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

arXiv: https://arxiv.org/abs/2303.03124 · HTML: https://mlsanity.com/paper/2303.03124/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 33 references that are in the map.
- First posted on arXiv 6 Mar 2023.

## Builds on (most cited first, 23 of 23)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection](https://mlsanity.com/paper/2012.10289/) (2020)
- [Dear Sir or Madam, May I introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer](https://mlsanity.com/paper/1803.06535/) (2018)
- [Post-hoc Interpretability for Neural NLP: A Survey](https://mlsanity.com/paper/2108.04840/) (2021)
- [Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions](https://mlsanity.com/paper/2005.06676/) (2020)
- [Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded](https://mlsanity.com/paper/1902.03751/) (2019)
- [The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models](https://mlsanity.com/paper/2008.05122/) (2020)
- [Natural Language Processing: State of The Art, Current Trends and Challenges](https://mlsanity.com/paper/1708.05148/) (2017)
- [AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models](https://mlsanity.com/paper/1909.09251/) (2019)
- [Explanation-Based Human Debugging of NLP Models: A Survey](https://mlsanity.com/paper/2104.15135/) (2021)
- [ExplainaBoard: An Explainable Leaderboard for NLP](https://mlsanity.com/paper/2104.06387/) (2021)
- [Putting Humans in the Natural Language Processing Loop: A Survey](https://mlsanity.com/paper/2103.04044/) (2021)
- [Do Human Rationales Improve Machine Explanations?](https://mlsanity.com/paper/1905.13714/) (2019)
- [Refining Language Models with Compositional Explanations](https://mlsanity.com/paper/2103.10415/) (2021)
- ["That Is a Suspicious Reaction!": Interpreting Logits Variation to Detect NLP Adversarial Attacks](https://mlsanity.com/paper/2204.04636/) (2022)
- [AdapterHub Playground: Simple and Flexible Few-Shot Learning with Adapters](https://mlsanity.com/paper/2108.08103/) (2021)

## Built on by (most cited first, 8 of 8)

- [Content Moderation by LLM: From Accuracy to Legitimacy](https://mlsanity.com/paper/2409.03219/) (2024)
- [LLMCheckup: Conversational Examination of Large Language Models via Interpretability Tools and Self-Explanations](https://mlsanity.com/paper/2401.12576/) (2024)
- [Multilingual and Explainable Text Detoxification with Parallel Corpora](https://mlsanity.com/paper/2412.11691/) (2024)
- [InterroLang: Exploring NLP Models and Datasets through Dialogue-based Explanations](https://mlsanity.com/paper/2310.05592/) (2023)
- [Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models](https://mlsanity.com/paper/2305.12544/) (2023)
- [Aligning Attention with Human Rationales for Self-Explaining Hate Speech Detection](https://mlsanity.com/paper/2511.07065/) (2025)
- [WATCHED: A Web AI Agent Tool for Combating Hate Speech by Expanding Data](https://mlsanity.com/paper/2509.01379/) (2025)
- [PEACE 2.0: Grounded Explanations and Counter-Speech for Combating Hate Expressions](https://mlsanity.com/paper/2602.17467/) (2026)

## 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.