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Expert feedback lays the foundation of rigorous research. However, the rapid growth of scholarly production and intricate knowledge specialization challenge the conventional scientific feedback mechanisms. High-quality peer reviews are…

Large Language Models (LLMs) have demonstrated substantial progress in biomedical and clinical applications, motivating rigorous evaluation of their ability to answer nuanced, evidence-based questions. We curate a multi-source benchmark…

计算与语言 · 计算机科学 2025-09-16 Can Wang , Yiqun Chen

Despite growing interest in using large language models (LLMs) in healthcare, current explorations do not assess the real-world utility and safety of LLMs in clinical settings. Our objective was to determine whether two LLMs can serve…

Large language models (LLMs) have shown great potential for the automatic generation of feedback in a wide range of computing contexts. However, concerns have been voiced around the privacy and ethical implications of sending student work…

计算与语言 · 计算机科学 2024-05-09 Charles Koutcheme , Nicola Dainese , Sami Sarsa , Arto Hellas , Juho Leinonen , Paul Denny

Lab results are often confusing and hard to understand. Large language models (LLMs) such as ChatGPT have opened a promising avenue for patients to get their questions answered. We aim to assess the feasibility of using LLMs to generate…

计算与语言 · 计算机科学 2024-04-23 Zhe He , Balu Bhasuran , Qiao Jin , Shubo Tian , Karim Hanna , Cindy Shavor , Lisbeth Garcia Arguello , Patrick Murray , Zhiyong Lu

The field of healthcare has increasingly turned its focus towards Large Language Models (LLMs) due to their remarkable performance. However, their performance in actual clinical applications has been underexplored. Traditional evaluations…

Large Language Models (LLMs) have emerged as transformative tools in the healthcare sector, demonstrating remarkable capabilities in natural language understanding and generation. However, their proficiency in numerical reasoning,…

人工智能 · 计算机科学 2025-01-27 Arjun R. Malghan

Large language models (LLMs) demonstrate impressive capabilities in mathematical reasoning. However, despite these achievements, current evaluations are mostly limited to specific mathematical topics, and it remains unclear whether LLMs are…

计算与语言 · 计算机科学 2025-04-01 Arash Gholami Davoodi , Seyed Pouyan Mousavi Davoudi , Pouya Pezeshkpour

Large language models (LLMs) power deep research agents that synthesize information from hundreds of web sources into cited reports, yet these citations cannot be reliably verified. Current approaches either trust models to self-cite…

计算与语言 · 计算机科学 2026-05-08 Hailey Onweller , Elias Lumer , Austin Huber , Pia Ramchandani , Vamse Kumar Subbiah , Corey Feld

Large language models (LLMs) have demonstrated powerful text generation capabilities, bringing unprecedented innovation to the healthcare field. While LLMs hold immense promise for applications in healthcare, applying them to real clinical…

计算与语言 · 计算机科学 2023-10-16 Rui Yang , Edison Marrese-Taylor , Yuhe Ke , Lechao Cheng , Qingyu Chen , Irene Li

The proliferation of Large Language Models (LLMs) in high-stakes applications such as medical (self-)diagnosis and preliminary triage raises significant ethical and practical concerns about the effectiveness, appropriateness, and possible…

In recent years, Large Language Models (LLMs) have gained immense attention due to their notable emergent capabilities, surpassing those seen in earlier language models. A particularly intriguing application of LLMs is their role as…

计算与语言 · 计算机科学 2023-11-02 Xue-Yong Fu , Md Tahmid Rahman Laskar , Cheng Chen , Shashi Bhushan TN

Large Language Models (LLMs) have emerged as powerful support tools across various natural language tasks and a range of application domains. Recent studies focus on exploring their capabilities for data annotation. This paper provides a…

计算与语言 · 计算机科学 2025-07-01 Maja Pavlovic , Massimo Poesio

The surge in scientific submissions has placed increasing strain on the traditional peer-review process, prompting the exploration of large language models (LLMs) for automated review generation. While LLMs demonstrate competence in…

计算与语言 · 计算机科学 2025-09-25 Ruochi Li , Haoxuan Zhang , Edward Gehringer , Ting Xiao , Junhua Ding , Haihua Chen

Automated fact-checking, using machine learning to verify claims, has grown vital as misinformation spreads beyond human fact-checking capacity. Large Language Models (LLMs) like GPT-4 are increasingly trusted to write academic papers,…

计算与语言 · 计算机科学 2024-02-08 Dorian Quelle , Alexandre Bovet

One of the central challenges for instructors is offering meaningful individual feedback, especially in large courses. Faced with limited time and resources, educators are often forced to rely on generalized feedback, even when more…

其他统计学 · 统计学 2025-11-07 Markus Herklotz , Niklas Ippisch , Anna-Carolina Haensch

Large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation across various domains, including medicine. We present a comprehensive evaluation of GPT-4, a state-of-the-art LLM, on…

计算与语言 · 计算机科学 2023-04-13 Harsha Nori , Nicholas King , Scott Mayer McKinney , Dean Carignan , Eric Horvitz

Although large language models (LLMs) often produce impressive outputs, it remains unclear how they perform in real-world scenarios requiring strong reasoning skills and expert domain knowledge. We set out to investigate whether close- and…

计算与语言 · 计算机科学 2023-12-27 Valentin Liévin , Christoffer Egeberg Hother , Andreas Geert Motzfeldt , Ole Winther

Large Language Models (LLMs) excel in various Natural Language Processing (NLP) tasks, yet their evaluation, particularly in languages beyond the top $20$, remains inadequate due to existing benchmarks and metrics limitations. Employing…

Given the rapid ascent of large language models (LLMs), we study the question: (How) can large language models help in reviewing of scientific papers or proposals? We first conduct some pilot studies where we find that (i) GPT-4 outperforms…

计算与语言 · 计算机科学 2023-06-02 Ryan Liu , Nihar B. Shah
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