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相关论文: Explaining Large Language Models with gSMILE

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The rapid advancement of generative artificial intelligence has enabled models capable of producing complex textual and visual outputs; however, their decision-making processes remain largely opaque, limiting trust and accountability in…

人工智能 · 计算机科学 2026-02-03 Zeinab Dehghani

Purpose: Large Language Models (LLMs) like GPT (Generative Pre-trained Transformer) from OpenAI and LLaMA (Large Language Model Meta AI) from Meta AI are increasingly recognized for their potential in the field of cheminformatics,…

生物大分子 · 定量生物学 2024-05-22 Shaghayegh Sadeghi , Alan Bui , Ali Forooghi , Jianguo Lu , Alioune Ngom

Generative AI, such as Large Language Models (LLMs), has achieved impressive progress but still produces hallucinations and unverifiable claims, limiting reliability in sensitive domains. Retrieval-Augmented Generation (RAG) improves…

Recent large vision-language models (LVLMs) have advanced capabilities in visual question answering (VQA). However, interpreting where LVLMs direct their visual attention remains a significant challenge, yet is essential for understanding…

计算机视觉与模式识别 · 计算机科学 2025-07-30 Guanxi Shen

Causal reasoning is a core component of intelligence. Large language models (LLMs) have shown impressive capabilities in generating human-like text, raising questions about whether their responses reflect true understanding or statistical…

人工智能 · 计算机科学 2025-06-09 Hanna M. Dettki , Brenden M. Lake , Charley M. Wu , Bob Rehder

Large Language Models (LLMs) are advancing at a rapid pace, with significant improvements at natural language processing and coding tasks. Yet, their ability to work with formal languages representing data, specifically within the realm of…

人工智能 · 计算机科学 2023-10-02 Johannes Frey , Lars-Peter Meyer , Natanael Arndt , Felix Brei , Kirill Bulert

Generative AI and Large Language Models (LLMs) hold promise for automating spreadsheet formula creation. However, due to hallucinations, bias and variable user skill, outputs obtained from generative AI cannot be assumed to be accurate or…

人机交互 · 计算机科学 2025-01-20 Simon Thorne

Commonsense reasoning is a difficult task for a computer, but a critical skill for an artificial intelligence (AI). It can enhance the explainability of AI models by enabling them to provide intuitive and human-like explanations for their…

人工智能 · 计算机科学 2024-07-08 Stefanie Krause , Frieder Stolzenburg

Despite recent advancements in Instruct-based Image Editing models for generating high-quality images, they are known as black boxes and a significant barrier to transparency and user trust. To solve this issue, we introduce SMILE…

The adoption of Large Language Models (LLMs) for code generation in data science offers substantial potential for enhancing tasks such as data manipulation, statistical analysis, and visualization. However, the effectiveness of these models…

Machine learning is currently undergoing an explosion in capability, popularity, and sophistication. However, one of the major barriers to widespread acceptance of machine learning (ML) is trustworthiness: most ML models operate as black…

Big Language Models (LLMs) are changing the way businesses use software, the way people live their lives and the way industries work. Companies like Google, High-Flyer, Anthropic, OpenAI and Meta are making better LLMs. So, it's crucial to…

人工智能 · 计算机科学 2025-10-28 Urja Kohli , Aditi Singh , Arun Sharma

We propose a collaborative framework in which multiple large language models -- including GPT-4-0125-preview, Meta-LLaMA-3-70B-Instruct, Claude-3-Opus, and Gemini-1.5-Flash -- generate and answer complex, PhD-level statistical questions…

计算与语言 · 计算机科学 2025-02-25 Alireza Amiri-Margavi , Iman Jebellat , Ehsan Jebellat , Seyed Pouyan Mousavi Davoudi

We introduce a new approach in which several advanced large language models-specifically GPT-4-0125-preview, Meta-LLAMA-3-70B-Instruct, Claude-3-Opus, and Gemini-1.5-Flash-collaborate to both produce and answer intricate, doctoral-level…

Large Language Models (LLMs) have made significant progress in reasoning, demonstrating their capability to generate human-like responses. This study analyzes the problem-solving capabilities of LLMs in the domain of thermodynamics. A…

计算工程、金融与科学 · 计算机科学 2025-12-18 Rebecca Loubet , Pascal Zittlau , Luisa Vollmer , Marco Hoffmann , Sophie Fellenz , Fabian Jirasek , Heike Leitte , Hans Hasse

This paper investigates the suitability of frontier Large Language Models (LLMs) for Q&A interactions in science centres, with the aim of boosting visitor engagement while maintaining factual accuracy. Using a dataset of questions collected…

人工智能 · 计算机科学 2024-12-09 Jacob Watson , Fabrício Góes , Marco Volpe , Talles Medeiros

Assessing the reliability of Large Language Models (LLMs) by confidence elicitation is a prominent approach to AI safety in high-stakes applications, such as healthcare and finance. Existing methods either require expensive computational…

计算与语言 · 计算机科学 2026-04-08 Zhaohan Zhang , Ziquan Liu , Ioannis Patras

Neurological disorders that affect speech production, such as Alzheimer's Disease (AD), significantly impact the lives of both patients and caregivers, whether through social, psycho-emotional effects or other aspects not yet fully…

Large Language Models (LLMs) like OpenAI's GPT series, Anthropic's Claude, and Meta's LLaMa have shown remarkable capabilities in text generation. However, their susceptibility to toxic prompts presents significant security challenges. This…

密码学与安全 · 计算机科学 2024-12-03 Jie Li , Yi Liu , Chongyang Liu , Xiaoning Ren , Ling Shi , Weisong Sun , Yinxing Xue

Large language models (LLMs) have shown significant general language understanding abilities. However, there has been a scarcity of attempts to assess the logical reasoning capacities of these LLMs, an essential facet of natural language…

计算与语言 · 计算机科学 2025-04-22 Hanmeng liu , Zhiyang Teng , Ruoxi Ning , Yiran Ding , Xiulai Li , Xiaozhang Liu , Yue Zhang
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