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About 10,000 papers for “Language model”

language model

Theologos Athanaselis, Stelios Bakamidis, Ioannis Dologlou · 2013

Cited by 0

STATISTICAL LANGUAGE MODEL

Thomas L. Packer, Using A Featurized Syntactic, Thomas L. Packer et al. · 2006

This Thesis is brought to you for free and open access by BYU ScholarsArchive. It has been accepted for inclusion in All Theses and Dissertations by an authorized administrator of BYU ScholarsArchive. For more information, please contact scholarsarchive@byu.edu.

Cited by 0

Masked language model.

Yuhao Jia (18806156), Zhicheng Yu (12277445), Zhen Hong (295111) · Figshare · 2024

<div><p>Binary code similarity detection plays a crucial role in various applications within binary security, including vulnerability detection, malicious software analysis, etc. However, existing methods suffer from limited differentiation in binary embedding representations across different compilation environments, lacking dynamic high-level semantics. Moreover, current approaches often neglect multi-level semantic feature extraction, thereby failing to acquire precise semantic information about the binary code. To address these limitations, this paper introduces a novel detection solution called BinBcla. This method employs an enhanced pre-training model to generate instruction embeddings with dynamic semantics for binary functions. Subsequently, multi-feature fusion technique is utilized to extract local semantic information and long-distance global features from the code, respectively, employing self-attention to comprehend the structure information of the code. Finally, an improved cosine similarity method is employed to learn relationships among all elements of the distance vectors, thereby enhancing the model’s robustness to new sample functions. Experiments are conducted across different architectures, compilers, and optimization levels. The results indicate that BinBcla achieves higher accuracy, precision and F1 score compared to existing methods.</p></div>

Cited by 0Open access

Language Model Resources

Wayne Xin Zhao, Kun Zhou, Junyi Li et al. · Artificial intelligence: foundations, theory, and algorithms/Artificial intelligence: Foundations, theory, and algorithms · 2025

Cited by 0

Language Model Programming

Beurer-Kellner, Luca · Repository for Publications and Research Data (ETH Zurich) · 2025

Cited by 0Open access

Random Language Model

Eric DeGiuli · Physical Review Letters · 2019

Many complex generative systems use languages to create structured objects. We consider a model of random languages, defined by weighted context-free grammars. As the distribution of grammar weights broadens, a transition is found from a random phase, in which sentences are indistinguishable from noise, to an organized phase in which nontrivial information is carried. This marks the emergence of deep structure in the language, and can be understood by a competition between energy and entropy.

Cited by 30Open access

Language Model Training

Picone, Rico · Figshare · 2023

Illustration of how a language model is trained.

Cited by 0Open access

Model Driven Language

Octavian Patrascoiu · 2005

Modeling is a most important exercise in software engineering and development and one of the current practices is object-oriented (OO) modeling. The Object Management Group (OMG) has defined a standard object-oriented modeling language – the Unified Modeling Language (UML). The OMG is not only interested in modeling languages; its primary aim is to enable easy integration of software systems and components using vendor-neutral technologies. This thesis investigates the possibilities for designing and implementing modeling frameworks and transformation languages that operate on models and to explore the validation of source and target models. Specifically, we will focus on OO models used in OMG’s Model Driven Architecture (MDA), which can be expressed in terms of UML terms (e.g. classes and associations). The thesis presents the Kent Modeling Framework (KMF), a modeling framework that we developed, and describes how this framework can be used to generate a modeling tool from a model. It then proceeds to describe the customization of the generated code, in particular the definition of methods that allows a rapid and repeatable instantiation of a model. Model

Cited by 0

Language Model Training

Picone, Rico · Figshare · 2023

Illustration of how a language model is trained.

Cited by 0Open access

Language agnostic model

Heena Khan, Joshua L. Phillips · 2021

Social media platforms can struggle to enforce rules preventing online abuse and hate speech due to the large amount of content that must be manually reviewed. Machine learning approaches have been proposed in the literature as a way to automate much of these labors, but social content in multiple languages further complicates this issue. Past work has focused on first building word embeddings in the target language which limits the application of such embeddings to other languages. We use the Google Neural Machine Translator (NMT) to identify and translate Non-English text to English to make the system language agnostic. We can therefore use already available pre-trained word embeddings, instead of training our models and word embeddings in different languages. We have experimented with different word-embedding and classifier pairs as we aimed to assess whether translated English data gives us accuracy comparable to an untranslated English dataset. Our best performing model, SVM with TF-IDF, gave us a 10-fold accuracy of 95.56 percent followed by the BERT model with a 10-fold accuracy of 94.66 percent on the translated data. This accuracy is close to the accuracy of the untranslated English dataset and far better than the accuracy of the untranslated Hindi dataset.

Cited by 6

Truveta Language Model

Inc. Truveta · Zenodo (CERN European Organization for Nuclear Research) · 2026

Please refer to https://www.truveta.com/blog/news/truveta-language-model/ for the most recent version of this whitepaper.

Cited by 0Open access

Truveta Language Model

Inc. Truveta · Zenodo (CERN European Organization for Nuclear Research) · 2026

Please refer to https://www.truveta.com/blog/news/truveta-language-model/ for the most recent version of this whitepaper.

Cited by 0Open access

The language model

Roth, H-J., Duarte, J., Broeder, P. et al. · Data Archiving and Networked Services (DANS) · 2010

Cited by 0

Item-Language Model: Improving Large Language Model for Recommendation via Item-Language Representation Learning

Association for Computational Linguistics 2025, Vikram Aggarwal, Fuli Feng et al. · Underline Science Inc. · 2025

Large Language Models (LLMs) have recently made significant advancements in tackling complex tasks, such as retrieving hard-to-find information and solving intricate problems. Consequently, various approaches have been proposed to integrate LLMs into recommender systems, primarily by embedding them within existing architectures or training them on the recommendation data. However, most existing methods fail to effectively incorporate user-item interaction signals into pretrained LLMs due to the modality gap between interaction data and the LLM’s internal knowledge. To address this challenge, we propose the Item-Language Model (ILM) to enhance LLMs for recommendation. ILM consists of two main components: An item-language representation learning module, where an ILM encoder is pretrained to generate text-aligned item representations. And an item-language co-training module, where the ILM encoder is integrated into a pretrained LLM for the recommendation tasks. Extensive experiments demonstrate the superior performance of our approach over several state-of-the-art methods, validating the importance of text-aligned item representations in bridging this modality gap. Our ablation studies further reveal the effectiveness of our model design for integrating the interaction knowledge into LLMs for recommendation tasks. Our code is available at: https://anonymous.4open.science/r/ILM-7AD4/.

Cited by 0Open access

Item-Language Model: Improving Large Language Model for Recommendation via Item-Language Representation Learning

Li Yang, Anushya Subbiah, Hardik Patel et al. · 2025

Li Yang, Anushya Subbiah, Hardik Patel, Judith Yue Li, Yanwei Song, Reza Mirghaderi, Vikram Aggarwal, Fuli Feng, Zenglin Xu, Dongfang Liu, Qifan Wang. Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics. 2025.

Cited by 0Open access

Language model adaptation for spoken language system

Ros Potamianos, Shrikanth Narayanan · 1998

In a human-machine interaction (dialog) the statistical lan-guage variations are large among different stages of the dialog and across different speakers. Moreover, spoken dialog systems require extensive training data for training adaptive language models. In this paper we address the problem of open-vocabulary language models allowing the user for any possible response at each stage of the dia-log. We propose a novel off-line adaptation of stochastic language models effective for their generalization (open-vocabulary) and selective (dialog context) properties. We outline the integration of the finite state dialog model and the language model adaptation algorithm. The performance of the speech recognition and understanding language mod-els are evaluated with the Carmen Sandiego multimodal computer game. The new language models give an overall understanding error rate reduction of 44 % over the base-line system. 2.

Cited by 0