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Paper

Holistic Sentence Embeddings for Better Out-of-Distribution Detection

by Independent / Community 020b0c80270af27cd28899f2711eeed7a88b95cd

Free2AITools Nexus Index

64.3

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 75

P: Popularity 50

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

Detecting out-of-distribution (OOD) instances is significant for the safe deployment of NLP models. Among recent textual OOD detection works based on pretrained language models (PLMs), distance-based methods have shown superior performance. However, they estimate sample distance scores in the last-layer CLS embedding space and thus do not make full use of linguistic information underlying in PLMs. To address the issue, we propose to boost OOD detection by deriving more holistic sentence embed...

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Semantic Scholar 8 Citations

Paper Information Summary
Entity Passport
Registry ID	020b0c80270af27cd28899f2711eeed7a88b95cd
License	ArXiv
Provider	semantic_scholar

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{020b0c80270af27cd28899f2711eeed7a88b95cd,
  author = {Unknown},
  title = {Holistic Sentence Embeddings for Better Out-of-Distribution Detection Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/020b0c80270af27cd28899f2711eeed7a88b95cd}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). Holistic Sentence Embeddings for Better Out-of-Distribution Detection [Paper]. Free2AITools. https://api.semanticscholar.org/020b0c80270af27cd28899f2711eeed7a88b95cd

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 75

Popularity (P) 50

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for Holistic Sentence Embeddings for Better Out-of-Distribution Detection: Authority (A:75), Popularity (P:50), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology

Open data Updated: Live data

📝 Executive Summary

"Detecting out-of-distribution (OOD) instances is significant for the safe deployment of NLP models. Among recent textual OOD detection works based on pretrained language models (PLMs), distance-based methods have shown superior performance. However, they estimate sample distance scores in the last-layer CLS embedding space and thus do not make full use of linguistic information underlying in PLMs. To address the issue, we propose to boost OOD detection by deriving more holistic sentence embed..."

❝ Cite Node

@article{Unknown2026Holistic,
  title={Holistic Sentence Embeddings for Better Out-of-Distribution Detection},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

🔗 Full Paper

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Read the full paper on arXiv

📊 Research Signals

📈8CitationsSemantic Scholar

🏛️75AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

🗂️knowledge retrievalField

🏷️ Research Topics

embeddings

📦Data Source: semantic_scholar

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Source summary: Based on semantic_scholar metadata. Not a recommendation.

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🛡️ Paper Transparency Report

Technical metadata sourced from upstream repositories.

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🆔 Identity & Source

source: semantic_scholar
author: Unknown
license: ArXiv
tags: paper, research, academic

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
pipeline tag

📊 Engagement & Metrics

downloads: 0
stars: null
forks: null
citations: 8

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