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Paper

Competency Problems: On Finding and Removing Artifacts in Language Data

by Independent / Community 023fc86c932fbc36702a6ad11c94ba419e1d8d88

Free2AITools Nexus Index

70.4

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 87

P: Popularity 64

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

Much recent work in NLP has documented dataset artifacts, bias, and spurious correlations between input features and output labels. However, how to tell which features have “spurious” instead of legitimate correlations is typically left unspecified. In this work we argue that for complex language understanding tasks, all simple feature correlations are spurious, and we formalize this notion into a class of problems which we call competency problems. For example, the word “amazing” on its own ...

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Semantic Scholar 124 Citations

Paper Information Summary
Entity Passport
Registry ID	023fc86c932fbc36702a6ad11c94ba419e1d8d88
License	ArXiv
Provider	semantic_scholar

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{023fc86c932fbc36702a6ad11c94ba419e1d8d88,
  author = {Unknown},
  title = {Competency Problems: On Finding and Removing Artifacts in Language Data Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/023fc86c932fbc36702a6ad11c94ba419e1d8d88}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). Competency Problems: On Finding and Removing Artifacts in Language Data [Paper]. Free2AITools. https://api.semanticscholar.org/023fc86c932fbc36702a6ad11c94ba419e1d8d88

🔬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) 87

Popularity (P) 64

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for Competency Problems: On Finding and Removing Artifacts in Language Data: Authority (A:87), Popularity (P:64), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

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HuggingFace API GitHub Metadata Arxiv Citation DB Methodology

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📝 Executive Summary

"Much recent work in NLP has documented dataset artifacts, bias, and spurious correlations between input features and output labels. However, how to tell which features have “spurious” instead of legitimate correlations is typically left unspecified. In this work we argue that for complex language understanding tasks, all simple feature correlations are spurious, and we formalize this notion into a class of problems which we call competency problems. For example, the word “amazing” on its own ..."

❝ Cite Node

@article{Unknown2026Competency,
  title={Competency Problems: On Finding and Removing Artifacts in Language Data},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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📊 Research Signals

📈124CitationsSemantic Scholar

🏛️87AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

🗂️text generationField

📦Data Source: semantic_scholar

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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
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downloads: 0
stars: null
forks: null
citations: 124

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