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Paper

Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text

by Independent / Community 03031d20494b9634b27fc5be6bce203a87383343

Free2AITools Nexus Index

70.3

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 —

Machine Learning has seen tremendous growth recently, which has led to a larger adaptation of ML systems for educational assessments, credit risk, healthcare, employment, criminal justice, to name a few. The trustworthiness of ML and NLP systems is a crucial aspect and requires a guarantee that the decisions they make are fair and robust. Aligned with this, we propose a novel framework GYC, to generate a set of exhaustive counterfactual text, which are crucial for testing these ML systems. Ou...

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Semantic Scholar 116 Citations

Paper Information Summary
Entity Passport
Registry ID	03031d20494b9634b27fc5be6bce203a87383343
License	ArXiv
Provider	semantic_scholar

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{03031d20494b9634b27fc5be6bce203a87383343,
  author = {Unknown},
  title = {Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/03031d20494b9634b27fc5be6bce203a87383343}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text [Paper]. Free2AITools. https://api.semanticscholar.org/03031d20494b9634b27fc5be6bce203a87383343

🔬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 Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text: 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

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology

Open data Updated: Live data

📝 Executive Summary

"Machine Learning has seen tremendous growth recently, which has led to a larger adaptation of ML systems for educational assessments, credit risk, healthcare, employment, criminal justice, to name a few. The trustworthiness of ML and NLP systems is a crucial aspect and requires a guarantee that the decisions they make are fair and robust. Aligned with this, we propose a novel framework GYC, to generate a set of exhaustive counterfactual text, which are crucial for testing these ML systems. Ou..."

❝ Cite Node

@article{Unknown2026Generate,
  title={Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

🔗 Full Paper

Free2AITools indexes the abstract and factual metadata for this paper. Read the complete, authoritative paper on the official source.

Read the full paper on arXiv

📊 Research Signals

📈116CitationsSemantic Scholar

🏛️87AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

🗂️text generationField

📦Data Source: semantic_scholar

🔄 Updated daily

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

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🛡️ Paper Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 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: 116

Data indexed from public sources. Updated daily.