arXiv AI

Toten: Knowledge-Based Ontological Tokenization Of Physical Quantities And Technical Notation In Brazilian Portuguese

arXiv:2606. 19626v1 Announce Type: new Abstract: Byte-Pair Encoding tokenization is statistically efficient for vocabulary compression, but semantically blind to structured technical entities, fragmenting physical quantities, numbers, units, and symbolic expressions into lexically arbitrary subwords.

arXiv Machine Learning

22h ago

TokEval: A Tokenizer Evaluation Suite

arXiv:2608. 18062v1 Announce Type: cross Abstract: Language model tokenizers are typically selected with minimal evaluation, despite the fact that their design choices directly impact model capabilities.

By Clara Meister

llms nlp benchmarks safety

arXiv AI

Jul 15

Ontology-Amplified Distillation and Contextuality Auditing for Sovereign Enterprise Language Models: A Combined Proof-of-Mechanism and Negative-Results Method Study

arXiv:2607. 11948v1 Announce Type: new Abstract: Regulated financial institutions operating under data-residency rules need tenant-owned language models that can run inside the institution's perimeter.

By Thanh Luong Tuan

llms agents reinforcement-learning fine-tuning efficiency safety

Hugging Face Trending Papers

1d ago

TokEval: A Tokenizer Evaluation Suite

Language model tokenizers are typically selected with minimal evaluation, despite the fact that their design choices directly impact model capabilities. This can be partly attributed to a limited understanding of which tokenizer properties affect which aspects of downstream performance.

llms nlp benchmarks safety

arXiv AI

Aug 7

Tytan: Interactive Neurosymbolic Construction of Analytic Semantic Schemas from Relational Data

arXiv:2608. 06331v1 Announce Type: cross Abstract: From natural-language query interfaces to automated report generation, data analysis tools need a description of the data: the real-world entities it contains, which columns function as measures or identifiers, and how tables connect into units of analysis.

By Donna Hooshmand, Shubham Shahi, Cameron Barrie, Abhratanu Dutta, Marko Sterbentz, Harper Pack, Kristian J. Hammond

llms benchmarks

arXiv AI

Jun 9

A Variability-Based Framework for Interpretable Naming in Formal and Relational Concept Analysis

arXiv:2606. 08477v1 Announce Type: new Abstract: Knowledge extraction from symbolic data often produces abstractions that are formally defined but not immediately interpretable by users.

By Alain Gutierrez, Marianne Huchard, Pierre Martin, Andr\'e Miralles, Violaine Prince

llms

arXiv AI

Jun 19

CREDENCE: Claim Reduction for Decomposition & Enhanced Credibility -- Semantic Metrics and Convergence Analysis

arXiv:2606. 19819v1 Announce Type: cross Abstract: Decomposing compound sentences into atomic, verifiable claims is a prerequisite for reliable automated fact-checking.

By Phuong Huu Vu Tran, Thuan Duc Mai, Bach Xuan Le

llms benchmarks

arXiv AI

Jul 22

Frontier LLM-based agents can overcome the ontology curation bottleneck for natural phenotypes

arXiv:2605. 28965v2 Announce Type: replace Abstract: Linking free-text phenotype descriptions to ontology terms, typically referred to as phenotype annotation, is essential for the cross-study integration of comparative morphological data.

By James P. Balhoff, Hilmar Lapp

llms agents nlp benchmarks

arXiv AI

Aug 12

Curate Before You Connect: Identity and Ontology Tagging in a Production Knowledge Graph

arXiv:2608. 10644v1 Announce Type: new Abstract: Extraction produces candidate entities and relationships; writing them into a graph is where identity is decided, and identity decisions are destructive in a way extraction errors are not.

By Vaibhav Dangaich, Kevin Lewis, Kundeshwar Pundalik

benchmarks

arXiv AI

Jul 22

From Dependency to Compositionality: A Neurosymbolic Lifting of LLM Outputs via Combinatory Categorial Grammar

arXiv:2607. 18961v1 Announce Type: new Abstract: Large language models (LLMs) generate fluent text by incrementally predicting the next token from a prefix.

By Remo Pareschi

llms data-engineering

arXiv AI

Jul 3

Verifiable Knowledge Expansion through Retrieval-Grounded Formal Concept Analysis

arXiv:2607. 01773v1 Announce Type: new Abstract: Ontology construction requires deciding which objects, attributes, and structural relations should be accepted as valid knowledge.

By Yujin Yang, Heejung Lee

llms rag

arXiv Machine Learning

Jul 8

A semantic mutation metric for metamorphic relation adequacy in scientific computing programs

arXiv:2605. 17437v2 Announce Type: replace-cross Abstract: Context.

By Meng Li (School of Computing, University of South China, Hengyang, China, Hunan Engineering Research Center of Software Evaluation and Testing for Intellectual Equipment, Hengyang, China, CNNC Key Laboratory on High Trusted Computing, Hengyang, China), Xiaohua Yang (School of Computing, University of South China, Hengyang, China, Hunan Engineering Research Center of Software Evaluation and Testing for Intellectual Equipment, Hengyang, China, CNNC Key Laboratory on High Trusted Computing, Hengyang, China), Jie Liu (School of Computing, University of South China, Hengyang, China, Hunan Engineering Research Center of Software Evaluation and Testing for Intellectual Equipment, Hengyang, China, CNNC Key Laboratory on High Trusted Computing, Hengyang, China), Shiyu Yan (School of Computing, University of South China, Hengyang, China, Hunan Engineering Research Center of Software Evaluation and Testing for Intellectual Equipment, Hengyang, China, CNNC Key Laboratory on High Trusted Computing, Hengyang, China)

llms benchmarks

arXiv AI

Jul 24

Knowledge Graph Re-engineering Along the Ontological Continuum (extended version)

arXiv:2605. 22093v3 Announce Type: replace Abstract: Knowledge graphs have become the primary vehicle for data integration and are critical to the success of modern AI, but the diversity of KG modelling practices, from lightweight vocabularies to richly axiomatised ontologies, makes integration and reuse expensive and brittle.

By Enrico Daga, Valentina Tamma, Terry Payne

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