# An Active Learning Framework for Inclusive Generation by Large Language Models

arXiv: https://arxiv.org/abs/2410.13641 · HTML: https://mlsanity.com/paper/2410.13641/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge distillation for LLMs, one of 12 directions in Knowledge distillation.
- Builds on 26 of its 40 references that are in the map.
- First posted on arXiv 17 Oct 2024.

## Placed in

- Knowledge distillation / [Knowledge distillation for LLMs](https://mlsanity.com/distill/knowledge-distillation-for-llms/)

## Builds on (most cited first, 26 of 26)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes](https://mlsanity.com/paper/2305.02301/) (2023)
- [Mitigating Gender Bias in Natural Language Processing: Literature Review](https://mlsanity.com/paper/1906.08976/) (2019)
- [Predicting the Type and Target of Offensive Posts in Social Media](https://mlsanity.com/paper/1902.09666/) (2019)
- [Symbolic Knowledge Distillation: from General Language Models to Commonsense Models](https://mlsanity.com/paper/2110.07178/) (2021)
- [Gender Bias in Neural Natural Language Processing](https://mlsanity.com/paper/1807.11714/) (2018)
- [Learning Gender-Neutral Word Embeddings](https://mlsanity.com/paper/1809.01496/) (2018)
- [Style Transfer Through Back-Translation](https://mlsanity.com/paper/1804.09000/) (2018)
- [Mitigating Language-Dependent Ethnic Bias in BERT](https://mlsanity.com/paper/2109.05704/) (2021)
- [Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speech](https://mlsanity.com/paper/2107.08720/) (2021)
- [Thou shalt not hate: Countering Online Hate Speech](https://mlsanity.com/paper/1808.04409/) (2018)
- [Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer](https://mlsanity.com/paper/1805.07685/) (2018)
- [Towards Knowledge-Grounded Counter Narrative Generation for Hate Speech](https://mlsanity.com/paper/2106.11783/) (2021)
- [Balancing out Bias: Achieving Fairness Through Balanced Training](https://mlsanity.com/paper/2109.08253/) (2021)
- [Robust Safety Classifier for Large Language Models: Adversarial Prompt Shield](https://mlsanity.com/paper/2311.00172/) (2023)
- [Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translation](https://mlsanity.com/paper/2203.09866/) (2022)
- [APPDIA: A Discourse-aware Transformer-based Style Transfer Model for Offensive Social Media Conversations](https://mlsanity.com/paper/2209.08207/) (2022)
- [Multi-task Active Learning for Pre-trained Transformer-based Models](https://mlsanity.com/paper/2208.05379/) (2022)
- [DisCGen: A Framework for Discourse-Informed Counterspeech Generation](https://mlsanity.com/paper/2311.18147/) (2023)
- [D-CALM: A Dynamic Clustering-based Active Learning Approach for Mitigating Bias](https://mlsanity.com/paper/2305.17013/) (2023)
- [Active Learning for Robust and Representative LLM Generation in Safety-Critical Scenarios](https://mlsanity.com/paper/2410.11114/) (2024)
- [Modeling Intensification for Sign Language Generation: A Computational Approach](https://mlsanity.com/paper/2203.09679/) (2022)
- [MedNgage: A Dataset for Understanding Engagement in Patient-Nurse Conversations](https://mlsanity.com/paper/2305.19981/) (2023)

## Built on by (most cited first, 5 of 5)

- [Coherence-Driven Multimodal Safety Dialogue with Active Learning for Embodied Agents](https://mlsanity.com/paper/2410.14141/) (2024)
- [CrowdAgent: Multi-Agent Managed Multi-Source Annotation System](https://mlsanity.com/paper/2509.14030/) (2025)
- [From Human Annotation to Automation: LLM-in-the-Loop Active Learning for Arabic Sentiment Analysis](https://mlsanity.com/paper/2509.23515/) (2025)
- [Moonworks Lunara Aesthetic Dataset](https://mlsanity.com/paper/2601.07941/) (2026)
- [Moonworks Lunara Aesthetic II: An Image Variation Dataset](https://mlsanity.com/paper/2602.01666/) (2026)

## Abstract (arXiv)

Ensuring that Large Language Models (LLMs) generate text representative of diverse sub-populations is essential, particularly when key concepts related to under-represented groups are scarce in the training data. We address this challenge with a novel clustering-based active learning framework, enhanced with knowledge distillation. The proposed framework transforms the intermediate outputs of the learner model, enabling effective active learning for generative tasks for the first time. Integration of clustering and knowledge distillation yields more representative models without prior knowledge of underlying data distribution and overbearing human efforts. We validate our approach in practice through case studies in counter-narration and style transfer. We construct two new datasets in tandem with model training, showing a performance improvement of 2%-10% over baseline models. Our results also show more consistent performance across various data subgroups and increased lexical diversity, underscoring our model's resilience to skewness in available data. Further, our results show that the data acquired via our approach improves the performance of secondary models not involved in the learning loop, showcasing practical utility of the framework.