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The emergent few-shot reasoning capabilities of Large Language Models (LLMs) have excited the natural language and machine learning community over recent years. Despite of numerous successful applications, the underlying mechanism of such…

计算与语言 · 计算机科学 2023-06-09 Xiaojuan Tang , Zilong Zheng , Jiaqi Li , Fanxu Meng , Song-Chun Zhu , Yitao Liang , Muhan Zhang

Large language models (LLMs) have exhibited impressive capabilities across a myriad of tasks, yet they occasionally yield undesirable outputs. We posit that these limitations are rooted in the foundational autoregressive architecture of…

计算与语言 · 计算机科学 2025-03-03 Cheng Yang , Chufan Shi , Siheng Li , Bo Shui , Yujiu Yang , Wai Lam

Large Language Models (LLMs) represent a class of deep learning models adept at understanding natural language and generating coherent responses to various prompts or queries. These models far exceed the complexity of conventional neural…

机器学习 · 计算机科学 2024-12-05 Minghao Shao , Abdul Basit , Ramesh Karri , Muhammad Shafique

Since the advent of ChatGPT, Large Language Models (LLMs) have excelled in various tasks but remain as black-box systems. Understanding the reasoning bottlenecks of LLMs has become a critical challenge, as these limitations are deeply tied…

计算与语言 · 计算机科学 2024-12-24 Zifan Zheng , Yezhaohui Wang , Yuxin Huang , Shichao Song , Mingchuan Yang , Bo Tang , Feiyu Xiong , Zhiyu Li

In this position paper, we advocate for the development of conversational technology that is inherently designed to support and facilitate argumentative processes. We argue that, at present, large language models (LLMs) are inadequate for…

Analogical reasoning -- the capacity to identify and map structural relationships between different domains -- is fundamental to human cognition and learning. Recent studies have shown that large language models (LLMs) can sometimes match…

计算与语言 · 计算机科学 2025-11-21 Sam Musker , Alex Duchnowski , Raphaël Millière , Ellie Pavlick

Achieving human-level intelligence requires refining the transition from the fast, intuitive System 1 to the slower, more deliberate System 2 reasoning. While System 1 excels in quick, heuristic decisions, System 2 relies on logical…

The application of role-playing large language models (LLMs) is rapidly expanding in both academic and commercial domains, driving an increasing demand for high-precision role-playing models. Simultaneously, the rapid advancement of…

计算与语言 · 计算机科学 2025-07-23 Xiachong Feng , Longxu Dou , Lingpeng Kong

The widespread adoption of Large Language Models (LLMs) and publicly available ChatGPT have marked a significant turning point in the integration of Artificial Intelligence (AI) into people's everyday lives. This study examines the ability…

计算与语言 · 计算机科学 2025-10-28 Sandeep Kumar , Tirthankar Ghosal , Vinayak Goyal , Asif Ekbal

Large Language Models (LLMs) have demonstrated impressive capabilities in structured reasoning and symbolic tasks, with coding emerging as a particularly successful application. This progress has naturally motivated efforts to extend these…

人工智能 · 计算机科学 2026-02-02 Andrea Asperti , Alberto Naibo , Claudio Sacerdoti Coen

Recent benchmarks reveal that despite strong reasoning capabilities, large language models (LLMs) still struggle to faithfully apply complex contextual knowledge. These failures are often not wholesale reasoning collapses: in context-rich…

计算与语言 · 计算机科学 2026-05-27 Hongbo Jin , Chi Wang , Haoran Tang , Zhongjing Du , Xu Jiang , Jingqi Tian , Qiaoman Zhang , Jiayu Ding

With the development of large language models (LLMs) like the GPT series, their widespread use across various application scenarios presents a myriad of challenges. This review initially explores the issue of domain specificity, where LLMs…

计算与语言 · 计算机科学 2023-10-23 Xiaoliang Chen , Liangbin Li , Le Chang , Yunhe Huang , Yuxuan Zhao , Yuxiao Zhang , Dinuo Li

In recent advancements, large language models (LLMs) have exhibited proficiency in code generation and chain-of-thought reasoning, laying the groundwork for tackling automatic formal planning tasks. This study evaluates the potential of…

Large language models (LLMs) have demonstrated significant capabilities in natural language processing and reasoning, yet their effectiveness in autonomous planning has been under debate. While existing studies have utilized LLMs with…

计算与语言 · 计算机科学 2025-01-24 Bilgehan Sel , Ruoxi Jia , Ming Jin

Efforts have been made to make machines converse like humans in the past few decades. The recent techniques of Large Language Models (LLMs) make it possible to have human-like conversations with machines, but LLM's flaws of lacking…

计算机科学中的逻辑 · 计算机科学 2025-02-14 Yankai Zeng

Large Language Models (LLMs) have shown remarkable performance in various natural language processing tasks but face challenges in mathematical reasoning, where complex problem-solving requires both linguistic understanding and mathematical…

计算与语言 · 计算机科学 2025-03-20 Shuguang Chen , Guang Lin

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

Large language models (LLMs) are advanced artificial intelligence (AI) systems that can perform a variety of tasks commonly found in human intelligence tests, such as defining words, performing calculations, and engaging in verbal…

计算与语言 · 计算机科学 2024-09-12 David Ilić , Gilles E. Gignac

Large language models (LLMs) have demonstrated significant potential in the realm of natural language understanding and programming code processing tasks. Their capacity to comprehend and generate human-like code has spurred research into…

Large language models (LLMs) have exploded in popularity in the past few years and have achieved undeniably impressive results on benchmarks as varied as question answering and text summarization. We provide a simple new prompting strategy…

计算与语言 · 计算机科学 2022-12-14 Joshua Albrecht , Ellie Kitanidis , Abraham J. Fetterman
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