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相关论文: Are large language models superhuman chemists?

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Human evaluation is indispensable and inevitable for assessing the quality of texts generated by machine learning models or written by humans. However, human evaluation is very difficult to reproduce and its quality is notoriously unstable,…

计算与语言 · 计算机科学 2023-05-04 Cheng-Han Chiang , Hung-yi Lee

Artificial intelligence (AI) has played an increasingly important role in chemical research. However, most models currently used in chemistry are specialist models that require training and tuning for specific tasks. A more generic and…

计算与语言 · 计算机科学 2025-07-03 Zihan Zhao , Da Ma , Lu Chen , Liangtai Sun , Zihao Li , Yi Xia , Bo Chen , Hongshen Xu , Zichen Zhu , Su Zhu , Shuai Fan , Guodong Shen , Kai Yu , Xin Chen

This paper provides a comprehensive survey of the latest research on multilingual large language models (MLLMs). MLLMs not only are able to understand and generate language across linguistic boundaries, but also represent an important…

计算与语言 · 计算机科学 2024-11-20 Shaolin Zhu , Supryadi , Shaoyang Xu , Haoran Sun , Leiyu Pan , Menglong Cui , Jiangcun Du , Renren Jin , António Branco , Deyi Xiong

Large Language Models (LLMs) have emerged as powerful tools in various research domains. This article examines their potential through a literature review and firsthand experimentation. While LLMs offer benefits like cost-effectiveness and…

人机交互 · 计算机科学 2024-04-10 M. Namvarpour , A. Razi

Recently developed large language models (LLMs) have been shown to perform remarkably well on a wide range of language understanding tasks. But, can they really "reason" over the natural language? This question has been receiving…

计算与语言 · 计算机科学 2024-06-07 Mihir Parmar , Nisarg Patel , Neeraj Varshney , Mutsumi Nakamura , Man Luo , Santosh Mashetty , Arindam Mitra , Chitta Baral

With the advent of large language models (LLMs), there is a growing interest in applying LLMs to scientific tasks. In this work, we conduct an experimental study to explore applicability of LLMs for configuring, annotating, translating,…

人机交互 · 计算机科学 2025-10-13 Orcun Yildiz , Tom Peterka

Large Language Models (LLMs) are transforming artificial intelligence, enabling autonomous agents to perform diverse tasks across various domains. These agents, proficient in human-like text comprehension and generation, have the potential…

人工智能 · 计算机科学 2024-04-10 Saikat Barua

Large Language Models (LLMs) are increasingly being used to automate programming tasks. Yet, LLMs' capabilities in reasoning about program semantics are still inadequately studied, leaving significant potential for further exploration. This…

编程语言 · 计算机科学 2025-05-30 Thanh Le-Cong , Bach Le , Toby Murray

Chemical reasoning usually involves complex, multi-step processes that demand precise calculations, where even minor errors can lead to cascading failures. Furthermore, large language models (LLMs) encounter difficulties handling…

Large Language Models (LLMs) excel in generating personalized content and facilitating interactive dialogues, showcasing their remarkable aptitude for a myriad of applications. However, their capabilities in reasoning and providing…

计算与语言 · 计算机科学 2024-02-16 Min Zhang , Sato Takumi , Jack Zhang , Jun Wang

Reasoning is a fundamental aspect of human intelligence that plays a crucial role in activities such as problem solving, decision making, and critical thinking. In recent years, large language models (LLMs) have made significant progress in…

计算与语言 · 计算机科学 2023-05-29 Jie Huang , Kevin Chen-Chuan Chang

Large Language Models (LLMs) have the potential to accelerate small molecule drug design due to their ability to reason about information from diverse sources and formats. However, their practical utility remains unclear due to the lack of…

Large Language Models (LLMs) have quickly risen to prominence due to their ability to perform at or close to the state-of-the-art in a variety of fields while handling natural language. An important field of research is the application of…

密码学与安全 · 计算机科学 2024-02-28 Gabriel de Jesus Coelho da Silva , Carlos Becker Westphall

Large Language Models (LLMs) have the potential to semi-automate some process mining (PM) analyses. While commercial models are already adequate for many analytics tasks, the competitive level of open-source LLMs in PM tasks is unknown. In…

计算与语言 · 计算机科学 2024-07-19 Alessandro Berti , Humam Kourani , Wil M. P. van der Aalst

Large Language Models (LLMs) have come closest among all models to date to mastering human language, yet opinions about their linguistic and cognitive capabilities remain split. Here, we evaluate LLMs using a distinction between formal…

计算与语言 · 计算机科学 2024-04-14 Kyle Mahowald , Anna A. Ivanova , Idan A. Blank , Nancy Kanwisher , Joshua B. Tenenbaum , Evelina Fedorenko

We present a new approach for benchmarking Large Language Model (LLM) capabilities on research-level mathematics. Existing benchmarks largely rely on static, hand-curated sets of contest or textbook-style problems as proxies for…

人工智能 · 计算机科学 2026-03-02 Antoine Peyronnet , Fabian Gloeckle , Amaury Hayat

Large language models (LLMs) have shown potential in assisting scientific research, yet their ability to discover high-quality research hypotheses remains unexamined due to the lack of a dedicated benchmark. To address this gap, we…

计算与语言 · 计算机科学 2026-04-21 Yujie Liu , Zonglin Yang , Tong Xie , Jinjie Ni , Ben Gao , Yuqiang Li , Shixiang Tang , Wanli Ouyang , Erik Cambria , Dongzhan Zhou

The planning ability of Large Language Models (LLMs) has garnered increasing attention in recent years due to their remarkable capacity for multi-step reasoning and their ability to generalize across a wide range of domains. While some…

人工智能 · 计算机科学 2025-02-19 Mohamed Aghzal , Erion Plaku , Gregory J. Stein , Ziyu Yao

While large language models (LLMs) with Chain-of-Thought (CoT) reasoning excel in mathematics and coding, their potential for systematic reasoning in chemistry, a domain demanding rigorous structural analysis for real-world tasks like drug…

人工智能 · 计算机科学 2026-01-08 Hao Li , He Cao , Bin Feng , Yanjun Shao , Xiangru Tang , Zhiyuan Yan , Li Yuan , Yonghong Tian , Yu Li