Imagen de Portada

Evaluating NLP Embedding Models for Handling Science-Specific Symbolic Expressions in Student Texts

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Bleckmann, Tom, Tschisgale, Paul
Formato:	Preprint
Publicado:	2025
Materias:	Computation and Language Artificial Intelligence Physics Education
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Daily and Weekly Periodicity in Large Language Model Performance and Its Implications for Research
por: Tschisgale, Paul, et al.
Publicado: (2026)

Developing and Evaluating a Large Language Model-Based Automated Feedback System Grounded in Evidence-Centered Design for Supporting Physics Problem Solving
por: Maus, Holger, et al.
Publicado: (2025)

Evaluating GPT- and Reasoning-based Large Language Models on Physics Olympiad Problems: Surpassing Human Performance and Implications for Educational Assessment
por: Tschisgale, Paul, et al.
Publicado: (2025)

Evaluation Metrics for Text Data Augmentation in NLP
por: Amadeus, Marcellus, et al.
Publicado: (2024)

Dissecting Physics Reasoning in Small Language Models: A Multi-Dimensional Analysis from an Educational Perspective
por: Scaria, Nicy, et al.
Publicado: (2025)

Assessing Large Language Models in Mechanical Engineering Education: A Study on Mechanics-Focused Conceptual Understanding
por: Tian, Jie, et al.
Publicado: (2024)

Mastering Olympiad-Level Physics with Artificial Intelligence
por: Jian, Dong-Shan, et al.
Publicado: (2025)

Evolutionary Feature-wise Thresholding for Binary Representation of NLP Embeddings
por: Sinha, Soumen, et al.
Publicado: (2025)

A Personalised Learning Tool for Physics Undergraduate Students Built On a Large Language Model for Symbolic Regression
por: Zhu, Yufan, et al.
Publicado: (2024)

Bias in Text Embedding Models
por: Rakivnenko, Vasyl, et al.
Publicado: (2024)

Comparative Evaluation of ChatGPT and DeepSeek Across Key NLP Tasks: Strengths, Weaknesses, and Domain-Specific Performance
por: Etaiwi, Wael, et al.
Publicado: (2025)

Enhancing Steganographic Text Extraction: Evaluating the Impact of NLP Models on Accuracy and Semantic Coherence
por: Li, Mingyang, et al.
Publicado: (2024)

An NLP Crosswalk Between the Common Core State Standards and NAEP Item Specifications
por: Camilli, Gregory
Publicado: (2024)

Optimizing Sentence Embedding with Pseudo-Labeling and Model Ensembles: A Hierarchical Framework for Enhanced NLP Tasks
por: Liu, Ziwei, et al.
Publicado: (2025)

Handling Numeric Expressions in Automatic Speech Recognition
por: Huber, Christian, et al.
Publicado: (2024)

Select, Label, Evaluate: Active Testing in NLP
por: Purificato, Antonio, et al.
Publicado: (2026)

ERAS: Evaluating the Robustness of Chinese NLP Models to Morphological Garden Path Errors
por: Li, Qinchan, et al.
Publicado: (2024)

Advancing NLP Models with Strategic Text Augmentation: A Comprehensive Study of Augmentation Methods and Curriculum Strategies
por: Kesgin, Himmet Toprak, et al.
Publicado: (2024)

NLP Cluster Analysis of Common Core State Standards and NAEP Item Specifications
por: Camilli, Gregory, et al.
Publicado: (2024)

Do They Understand Them? An Updated Evaluation on Nonbinary Pronoun Handling in Large Language Models
por: Tang, Xushuo, et al.
Publicado: (2025)

Digestion Algorithm in Hierarchical Symbolic Forests: A Fast Text Normalization Algorithm and Semantic Parsing Framework for Specific Scenarios and Lightweight Deployment
por: You, Kevin
Publicado: (2024)

Text2World: Benchmarking Large Language Models for Symbolic World Model Generation
por: Hu, Mengkang, et al.
Publicado: (2025)

CardiffNLP at CLEARS-2025: Prompting Large Language Models for Plain Language and Easy-to-Read Text Rewriting
por: Ayesh, Mutaz, et al.
Publicado: (2025)

Linearly-Interpretable Concept Embedding Models for Text Analysis
por: De Santis, Francesco, et al.
Publicado: (2024)

Fine-Tuning BERT for Domain-Specific Question Answering: Toward Educational NLP Resources at University Scale
por: Montfrond, Aurélie
Publicado: (2025)

Machine Learning-based NLP for Emotion Classification on a Cholera X Dataset
por: Jideani, Paul, et al.
Publicado: (2024)

Revealing the Numeracy Gap: An Empirical Investigation of Text Embedding Models
por: Deng, Ningyuan, et al.
Publicado: (2025)

Sticking to the Mean: Detecting Sticky Tokens in Text Embedding Models
por: Chen, Kexin, et al.
Publicado: (2025)

The Science of Evaluating Foundation Models
por: Yuan, Jiayi, et al.
Publicado: (2025)

EmbeddingGemma: Powerful and Lightweight Text Representations
por: Vera, Henrique Schechter, et al.
Publicado: (2025)

How do Large Language Models Handle Multilingualism?
por: Zhao, Yiran, et al.
Publicado: (2024)

Large Language Models Meet NLP: A Survey
por: Qin, Libo, et al.
Publicado: (2024)

Deanthropomorphising NLP: Can a Language Model Be Conscious?
por: Shardlow, Matthew, et al.
Publicado: (2022)

A Method for Handling Negative Similarities in Explainable Graph Spectral Clustering of Text Documents -- Extended Version
por: Kłopotek, Mieczysław A., et al.
Publicado: (2025)

Gecko: Versatile Text Embeddings Distilled from Large Language Models
por: Lee, Jinhyuk, et al.
Publicado: (2024)

German Text Embedding Clustering Benchmark
por: Wehrli, Silvan, et al.
Publicado: (2024)

Using Generative Text Models to Create Qualitative Codebooks for Student Evaluations of Teaching
por: Katz, Andrew, et al.
Publicado: (2024)

The Scandinavian Embedding Benchmarks: Comprehensive Assessment of Multilingual and Monolingual Text Embedding
por: Enevoldsen, Kenneth, et al.
Publicado: (2024)

Arctic-Embed: Scalable, Efficient, and Accurate Text Embedding Models
por: Merrick, Luke, et al.
Publicado: (2024)

Hakim: Farsi Text Embedding Model
por: Sarmadi, Mehran, et al.
Publicado: (2025)