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29 papers · ranked by Valyu relevance

01
AarXiv16 May 2025
LDIR: Low-Dimensional Dense and Interpretable Text Embeddings with Relative Representations
Yile Wang, Zhanyu Shen, Hui Huang
Semantic text representation is a fundamental task in the field of natural language processing. Existing text embedding (e.g., Sim-CSE and LLM2Vec) have demonstrated excellent performance, but the values of each dimension are difficult to trace and interpret. Bag-of-words, as classic sparse interpretable embeddings…
83relevance
02
PPubMed14 Apr 2026
Optimizing document retrieval using massive text embeddings and LLM prompt engineering
Goran Mitrov, Boris Stanoev, Vladimir Trajkovik, Biljana Risteska Stojkoska + 4 more
Background The rapid expansion of digital data poses a unique challenge for retrieving relevant and insightful information efficiently. In particular, the increasing volume of scientific publications has made literature reviews time-consuming. The emergence of large language models (LLMs) offers new opportunities to…
81relevance
03
AarXiv2023
Text Embeddings Reveal (Almost) As Much As Text
John X. Morris, Volodymyr Kuleshov, Vitaly Shmatikov, Alexander M. Rush
'Alexander M. Rush'] How much private information do text embeddings reveal about the original text? We investigate the problem of embedding inversion, reconstructing the full text represented in dense text embeddings. We frame the problem as controlled generation: generating text that, when reembedded, is close to a…
81relevance
04
PPubMed23 Jul 2022
Using word embeddings to investigate cultural biases Biases in word embeddings DURRHEIM et al.
Kevin Durrheim, Maria Schuld, Martin Mafunda, Sindisiwe Mazibuko
Word embeddings provide quantitative representations of word semantics and the associations between word meanings in text data, including in large repositories in media and social media archives. This article introduces social psychologists to word embedding research via a consideration of bias analysis, a topic of…
80relevance
05
AarXiv2022
Word Tour: One-dimensional Word Embeddings via the Traveling Salesman Problem
Ryoma Sato
Word embeddings are one of the most fundamental technologies used in natural language processing. Existing word embeddings are high-dimensional and consume considerable computational resources. In this study, we propose WORDTOUR, unsupervised onedimensional word embeddings. To achieve the challenging goal, we propose a…
78relevance
06
AarXiv10 Jul 2025
Rethinking the Privacy of Text Embeddings: A Reproducibility Study of "Text Embeddings Reveal (Almost) As Much As Text"
Dominykas Seputis, Yongkang Li, Karsten Langerak, Serghei Mihailov
Text embeddings are fundamental to many natural language processing (NLP) tasks, extensively applied in domains such as recommendation systems and information retrieval (IR). Traditionally, transmitting embeddings instead of raw text has been seen as privacy-preserving. However, recent methods such as Vec2Text…
78relevance
07
AarXiv6 Nov 2024
From Word Vectors to Multimodal Embeddings: Techniques, Applications, and Future Directions For Large Language Models
Charles Zhang, Benji Peng, Xintian Sun, Qian Niu + 11 more
and Future Directions For Large Language Models Authors: ['Charles Zhang' 'Benji Peng' 'Xintian Sun' 'Qian Niu' 'Junyu Liu' 'Keyu Chen' 'Ming Li' 'Peiyong Feng' 'Ziqian Bi' 'Ming Liu' 'Yichao Zhang' 'Fei Cheng' 'Caitlyn Heqi Yin' 'Lingzhi Yan' 'Tianyang Wang'] Abstract—Word embeddings and language models have…
77relevance
08
AarXiv10 Oct 2024
Semantic Token Reweighting for Interpretable and Controllable Text Embeddings in CLIP
Eunji Kim, Kyuhong Shim, Simyung Chang, Sungroh Yoon
Embeddings in CLIP Authors: ['Eunji Kim' 'Kyuhong Shim' 'Simyung Chang' 'Sungroh Yoon'] A text encoder within Vision-Language Models (VLMs) like CLIP plays a crucial role in translating textual input into an embedding space shared with images, thereby facilitating the interpretative analysis of vision tasks through…
77relevance
09
AarXiv16 Nov 2025
Evaluating Embedding Generalization: How LLMs, LoRA, and SLERP Shape Representational Geometry
Kabane, Siyaxolisa
We investigate the generalization properties of dense text embeddings when the embedding backbone is a large language model (LLM) versus when it is a non-LLM encoder, and we study the extent to which spherical linear interpolation (SLERP) model-merging mitigates over-specialization introduced by task-specific…
75relevance
10
PPubMed22 Jan 2026
Improved Arabic query expansion using word embedding
Yaser A. Al-Lahham, Sattam Almatarneh, Kaznah Alshammari, Mutasem Al-Smadi
Word embedding enhances pseudo-relevance feedback query expansion (PRFQE), but training word embedding models takes a long time and is applied to large datasets. Moreover, the Arabic language, which has rich morphology, dialectal variations, and a lack of high-quality linguistic resources, training embedding models…
75relevance
11
AarXiv6 Jun 2024
TexIm FAST: Text-to-Image Representation for Semantic Similarity Evaluation using Transformers
Wazib Ansar, Saptarsi Goswami, Amlan Chakrabarti
Evaluation using Transformers Authors: ['Wazib Ansar' 'Saptarsi Goswami' 'Amlan Chakrabarti'] One of the principal objectives of Natural Language Processing (NLP) is to generate meaningful representations from text. Improving the informativeness of the representations has led to a tremendous rise in the dimensionality…
74relevance
12
PPubMed19 Dec 2023
Language with vision: A study on grounded word and sentence embeddings
Hassan Shahmohammadi, Maria Heitmeier, Elnaz Shafaei-Bajestan, Hendrik P. A. Lensch + 1 more
'Hendrik P. A. Lensch' 'R. Harald Baayen'] Grounding language in vision is an active field of research seeking to construct cognitively plausible word and sentence representations by incorporating perceptual knowledge from vision into text-based representations. Despite many attempts at language grounding, achieving an…
73relevance
13
CchemRxiv4 Sep 2024
A Survey of Artificial Intelligence Methods for Clinical Trial Outcome Prediction
Long Qian, Xin Lu, Parvez Haris, Jianyong Zhu + 2 more
Clinical trials are crucial for drug development, but they require significant time and financial resources. Additionally, uncertainties may arise during these trials concerning their results due to concerns surrounding effectiveness, safety, or the enrollment of participants. If robust AI (artificial intelligence)…
72relevance
14
BbioRxiv4 Jul 2024
Replacing non-biomedical concepts improves embedding of biomedical concepts
Enock Niyonkuru, Mauricio Soto Gomez, Elena Casiraghi, Stephan Antogiovanni + 4 more
Concept embeddings are low-dimensional vector representations of concepts such as MeSH:D009203 (Myocardial Infarction), whose similarity in the embedded vector space reflects their semantic similarity. Here, we test the hypothesis that non-biomedical concept synonym replacement can improve the quality of biomedical…
72relevance
15
PPubMed19 May 2025
Fast2Vec, a modified model of FastText that enhances semantic analysis in topic evolution
Ayu Pertiwi, Azhari Azhari, Sri Mulyana, Bilal Alatas
Background Topic modeling approaches, such as latent Dirichlet allocation (LDA) and its successor, the dynamic topic model (DTM), are widely used to identify specific topics by extracting words with similar frequencies from documents. However, these topics often require manual interpretation, which poses challenges in…
71relevance
16
PPubMed15 Feb 2023
Biomedical Text Classification Using Augmented Word Representation Based on Distributional and Relational Contexts
Md. Aslam Parwez, Mohd. Fazil, Muhammad Arif, Md Tabrez Nafis + 1 more
'Md. Rabiul Auwul'] Due to the increasing use of information technologies by biomedical experts, researchers, public health agencies, and healthcare professionals, a large number of scientific literatures, clinical notes, and other structured and unstructured text resources are rapidly increasing and being stored in…
70relevance
17
BbioRxiv27 Aug 2025
Adding layers of information to scRNA-seq data using pre-trained language models
Sonia Maria Krißmer, Jonatan Menger, Johan Rollin, Tanja Vogel + 2 more
Pre-trained language models promise to enrich analyses of single-cell data with additional layers of information leveraging large text corpora. Yet, it is still unclear how to achieve optimal alignment with the primary quantitative single-cell data. To address this, we construct text-based training datasets from both…
67relevance
18
PPubMed29 Mar 2022
Visualization of medical concepts represented using word embeddings: a scoping review
Naima Oubenali, Sabrina Messaoud, Alexandre Filiot, Antoine Lamer + 1 more
'Paul Andrey'] Background Analyzing the unstructured textual data contained in electronic health records (EHRs) has always been a challenging task. Word embedding methods have become an essential foundation for neural network-based approaches in natural language processing (NLP), to learn dense and low-dimensional word…
67relevance
19
BbioRxiv19 Mar 2025
Domain-specific embeddings uncover latent genetics knowledge
S. S. Ho, R. E. Mills
The inundating rate of scientific publishing means every researcher will miss new discoveries from overwhelming saturation. To address this limitation, we employ natural language processing to overcome human limitations in reading, curation, and knowledge synthesis, with domain-specific applications to genetics and…
67relevance
20
CchemRxiv10 Mar 2023
Structure feature vectors derived from Robocrystallographer text descriptions of crystal structures using word embeddings
Hasan M. Sayeed, Sterling G. Baird, Taylor D. Sparks
Capturing structure-property relationships of materials for property prediction using machine learning requires the representation or featurization of the structural aspects of materials at different levels, including atomic, crystal, and microscales. While crystal structure-based modeling techniques are effective for…
65relevance
21
BbioRxiv26 Aug 2024
Benchmarking text-integrated protein language model embeddings and embedding fusion on diverse downstream tasks
Young Su Ko, Jonathan Parkinson, Wei Wang
Protein language models (pLMs) have traditionally been trained in an unsupervised manner using large protein sequence databases with an autoregressive or masked-language modeling training paradigm. Recent methods have attempted to enhance pLMs by integrating additional information, in the form of text, which are…
64relevance
22
BbioRxiv7 Jun 2026
Polynomial Trajectory Compression for Protein Language Model Embeddings
Harshita Sahni, Xin Chen, Trilce Estrada
Protein language models (PLMs) generate rich, layer-wise embeddings that capture diverse biological information but are expensive in terms of storage and computation at scale. In this work, we propose a compact surrogate representation for PLM embeddings across transformer layers using low-dimensional PCA projections…
63relevance
23
CchemRxiv27 Mar 2024
Graph-Text Contrastive Learning of Inorganic Crystal Structure toward a Foundation Model of Inorganic Materials
Keisuke Ozawa, Teppei Suzuki, Shunsuke Tonogai, Tomoya Itakura
Developing foundation models for materials science has attracted attention. However, there is a lack of studies on inorganic materials due to the difficulty in the comprehensive representation of geometric concepts composing crystals: local atomic environments, their connections, and the global symmetries. We present a…
61relevance
24
BbioRxiv2 May 2024
ntEmbd: Deep learning embedding for nucleotide sequences
Saber Hafezqorani, Ka Ming Nip, Inanc Birol
Enabled by the explosion of data and substantial increase in computational power, deep learning has transformed fields such as computer vision and natural language processing (NLP) and it has become a successful method to be applied to many transcriptomic analysis tasks. A core advantage of deep learning is its…
61relevance
25
CchemRxiv1 Apr 2024
Graph-Text Contrastive Learning of Inorganic Crystal Structure toward a Foundation Model of Inorganic Materials
Keisuke Ozawa, Teppei Suzuki, Shunsuke Tonogai, Tomoya Itakura
Developing foundation models for materials science has attracted attention. However, there is a lack of studies on inorganic materials due to the difficulty in the comprehensive representation of geometric concepts composing crystals: local atomic environments, their connections, and the global symmetries. We present a…
61relevance
26
CchemRxiv16 May 2022
Neural Scaling of Deep Chemical Models
Nathan Frey, Ryan Soklaski, Simon Axelrod, Siddharth Samsi + 3 more
Massive scale, both in terms of data availability and computation, enables significant breakthroughs in key application areas of deep learning such as natural language processing (NLP) and computer vision. There is emerging evidence that scale may be a key ingredient in scientific deep learning, but the importance of…
55relevance
27
CchemRxiv19 Jul 2024
"Amide - amine + alcohol = carboxylic acid." Chemical reactions as linear algebraic analogies in graph neural networks.
Amer El-Samman, Stijn De Baerdemacker
In deep learning methods, especially in the context of chemistry, there is an increasing urgency to uncover the hidden learning mechanisms often dubbed as ``black box." In this work, we show that graph models built on computational chemical data behave similar to natural language processing (NLP) models built on text…
55relevance
28
CchemRxiv15 Apr 2025
Comparative Analysis of Molecular Embeddings for Efficient Compound Similarity Search Using Vector Databases
Authors not listed
Compound similarity is fundamental to various cheminformatics analyses, particularly in the drug discovery industry, where the structure-activity principle is central to medicinal chemistry. Historically, binary fingerprints combined with Tanimoto and “Tanimoto-related metrics” (such as Dice, Sørensen–Dice, and…
55relevance
29
BbioRxiv10 Feb 2026
Token Alignment for Verifying LLM-Extracted Text
A. Sina Booeshaghi, Aaron Streets
Large language models excel at text extraction, but they sometimes hallucinate. A simple way to avoid hallucinations is to remove any extracted text that does not appear in the original source. This is easy when the extracted text is contiguous (findable with exact string matching), but much harder when it is…
50relevance

Results for text embedding

LDIR: Low-Dimensional Dense and Interpretable Text Embeddings with Relative Representations

Optimizing document retrieval using massive text embeddings and LLM prompt engineering

Text Embeddings Reveal (Almost) As Much As Text

Using word embeddings to investigate cultural biases Biases in word embeddings DURRHEIM et al.

Word Tour: One-dimensional Word Embeddings via the Traveling Salesman Problem

Rethinking the Privacy of Text Embeddings: A Reproducibility Study of "Text Embeddings Reveal (Almost) As Much As Text"

From Word Vectors to Multimodal Embeddings: Techniques, Applications, and Future Directions For Large Language Models

Semantic Token Reweighting for Interpretable and Controllable Text Embeddings in CLIP

Evaluating Embedding Generalization: How LLMs, LoRA, and SLERP Shape Representational Geometry

Improved Arabic query expansion using word embedding

TexIm FAST: Text-to-Image Representation for Semantic Similarity Evaluation using Transformers

Language with vision: A study on grounded word and sentence embeddings

A Survey of Artificial Intelligence Methods for Clinical Trial Outcome Prediction

Replacing non-biomedical concepts improves embedding of biomedical concepts

Fast2Vec, a modified model of FastText that enhances semantic analysis in topic evolution

Biomedical Text Classification Using Augmented Word Representation Based on Distributional and Relational Contexts

Adding layers of information to scRNA-seq data using pre-trained language models

Visualization of medical concepts represented using word embeddings: a scoping review

Domain-specific embeddings uncover latent genetics knowledge

Structure feature vectors derived from Robocrystallographer text descriptions of crystal structures using word embeddings

Benchmarking text-integrated protein language model embeddings and embedding fusion on diverse downstream tasks

Polynomial Trajectory Compression for Protein Language Model Embeddings

Graph-Text Contrastive Learning of Inorganic Crystal Structure toward a Foundation Model of Inorganic Materials

ntEmbd: Deep learning embedding for nucleotide sequences

Graph-Text Contrastive Learning of Inorganic Crystal Structure toward a Foundation Model of Inorganic Materials

Neural Scaling of Deep Chemical Models

"Amide - amine + alcohol = carboxylic acid." Chemical reactions as linear algebraic analogies in graph neural networks.

Comparative Analysis of Molecular Embeddings for Efficient Compound Similarity Search Using Vector Databases

Token Alignment for Verifying LLM-Extracted Text

LDIR: Low-Dimensional Dense and Interpretable Text Embeddings with Relative Representations

Optimizing document retrieval using massive text embeddings and LLM prompt engineering

Text Embeddings Reveal (Almost) As Much As Text

Using word embeddings to investigate cultural biases Biases in word embeddings DURRHEIM et al.

Word Tour: One-dimensional Word Embeddings via the Traveling Salesman Problem

Rethinking the Privacy of Text Embeddings: A Reproducibility Study of "Text Embeddings Reveal (Almost) As Much As Text"

From Word Vectors to Multimodal Embeddings: Techniques, Applications, and Future Directions For Large Language Models

Semantic Token Reweighting for Interpretable and Controllable Text Embeddings in CLIP

Evaluating Embedding Generalization: How LLMs, LoRA, and SLERP Shape Representational Geometry

Improved Arabic query expansion using word embedding

TexIm FAST: Text-to-Image Representation for Semantic Similarity Evaluation using Transformers

Language with vision: A study on grounded word and sentence embeddings

A Survey of Artificial Intelligence Methods for Clinical Trial Outcome Prediction

Replacing non-biomedical concepts improves embedding of biomedical concepts

Fast2Vec, a modified model of FastText that enhances semantic analysis in topic evolution

Biomedical Text Classification Using Augmented Word Representation Based on Distributional and Relational Contexts

Adding layers of information to scRNA-seq data using pre-trained language models

Visualization of medical concepts represented using word embeddings: a scoping review

Domain-specific embeddings uncover latent genetics knowledge

Structure feature vectors derived from Robocrystallographer text descriptions of crystal structures using word embeddings

Benchmarking text-integrated protein language model embeddings and embedding fusion on diverse downstream tasks

Polynomial Trajectory Compression for Protein Language Model Embeddings

Graph-Text Contrastive Learning of Inorganic Crystal Structure toward a Foundation Model of Inorganic Materials

ntEmbd: Deep learning embedding for nucleotide sequences

Graph-Text Contrastive Learning of Inorganic Crystal Structure toward a Foundation Model of Inorganic Materials

Neural Scaling of Deep Chemical Models

"Amide - amine + alcohol = carboxylic acid." Chemical reactions as linear algebraic analogies in graph neural networks.

Comparative Analysis of Molecular Embeddings for Efficient Compound Similarity Search Using Vector Databases

Token Alignment for Verifying LLM-Extracted Text