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相关论文: Large Language Models are Not Yet Human-Level Eval…

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Large Language Models (LLMs) such as ChatGPT have shown remarkable abilities in producing human-like text. However, it is unclear how accurately these models internalize concepts that shape human thought and behavior. Here, we developed a…

机器学习 · 计算机科学 2025-07-01 Hiro Taiyo Hamada , Ippei Fujisawa , Genji Kawakita , Yuki Yamada

When asked, large language models (LLMs) like ChatGPT claim that they can assist with relevance judgments but it is not clear whether automated judgments can reliably be used in evaluations of retrieval systems. In this perspectives paper,…

Automatic summarization of legal case judgements has traditionally been attempted by using extractive summarization methods. However, in recent years, abstractive summarization models are gaining popularity since they can generate more…

计算与语言 · 计算机科学 2023-06-16 Aniket Deroy , Kripabandhu Ghosh , Saptarshi Ghosh

Large language models (LLMs) like ChatGPT are increasingly used in academic writing, yet issues such as incorrect or fabricated references raise ethical concerns. Moreover, current content quality evaluations often rely on subjective human…

计算与语言 · 计算机科学 2025-09-15 Jing Ren , Weiqi Wang

Large Language Models (LLMs) have been reported to outperform existing automatic evaluation metrics in some tasks, such as text summarization and machine translation. However, there has been a lack of research on LLMs as evaluators in…

计算与语言 · 计算机科学 2024-05-28 Masamune Kobayashi , Masato Mita , Mamoru Komachi

Text summarization has been a crucial problem in natural language processing (NLP) for several decades. It aims to condense lengthy documents into shorter versions while retaining the most critical information. Various methods have been…

计算与语言 · 计算机科学 2023-02-17 Xianjun Yang , Yan Li , Xinlu Zhang , Haifeng Chen , Wei Cheng

While large language models (LLMs) can already achieve strong performance on standard generic summarization benchmarks, their performance on more complex summarization task settings is less studied. Therefore, we benchmark LLMs on…

Evaluating text summarization has been a challenging task in natural language processing (NLP). Automatic metrics which heavily rely on reference summaries are not suitable in many situations, while human evaluation is time-consuming and…

计算与语言 · 计算机科学 2024-07-02 Huyen Nguyen , Haihua Chen , Lavanya Pobbathi , Junhua Ding

Large Language Models (LLMs) have the potential to be used to support research evaluation and have a moderate capability to estimate the research quality of a journal article from its title and abstract. This paper assesses whether there…

数字图书馆 · 计算机科学 2026-03-17 Kayvan Kousha , Mike Thelwall

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

Objective: This study aims to summarize the usage of Large Language Models (LLMs) in the process of creating a scientific review. We look at the range of stages in a review that can be automated and assess the current state-of-the-art…

数字图书馆 · 计算机科学 2025-05-16 Dmitry Scherbakov , Nina Hubig , Vinita Jansari , Alexander Bakumenko , Leslie A. Lenert

Following the widespread adoption of ChatGPT in early 2023, numerous studies reported that large language models (LLMs) can match or even surpass human performance in creative tasks. However, it remains unclear whether LLMs have become more…

计算与语言 · 计算机科学 2025-04-18 Jennifer Haase , Paul H. P. Hanel , Sebastian Pokutta

Large language models (LLMs) are currently at the forefront of intertwining AI systems with human communication and everyday life. Therefore, it is of great importance to evaluate their emerging abilities. In this study, we show that LLMs…

计算与语言 · 计算机科学 2023-10-10 Thilo Hagendorff , Sarah Fabi , Michal Kosinski

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

Using large language models (LLMs) to evaluate text quality has recently gained popularity. Some prior works explore the idea of using LLMs for evaluation, while they differ in some details of the evaluation process. In this paper, we…

计算与语言 · 计算机科学 2023-10-10 Cheng-Han Chiang , Hung-yi Lee

In this paper, we introduce ChatCite, a novel method leveraging large language models (LLMs) for generating comparative literature summaries. The ability to summarize research papers with a focus on key comparisons between studies is an…

计算与语言 · 计算机科学 2024-12-04 Fernando Gabriela Garcia , Spencer Burns , Harrison Fuller

Large Language Models (LLMs) have revolutionized various Natural Language Generation (NLG) tasks, including Argument Summarization (ArgSum), a key subfield of Argument Mining. This paper investigates the integration of state-of-the-art LLMs…

Understanding the limits of language is a prerequisite for Large Language Models (LLMs) to act as theories of natural language. LLM performance in some language tasks presents both quantitative and qualitative differences from that of…

计算与语言 · 计算机科学 2025-06-30 Vittoria Dentella , Fritz Guenther , Evelina Leivada

Large Language Models (LLMs) are increasingly explored for educational tasks such as grading, yet their alignment with human evaluation in real classrooms remains underexamined. In this study, we investigate the feasibility of using an LLM…

计算与语言 · 计算机科学 2025-11-19 Grace Byun , Swati Rajwal , Jinho D. Choi

With the release of ChatGPT and other large language models (LLMs) the discussion about the intelligence, possibilities, and risks, of current and future models have seen large attention. This discussion included much debated scenarios…

人工智能 · 计算机科学 2024-07-31 Nils Körber , Silvan Wehrli , Christopher Irrgang