Do LLMs produce texts with "human-like" lexical diversity?

A recent study indicates that advanced AI systems like ChatGPT and Gemini simulate an understanding of humor but do not genuinely comprehend jokes. This finding highlights a significant limitation in the capabilities of these AI models, which are often perceived as more intelligent than they are.

Privacy concerns surrounding large language models (LLMs) like ChatGPT, Anthropic, and Gemini have escalated, as highlighted by a Northeastern University computer science expert. The issues extend beyond the data these algorithms process, raising alarms about user privacy and data security.

A group is actively working to help individuals recognize and break free from their delusions related to artificial intelligence, particularly those who have become overly reliant on AI tools like ChatGPT. This phenomenon highlights a growing concern about the psychological impact of AI on users, as some individuals no longer feel the need for human interaction.

A key research leader involved in ChatGPT's mental health initiatives is departing from OpenAI, which raises questions about the future direction of AI safety research, particularly in how the chatbot interacts with users in crisis situations. This change comes at a time when OpenAI is expanding its features, including group chats and a free version for educators.

Marc Benioff, CEO of Salesforce, expressed his strong preference for Google's Gemini 3 over OpenAI's ChatGPT, stating, 'Holy S***… I’m Not Going Back to ChatGPT' after experiencing the new AI model. This statement highlights the growing competition between Google and OpenAI in the AI landscape.

A recent study highlights the limitations of Large Language Models (LLMs) in understanding Tunisian Arabic, also known as Tunizi. This research introduces a new dataset that includes parallel translations in Tunizi, standard Tunisian Arabic, and English, aiming to benchmark LLMs on their comprehension of this low-resource language. The findings indicate that the neglect of such dialects may hinder millions of Tunisians from engaging with AI in their native language.

A recent study on multilingual large language models (LLMs) reveals that language information is distinctly organized within their internal representation space, particularly showing significant separation in the first transformer block. This comprehensive probing study analyzed six multilingual LLMs across all 268 transformer layers, utilizing both linear and nonlinear probes alongside a new Token-Language Alignment analysis.

A new benchmark named MUCH has been introduced to assess Claim-level Uncertainty Quantification (UQ) in Large Language Models (LLMs). This benchmark includes 4,873 samples in English, French, Spanish, and German, and provides 24 generation logits per token, enhancing the evaluation of UQ methods under realistic conditions.