English
Related papers

Related papers: Constituency Parsing using LLMs

200 papers

Multimodal large language models (MLLMs) have shown remarkable performance for cross-modal understanding and generation, yet still suffer from severe inference costs. Recently, abundant works have been proposed to solve this problem with…

Computation and Language · Computer Science 2025-05-30 Zichen Wen , Yifeng Gao , Weijia Li , Conghui He , Linfeng Zhang

Continual Learning (CL) is essential for enabling self-evolving large language models (LLMs) to adapt and remain effective amid rapid knowledge growth. Yet, despite its importance, little attention has been given to establishing statistical…

Machine Learning · Computer Science 2025-10-29 Xiaofan Zhou , Lu Cheng

Large Language Models (LLMs) exhibit remarkable fluency and competence across various natural language tasks. However, recent research has highlighted their sensitivity to variations in input prompts. To deploy LLMs in a safe and reliable…

Computation and Language · Computer Science 2025-04-30 Harsh Raj , Vipul Gupta , Domenic Rosati , Subhabrata Majumdar

We develop new conformal inference methods for obtaining validity guarantees on the output of large language models (LLMs). Prior work in conformal language modeling identifies a subset of the text that satisfies a high-probability…

Machine Learning · Statistics 2024-11-01 John J. Cherian , Isaac Gibbs , Emmanuel J. Candès

Constituent and dependency representation for syntactic structure share a lot of linguistic and computational characteristics, this paper thus makes the first attempt by introducing a new model that is capable of parsing constituent and…

Computation and Language · Computer Science 2019-09-27 Junru Zhou , Shuailiang Zhang , Hai Zhao

Recent Large Language Models (LLMs) have demonstrated remarkable capabilities in generating text that closely resembles human writing across wide range of styles and genres. However, such capabilities are prone to potential abuse, such as…

Computation and Language · Computer Science 2023-11-09 Harika Abburi , Kalyani Roy , Michael Suesserman , Nirmala Pudota , Balaji Veeramani , Edward Bowen , Sanmitra Bhattacharya

Large language models (LLMs) have achieved state-of-the-art performance on a series of natural language understanding tasks. However, these LLMs might rely on dataset bias and artifacts as shortcuts for prediction. This has significantly…

Computation and Language · Computer Science 2023-05-09 Mengnan Du , Fengxiang He , Na Zou , Dacheng Tao , Xia Hu

Reliably counting and generating sequences of items remain a significant challenge for neural networks, including Large Language Models (LLMs). Indeed, although this capability is readily handled by rule-based symbolic systems based on…

Artificial Intelligence · Computer Science 2026-01-14 Kuinan Hou , Marco Zorzi , Alberto Testolin

In the rapidly evolving domain of Natural Language Generation (NLG) evaluation, introducing Large Language Models (LLMs) has opened new avenues for assessing generated content quality, e.g., coherence, creativity, and context relevance.…

Computation and Language · Computer Science 2024-06-13 Zhen Li , Xiaohan Xu , Tao Shen , Can Xu , Jia-Chen Gu , Yuxuan Lai , Chongyang Tao , Shuai Ma

Clinical trials are a critical process in the medical field for introducing new treatments and innovations. However, cohort selection for clinical trials is a time-consuming process that often requires manual review of patient text records…

Computation and Language · Computer Science 2025-01-22 Chi-en Amy Tai , Xavier Tannier

Consistency, which refers to the capability of generating the same predictions for semantically similar contexts, is a highly desirable property for a sound language understanding model. Although recent pretrained language models (PLMs)…

Computation and Language · Computer Science 2021-08-17 Myeongjun Jang , Deuk Sin Kwon , Thomas Lukasiewicz

Large language models (LLMs) are becoming increasingly better at a wide range of Natural Language Processing tasks (NLP), such as text generation and understanding. Recently, these models have extended their capabilities to coding tasks,…

Machine Learning · Computer Science 2024-10-23 Nishat Raihan , Mohammed Latif Siddiq , Joanna C. S. Santos , Marcos Zampieri

In the rapidly evolving landscape of genomics, deep learning has emerged as a useful tool for tackling complex computational challenges. This review focuses on the transformative role of Large Language Models (LLMs), which are mostly based…

Large Language Models (LLMs) are reshaping unsupervised learning by offering an unprecedented ability to perform text clustering based on their deep semantic understanding. However, their direct application is fundamentally limited by a…

Computation and Language · Computer Science 2026-04-08 Yuanjie Zhu , Liangwei Yang , Ke Xu , Weizhi Zhang , Zihe Song , Jindong Wang , Philip S. Yu

Large Language Models (LLMs) have shown significant potential for ontology engineering. However, it is still unclear to what extent they are applicable to the task of domain-specific ontology generation. In this study, we explore the…

Large Language Models (LLMs) represent a class of deep learning models adept at understanding natural language and generating coherent responses to various prompts or queries. These models far exceed the complexity of conventional neural…

Machine Learning · Computer Science 2024-12-05 Minghao Shao , Abdul Basit , Ramesh Karri , Muhammad Shafique

Mathematical optimization is a powerful tool for structured decision-making across domains such as resource allocation and planning. Formulating optimization models faithful to reality, though, remains a significant bottleneck as it…

Artificial Intelligence · Computer Science 2026-05-27 Eleni Straitouri , Cheol Woo Kim , Milind Tambe

Ontologies provide formal representation of knowledge shared within Semantic Web applications. Ontology learning involves the construction of ontologies from a given corpus. In the past years, ontology learning has traversed through shallow…

Information Retrieval · Computer Science 2024-06-18 Rick Du , Huilong An , Keyu Wang , Weidong Liu

Despite large language models' (LLMs) recent advancements, their bias and hallucination issues persist, and their ability to offer consistent preferential rankings remains underexplored. This study investigates the capacity of LLMs to…

Computation and Language · Computer Science 2024-10-14 Xiutian Zhao , Ke Wang , Wei Peng

Large Language Models (LLMs) have demonstrated their remarkable capabilities in numerous fields. This survey focuses on how LLMs empower users, regardless of their technical background, to use human languages to automatically generate…

Software Engineering · Computer Science 2025-04-03 Nam Huynh , Beiyu Lin