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The most recent large language models(LLMs) such as ChatGPT and GPT-4 have shown exceptional capabilities of generalist models, achieving state-of-the-art performance on a wide range of NLP tasks with little or no adaptation. How effective…

计算与语言 · 计算机科学 2023-10-12 Xianzhi Li , Samuel Chan , Xiaodan Zhu , Yulong Pei , Zhiqiang Ma , Xiaomo Liu , Sameena Shah

Large language models (LLMs) demonstrate extraordinary abilities in a wide range of natural language processing (NLP) tasks. In this paper, we show that, beyond text understanding capability, LLMs are capable of processing text layouts that…

计算与语言 · 计算机科学 2024-08-29 Weiming Li , Manni Duan , Dong An , Yan Shao

Despite their success in many natural language tasks, solving math problems remains a significant challenge for large language models (LLMs). A large gap exists between LLMs' pass-at-one and pass-at-N performance in solving math problems,…

计算与语言 · 计算机科学 2023-10-17 Yixin Liu , Avi Singh , C. Daniel Freeman , John D. Co-Reyes , Peter J. Liu

We study the performance of a commercially available large language model (LLM) known as ChatGPT on math word problems (MWPs) from the dataset DRAW-1K. To our knowledge, this is the first independent evaluation of ChatGPT. We found that…

计算与语言 · 计算机科学 2023-03-01 Paulo Shakarian , Abhinav Koyyalamudi , Noel Ngu , Lakshmivihari Mareedu

Pretrained large language models (LLMs) are widely used in many sub-fields of natural language processing (NLP) and generally known as excellent few-shot learners with task-specific exemplars. Notably, chain of thought (CoT) prompting, a…

计算与语言 · 计算机科学 2023-01-31 Takeshi Kojima , Shixiang Shane Gu , Machel Reid , Yutaka Matsuo , Yusuke Iwasawa

Evaluating reasoning ability in Large Language Models (LLMs) is important for advancing artificial intelligence, as it transcends mere linguistic task performance. It involves understanding whether these models truly understand information,…

人工智能 · 计算机科学 2025-10-29 Benjamin Grando Moreira

Large Language Models (LLMs) have recently developed new advanced functionalities. Their effectiveness relies on statistical learning and generalization capabilities. However, they face limitations in internalizing the data they process and…

机器学习 · 计算机科学 2026-01-14 Farah Ben Slama , Frédéric Armetta

We investigate the mathematical capabilities of two iterations of ChatGPT (released 9-January-2023 and 30-January-2023) and of GPT-4 by testing them on publicly available datasets, as well as hand-crafted ones, using a novel methodology. In…

Large language models (LLMs) can solve problems step-by-step. While this chain-of-thought (CoT) reasoning boosts LLMs' performance, it is unclear if LLMs \textit{know} when to use CoT and whether those CoT are always necessary to answer the…

计算与语言 · 计算机科学 2024-03-21 Cheng-Han Chiang , Hung-yi Lee

This study explores the application of Large Language Models (LLMs), specifically GPT-4, in the analysis of classroom dialogue, a crucial research task for both teaching diagnosis and quality improvement. Recognizing the knowledge-intensive…

计算与语言 · 计算机科学 2024-10-08 Yun Long , Haifeng Luo , Yu Zhang

Artificial intelligence is making spectacular progress, and one of the best examples is the development of large language models (LLMs) such as OpenAI's GPT series. In these lectures, written for readers with a background in mathematics or…

计算与语言 · 计算机科学 2023-10-09 Michael R. Douglas

This paper assesses the ability of large language models (LLMs) to translate texts that include inter-sentential dependencies. We use the English-French DiscEvalMT benchmark (Bawden et al., 2018) with pairs of sentences containing…

计算与语言 · 计算机科学 2026-03-09 Shabnam Ataee , Hugo Huart , Andrei Popescu-Belis

In this paper, we explore how to leverage large language models (LLMs) to solve mathematical problems efficiently and accurately. Specifically, we demonstrate the effectiveness of classifying problems into distinct categories and employing…

计算与语言 · 计算机科学 2024-12-24 Amogh Akella

This paper investigates the effectiveness of large language models (LLMs) in answering questions over datasets. We examine their performance in two scenarios: (a) directly answering questions given a dataset file as input, and (b)…

计算与语言 · 计算机科学 2026-05-12 Andreas Xenofontos , Pavlos Fafalios

Reasoning is a central capability of human intelligence. In recent years, with the advent of large-scale datasets, pretrained large language models have emerged with new capabilities, including reasoning. However, these models still…

计算与语言 · 计算机科学 2025-03-03 Shu Wang , Lei Ji , Renxi Wang , Wenxiao Zhao , Haokun Liu , Yifan Hou , Ying Nian Wu

Large language models (LLMs) such as ChatGPT have gained considerable interest across diverse research communities. Their notable ability for text completion and generation has inaugurated a novel paradigm for language-interfaced problem…

Large Language Models (LLMs) are increasingly utilized in AI-driven educational instruction and assessment, particularly within mathematics education. The capability of LLMs to generate accurate answers and detailed solutions for math…

人工智能 · 计算机科学 2025-08-15 Liang Zhang , Edith Aurora Graf

Large Language Models (LLMs) have demonstrated remarkable zero-shot generalization across various language-related tasks, including search engines. However, existing work utilizes the generative ability of LLMs for Information Retrieval…

计算与语言 · 计算机科学 2024-12-31 Weiwei Sun , Lingyong Yan , Xinyu Ma , Shuaiqiang Wang , Pengjie Ren , Zhumin Chen , Dawei Yin , Zhaochun Ren

In 2022, with the release of ChatGPT, large-scale language models gained widespread attention. ChatGPT not only surpassed previous models in terms of parameters and the scale of its pretraining corpus but also achieved revolutionary…

人工智能 · 计算机科学 2024-11-13 Yiming Ju , Huanhuan Ma

Large-scale language models (LLMs) has shown remarkable capability in various of Natural Language Processing (NLP) tasks and attracted lots of attention recently. However, some studies indicated that large language models fail to achieve…

计算与语言 · 计算机科学 2025-03-18 Fanyi Qu , Chenming Tang , Yunfang Wu