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Large Language Models (LLMs) offer a promising approach to enhancing Explainable AI (XAI) by transforming complex machine learning outputs into easy-to-understand narratives, making model predictions more accessible to users, and helping…

人工智能 · 计算机科学 2025-04-02 Ahsan Bilal , David Ebert , Beiyu Lin

Large language models (LLMs) can "lie", which we define as outputting false statements despite "knowing" the truth in a demonstrable sense. LLMs might "lie", for example, when instructed to output misinformation. Here, we develop a simple…

Large Language Models (LLMs) are becoming vital tools that help us solve and understand complex problems by acting as digital assistants. LLMs can generate convincing explanations, even when only given the inputs and outputs of these…

计算与语言 · 计算机科学 2024-10-14 Rohan Ajwani , Shashidhar Reddy Javaji , Frank Rudzicz , Zining Zhu

Reliably predicting the behavior of language models -- such as whether their outputs are correct or have been adversarially manipulated -- is a fundamentally challenging task. This is often made even more difficult as frontier language…

机器学习 · 计算机科学 2025-12-02 Dylan Sam , Marc Finzi , J. Zico Kolter

Existing tasks fall short in evaluating reasoning ability of Large Language Models (LLMs) in an interactive, unknown environment. This deficiency leads to the isolated assessment of deductive, inductive, and abductive reasoning, neglecting…

人工智能 · 计算机科学 2026-05-07 Congchi Yin , Tianyi Wu , Yankai Shu , Alex Gu , Yunhan Wang , Jun Shao , Xun Jiang , Piji Li

The rapid emergence of Large Language Models (LLMs) has precipitated a profound paradigm shift in Artificial Intelligence, delivering monumental engineering successes that increasingly impact modern society. However, a critical paradox…

计算与语言 · 计算机科学 2026-03-13 Zeyu Gan , Ruifeng Ren , Wei Yao , Xiaolin Hu , Gengze Xu , Chen Qian , Huayi Tang , Zixuan Gong , Xinhao Yao , Pengwei Tang , Zhenxing Dou , Yong Liu

Large Language Models (LLMs) have played a pivotal role in advancing Artificial Intelligence (AI). However, despite their achievements, LLMs often struggle to explain their decision-making processes, making them a 'black box' and presenting…

计算与语言 · 计算机科学 2025-06-30 Avash Palikhe , Zhenyu Yu , Zichong Wang , Wenbin Zhang

Large language models (LLMs) have shown significant potential to change how we write, communicate, and create, leading to rapid adoption across society. This dissertation examines how individuals and institutions are adapting to and…

计算与语言 · 计算机科学 2025-06-24 Weixin Liang

Large language models (LLMs) are the result of a massive experiment in bottom-up, data-driven reverse engineering of language at scale. Despite their utility in a number of downstream NLP tasks, ample research has shown that LLMs are…

人工智能 · 计算机科学 2024-08-05 Walid S. Saba

Large Language Models (LLMs) demonstrate remarkable performance in semantic understanding and generation, yet accurately assessing their output reliability remains a significant challenge. While numerous studies have explored calibration…

人工智能 · 计算机科学 2024-12-18 Liangru Xie , Hui Liu , Jingying Zeng , Xianfeng Tang , Yan Han , Chen Luo , Jing Huang , Zhen Li , Suhang Wang , Qi He

Large language models (LLMs) have achieved a milestone that undenia-bly changed many held beliefs in artificial intelligence (AI). However, there remains many limitations of these LLMs when it comes to true language understanding,…

计算与语言 · 计算机科学 2023-07-28 Walid S. Saba

Large Language Models (LLMs) have been transformative. They are pre-trained foundational models that are self-supervised and can be adapted with fine tuning to a wide range of natural language tasks, each of which previously would have…

计算与语言 · 计算机科学 2023-02-22 Terrence Sejnowski

Humans are accustomed to reading and writing in a forward manner, and this natural bias extends to text understanding in auto-regressive large language models (LLMs). This paper investigates whether LLMs, like humans, struggle with reverse…

计算与语言 · 计算机科学 2025-02-25 Sicheng Yu , Yuanchen Xu , Cunxiao Du , Yanying Zhou , Minghui Qiu , Qianru Sun , Hao Zhang , Jiawei Wu

Large Language Models (LLMs) exhibit remarkable capabilities across a spectrum of tasks in financial services, including report generation, chatbots, sentiment analysis, regulatory compliance, investment advisory, financial knowledge…

计算工程、金融与科学 · 计算机科学 2025-06-02 Hariom Tatsat , Ariye Shater

Artificial intelligence (AI) methods have become critical in scientific applications to help accelerate scientific discovery. Large language models (LLMs) are being considered as a promising approach to address some of the challenging…

The exponential growth of text-based data in domains such as healthcare, education, and social sciences has outpaced the capacity of traditional qualitative analysis methods, which are time-intensive and prone to subjectivity. Large…

Large Transformer models are capable of implementing a plethora of so-called in-context learning algorithms. These include gradient descent, classification, sequence completion, transformation, and improvement. In this work, we investigate…

人工智能 · 计算机科学 2024-02-29 Robert Tjarko Lange , Yingtao Tian , Yujin Tang

With recent Nobel Prizes recognising AI contributions to science, Large Language Models (LLMs) are transforming scientific research by enhancing productivity and reshaping the scientific method. LLMs are now involved in experimental design,…

This study explores the idea of AI Personality or AInality suggesting that Large Language Models (LLMs) exhibit patterns similar to human personalities. Assuming that LLMs share these patterns with humans, we investigate using…

计算与语言 · 计算机科学 2023-12-25 Yang Lu , Jordan Yu , Shou-Hsuan Stephen Huang

Large Language Models (LLMs) are recruited in applications that span from clinical assistance and legal support to question answering and education. Their success in specialized tasks has led to the claim that they possess human-like…

计算与语言 · 计算机科学 2024-07-10 Vittoria Dentella , Fritz Guenther , Elliot Murphy , Gary Marcus , Evelina Leivada
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