mlsanityData to 7 Oct 2026

IFAN: An Explainability-Focused Interaction Framework for Humans and NLP Models

arXiv 2303.03124 ↗First posted 6 Mar 2023cs.CL cs.AIcited by 8

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Abstract (arXiv)

Interpretability and human oversight are fundamental pillars of deploying complex NLP models into real-world applications. However, applying explainability and human-in-the-loop methods requires technical proficiency. Despite existing toolkits for model understanding and analysis, options to integrate human feedback are still limited. We propose IFAN, a framework for real-time explanation-based interaction with NLP models. Through IFAN's interface, users can provide feedback to selected model explanations, which is then integrated through adapter layers to align the model with human rationale. We show the system to be effective in debiasing a hate speech classifier with minimal impact on performance. IFAN also offers a visual admin system and API to manage models (and datasets) as well as control access rights. A demo is live at https://ifan.ml.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 23 of its 33 references that are in the map. First posted on arXiv 6 Mar 2023.

Builds on

23 of 33 references.

References · 23

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  4. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  5. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  6. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,195
  7. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,968
  8. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  9. 2020HateXplain: A Benchmark Dataset for Explainable Hate Speech Detectioncited by 403
  10. 2018Dear Sir or Madam, May I introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfercited by 209
  11. 2021Post-hoc Interpretability for Neural NLP: A Surveycited by 155
  12. 2020Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functionscited by 144
11 more
  1. 2019Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Groundedcited by 127
  2. 2020The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Modelscited by 125
  3. 2017Natural Language Processing: State of The Art, Current Trends and Challengescited by 114
  4. 2019AllenNLP Interpret: A Framework for Explaining Predictions of NLP Modelscited by 74
  5. 2021Explanation-Based Human Debugging of NLP Models: A Surveycited by 53
  6. 2021ExplainaBoard: An Explainable Leaderboard for NLPcited by 50
  7. 2021Putting Humans in the Natural Language Processing Loop: A Surveycited by 40
  8. 2019Do Human Rationales Improve Machine Explanations?cited by 33
  9. 2021Refining Language Models with Compositional Explanationscited by 27
  10. 2022"That Is a Suspicious Reaction!": Interpreting Logits Variation to Detect NLP Adversarial Attackscited by 24
  11. 2021AdapterHub Playground: Simple and Flexible Few-Shot Learning with Adapterscited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 2; 2026: 1.

8 papers.

  1. 2024Content Moderation by LLM: From Accuracy to Legitimacycited by 30
  2. 2024LLMCheckup: Conversational Examination of Large Language Models via Interpretability Tools and Self-Explanationscited by 16
  3. 2024Multilingual and Explainable Text Detoxification with Parallel Corporacited by 12
  4. 2023InterroLang: Exploring NLP Models and Datasets through Dialogue-based Explanationscited by 11
  5. 2023Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Modelscited by 7
  6. 2025Aligning Attention with Human Rationales for Self-Explaining Hate Speech Detectioncited by 3
  7. 2025WATCHED: A Web AI Agent Tool for Combating Hate Speech by Expanding Datacited by 0
  8. 2026PEACE 2.0: Grounded Explanations and Counter-Speech for Combating Hate Expressionscited by 0