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Can LLMs Talk 'Sex'? Exploring How AI Models Handle Intimate Conversations
Huiqian Lai
https://arxiv.org/abs/2506.05514 https://

Can LLMs Talk 'Sex'? Exploring How AI Models Handle Intimate Conversations
This study examines how four prominent large language models (Claude 3.7 Sonnet, GPT-4o, Gemini 2.5 Flash, and Deepseek-V3) handle sexually oriented requests through qualitative content analysis. By evaluating responses to prompts ranging from explicitly sexual to educational and neutral control scenarios, the research reveals distinct moderation paradigms reflecting fundamentally divergent ethical positions. Claude 3.7 Sonnet employs strict and consistent prohibitions, while GPT-4o navigates u…

Interesting, "GPT-style models have a fixed memorization capacity of approximately 3.6 bits per parameter."
https://venturebeat.com/ai/how-much-information-do-llms-really-memorize-now-we-know-thanks-to-met…

How much information do LLMs really memorize? Now we know, thanks to Meta, Google, Nvidia and Cornell
Using a clever solution, researchers find GPT-style models have a fixed memorization capacity of approximately 3.6 bits per parameter.

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study
E. G. Santana Jr, Jander Pereira Santos Junior, Erlon P. Almeida, Iftekhar Ahmed, Paulo Anselmo da Mota Silveira Neto, Eduardo Santana de Almeida
https://arxiv.org/abs/2506.07594

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study
Test smells indicate poor development practices in test code, reducing maintainability and reliability. While developers often struggle to prevent or refactor these issues, existing tools focus primarily on detection rather than automated refactoring. Large Language Models (LLMs) have shown strong potential in code understanding and transformation, but their ability to both identify and refactor test smells remains underexplored. We evaluated GPT-4-Turbo, LLaMA 3 70B, and Gemini-1.5 Pro on Pyth…

FinBERT2: A Specialized Bidirectional Encoder for Bridging the Gap in Finance-Specific Deployment of Large Language Models
Xuan Xu, Fufang Wen, Beilin Chu, Zhibing Fu, Qinhong Lin, Jiaqi Liu, Binjie Fei, Zhongliang Yang, Linna Zhou, Yu Li
https://arxiv.org/abs/2506.06335

FinBERT2: A Specialized Bidirectional Encoder for Bridging the Gap in Finance-Specific Deployment of Large Language Models
In natural language processing (NLP), the focus has shifted from encoder-only tiny language models like BERT to decoder-only large language models(LLMs) such as GPT-3. However, LLMs' practical application in the financial sector has revealed three limitations: (1) LLMs often perform worse than fine-tuned BERT on discriminative tasks despite costing much higher computational resources, such as market sentiment analysis in financial reports; (2) Application on generative tasks heavily relies on r…

This https://arxiv.org/abs/2505.18889 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Security Concerns for Large Language Models: A Survey
Large Language Models (LLMs) such as GPT-4 (and its recent iterations like GPT-4o and the GPT-4.1 series), Google's Gemini, Anthropic's Claude 3 models, and xAI's Grok have caused a revolution in natural language processing, but their capabilities also introduce new security vulnerabilities. In this survey, we provide a comprehensive overview of the emerging security concerns around LLMs, categorizing threats into prompt injection and jailbreaking, adversarial attacks (including input perturbat…

Do Language Models Mirror Human Confidence? Exploring Psychological Insights to Address Overconfidence in LLMs
Chenjun Xu, Bingbing Wen, Bin Han, Robert Wolfe, Lucy Lu Wang, Bill Howe
https://arxiv.org/abs/2506.00582

Do Language Models Mirror Human Confidence? Exploring Psychological Insights to Address Overconfidence in LLMs
Psychology research has shown that humans are poor at estimating their performance on tasks, tending towards underconfidence on easy tasks and overconfidence on difficult tasks. We examine three LLMs, Llama-3-70B-instruct, Claude-3-Sonnet, and GPT-4o, on a range of QA tasks of varying difficulty, and show that models exhibit subtle differences from human patterns of overconfidence: less sensitive to task difficulty, and when prompted to answer based on different personas -- e.g., expert vs laym…

Last leg on our brief history of NLP (so far) is the advent of large language models with GPT-3 in 2020 and the introduction of learning from the prompt (aka few-shot learning).
T. B. Brown et al. (2020). Language models are few-shot learners. NIPS'20
https://…

Facts are Harder Than Opinions -- A Multilingual, Comparative Analysis of LLM-Based Fact-Checking Reliability
Lorraine Saju, Arnim Bleier, Jana Lasser, Claudia Wagner
https://arxiv.org/abs/2506.03655

Facts are Harder Than Opinions -- A Multilingual, Comparative Analysis of LLM-Based Fact-Checking Reliability
The proliferation of misinformation necessitates scalable, automated fact-checking solutions. Yet, current benchmarks often overlook multilingual and topical diversity. This paper introduces a novel, dynamically extensible data set that includes 61,514 claims in multiple languages and topics, extending existing datasets up to 2024. Through a comprehensive evaluation of five prominent Large Language Models (LLMs), including GPT-4o, GPT-3.5 Turbo, LLaMA 3.1, and Mixtral 8x7B, we identify signific…

Am 3. April,15:00–16:30, bietet der Workshop "RAGtAIme – vom Custom GPT zum KI-Chatbot mit OpenAI Assistant" praxisnahe Einblicke in die Anwendung von KI im Bibliothekswesen. Uwe Dierolf (KIT-Bibliothek) erläutert RAG, Custom GPTs und die Entwicklung KI-gestützter Chatbots – auch ohne tiefgehende Programmierkenntnisse.
Anmeldung unter:

Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs
Nariman Naderi, Zahra Atf, Peter R Lewis, Aref Mahjoub far, Seyed Amir Ahmad Safavi-Naini, Ali Soroush
https://arxiv.org/abs/2506.00072

Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs
This paper investigates how prompt engineering techniques impact both accuracy and confidence elicitation in Large Language Models (LLMs) applied to medical contexts. Using a stratified dataset of Persian board exam questions across multiple specialties, we evaluated five LLMs - GPT-4o, o3-mini, Llama-3.3-70b, Llama-3.1-8b, and DeepSeek-v3 - across 156 configurations. These configurations varied in temperature settings (0.3, 0.7, 1.0), prompt styles (Chain-of-Thought, Few-Shot, Emotional, Exper…