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Large Language Models (LLMs) are increasingly being used in education, yet their correctness alone does not capture the quality, reliability, or pedagogical validity of their problem-solving behavior, especially in mathematics, where…

Computers and Society · Computer Science 2025-10-22 Sagnik Dakshit , Sushmita Sinha Roy

Large Language Models (LLMs) have transformed the Natural Language Processing (NLP) landscape with their remarkable ability to understand and generate human-like text. However, these models are prone to ``hallucinations'' -- outputs that do…

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…

Computation and Language · Computer Science 2025-03-20 Shuguang Chen , Guang Lin

Multi-modal Large Language Models (MLLMs) are increasingly prominent in the field of artificial intelligence. These models not only excel in traditional vision-language tasks but also demonstrate impressive performance in contemporary…

Computer Vision and Pattern Recognition · Computer Science 2023-12-06 Xiaotian Han , Quanzeng You , Yongfei Liu , Wentao Chen , Huangjie Zheng , Khalil Mrini , Xudong Lin , Yiqi Wang , Bohan Zhai , Jianbo Yuan , Heng Wang , Hongxia Yang

The emergence of large language models (LLMs) has marked a significant breakthrough in natural language processing (NLP), fueling a paradigm shift in information acquisition. Nevertheless, LLMs are prone to hallucination, generating…

Computation and Language · Computer Science 2024-11-20 Lei Huang , Weijiang Yu , Weitao Ma , Weihong Zhong , Zhangyin Feng , Haotian Wang , Qianglong Chen , Weihua Peng , Xiaocheng Feng , Bing Qin , Ting Liu

We propose a novel framework for comprehending the reasoning capabilities of large language models (LLMs) through the perspective of meta-learning. By conceptualizing reasoning trajectories as pseudo-gradient descent updates to the LLM's…

Computation and Language · Computer Science 2025-05-27 Junnan Liu , Hongwei Liu , Linchen Xiao , Shudong Liu , Taolin Zhang , Zihan Ma , Songyang Zhang , Kai Chen

As Large Language Models (LLMs) are increasingly being employed in real-world applications in critical domains such as healthcare, it is important to ensure that the Chain-of-Thought (CoT) reasoning generated by these models faithfully…

Computation and Language · Computer Science 2024-07-02 Sree Harsha Tanneru , Dan Ley , Chirag Agarwal , Himabindu Lakkaraju

Exploiting large language models (LLMs) to tackle reasoning has garnered growing attention. It still remains highly challenging to achieve satisfactory results in complex logical problems, characterized by plenty of premises within the…

Computation and Language · Computer Science 2025-03-17 Junjie Liu , Shaotian Yan , Chen Shen , Zhengdong Xiao , Liang Xie , Wenxiao Wang , Jieping Ye

Large language models (LLMs) have recently shown great potential for in-context learning, where LLMs learn a new task simply by conditioning on a few input-label pairs (prompts). Despite their potential, our understanding of the factors…

Computation and Language · Computer Science 2023-09-12 Ruixiang Tang , Dehan Kong , Longtao Huang , Hui Xue

The causal capabilities of large language models (LLMs) are a matter of significant debate, with critical implications for the use of LLMs in societally impactful domains such as medicine, science, law, and policy. We conduct a "behavorial"…

Artificial Intelligence · Computer Science 2024-08-21 Emre Kıcıman , Robert Ness , Amit Sharma , Chenhao Tan

Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, leading to their adoption in high-stakes domains such as healthcare, law, and scientific research. However, their reasoning often contains subtle logical…

Software Engineering · Computer Science 2025-12-30 Xinyi Zheng , Ningke Li , Xiaokun Luan , Kailong Wang , Ling Shi , Meng Sun , Haoyu Wang

Large Language Models (LLMs) have rapidly transformed the landscape of artificial intelligence, enabling natural language interfaces and dynamic orchestration of software components. However, their reliance on probabilistic inference limits…

Machine Learning · Computer Science 2025-07-01 Claudionor Coelho , Yanen Li , Philip Tee

The real-world information sources are inherently multilingual, which naturally raises a question about whether language models can synthesize information across languages. In this paper, we introduce a simple two-hop question answering…

Computation and Language · Computer Science 2026-01-13 Yan Meng , Wafaa Mohammed , Christof Monz

While large language models (LLMs) have demonstrated strong capability in structured prediction tasks such as semantic parsing, few amounts of research have explored the underlying mechanisms of their success. Our work studies different…

Computation and Language · Computer Science 2023-02-01 Daking Rai , Yilun Zhou , Bailin Wang , Ziyu Yao

Large language models (LLMs) are designed to perform a wide range of tasks. To improve their ability to solve complex problems requiring multi-step reasoning, recent research leverages process reward modeling to provide fine-grained…

Computation and Language · Computer Science 2025-09-29 Weixuan Wang , Minghao Wu , Barry Haddow , Alexandra Birch

Large language models (LLMs) demonstrate remarkable breadth of knowledge, yet their ability to reason about computational processes remains poorly understood. Closing this gap matters for practitioners who rely on LLMs to guide algorithm…

Computation and Language · Computer Science 2026-04-07 Sohan Venkatesh , Ashish Mahendran Kurapath , Tejas Melkote

Large Language Models (LLMs) often exhibit sycophancy, distorting responses to align with user beliefs, notably by readily agreeing with user counterarguments. Paradoxically, LLMs are increasingly adopted as successful evaluative agents for…

Computation and Language · Computer Science 2025-09-23 Sungwon Kim , Daniel Khashabi

Multimodal Large Language Models (MLLMs) are gaining increasing popularity in both academia and industry due to their remarkable performance in various applications such as visual question answering, visual perception, understanding, and…

Computation and Language · Computer Science 2024-09-09 Jian Li , Weiheng Lu , Hao Fei , Meng Luo , Ming Dai , Min Xia , Yizhang Jin , Zhenye Gan , Ding Qi , Chaoyou Fu , Ying Tai , Wankou Yang , Yabiao Wang , Chengjie Wang

Large Language Models (LLMs) have benefited enormously from scaling, yet these gains are bounded by five fundamental limitations: (1) hallucination, (2) context compression, (3) reasoning degradation, (4) retrieval fragility, and (5)…

Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks. However, the auto-regressive generation process makes LLMs prone to produce errors, hallucinations and inconsistent statements when…

Artificial Intelligence · Computer Science 2024-07-23 Chaojie Wang , Yanchen Deng , Zhiyi Lyu , Liang Zeng , Jujie He , Shuicheng Yan , Bo An
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