time and resource consumption indicate that current LLMs are highly impractical for the compression task, where traditional methods continue to excel. Codes are available at: https://github.com/mehranhaddadi13/llm_compress.

Anti-LGBTQIA+ texts in user-generated content pose significant risks to online safety and inclusivity. This study investigates the capabilities and limitations of five widely adopted Large Language Models (LLMs)—DeepSeek-V3, GPT-4o, GPT-4o-mini, GPT-o1-mini, and Llama3.3-70B—in detecting such harmful content. Our findings reveal that while LLMs demonstrate potential in identifying offensive language, their effectiveness varies across models and metrics, with notable shortcomings in calibration. Furthermore, linguistic analysis exposes deeply embedded patterns of discrimination, reinforcing the urgency for improved detection mechanisms for this marginalised population. In summary, this study demonstrates the significant potential of LLMs for practical application in detecting anti-LGBTQIA+ user-generated texts and provides valuable insights from text analysis that can inform topic modelling. These findings contribute to developing safer digital platforms and enhancing protection for LGBTQIA+ individuals.