# ConvXAI: Delivering Heterogeneous AI Explanations via Conversations to Support Human-AI Scientific Writing

arXiv: https://arxiv.org/abs/2305.09770 · HTML: https://mlsanity.com/paper/2305.09770/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 57 of its 93 references that are in the map.
- First posted on arXiv 16 May 2023.

## Builds on (most cited first, 30 of 57)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018)
- [Attention is not not Explanation](https://mlsanity.com/paper/1908.04626/) (2019)
- [Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?](https://mlsanity.com/paper/2004.03685/) (2020)
- [Towards a Human-like Open-Domain Chatbot](https://mlsanity.com/paper/2001.09977/) (2020)
- [e-SNLI: Natural Language Inference with Natural Language Explanations](https://mlsanity.com/paper/1812.01193/) (2018)
- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [Explain Yourself! Leveraging Language Models for Commonsense Reasoning](https://mlsanity.com/paper/1906.02361/) (2019)
- [Rationalizing Neural Predictions](https://mlsanity.com/paper/1606.04155/) (2016)
- [Manipulating and Measuring Model Interpretability](https://mlsanity.com/paper/1802.07810/) (2018)
- [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020)
- [CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities](https://mlsanity.com/paper/2201.06796/) (2022)
- [Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks](https://mlsanity.com/paper/1608.04207/) (2016)
- [Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?](https://mlsanity.com/paper/2005.01831/) (2020)
- [ParlAI: A Dialog Research Software Platform](https://mlsanity.com/paper/1705.06476/) (2017)
- [The elephant in the interpretability room: Why use attention as explanation when we have saliency methods?](https://mlsanity.com/paper/2010.05607/) (2020)
- [Explaining NLP Models via Minimal Contrastive Editing (MiCE)](https://mlsanity.com/paper/2012.13985/) (2020)
- [Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences](https://mlsanity.com/paper/1712.00547/) (2017)
- [ACUTE-EVAL: Improved Dialogue Evaluation with Optimized Questions and Multi-turn Comparisons](https://mlsanity.com/paper/1909.03087/) (2019)
- [AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models](https://mlsanity.com/paper/1909.09251/) (2019)
- [How do Decisions Emerge across Layers in Neural Models? Interpretation with Differentiable Masking](https://mlsanity.com/paper/2004.14992/) (2020)

## Built on by (most cited first, 30 of 31)

- [Plan-Then-Execute: An Empirical Study of User Trust and Team Performance When Using LLM Agents As A Daily Assistant](https://mlsanity.com/paper/2502.01390/) (2025)
- [LLMs for Explainable AI: A Comprehensive Survey](https://mlsanity.com/paper/2504.00125/) (2025)
- [IdeaSynth: Iterative Research Idea Development Through Evolving and Composing Idea Facets with Literature-Grounded Feedback](https://mlsanity.com/paper/2410.04025/) (2024)
- [CulturalTeaming: AI-Assisted Interactive Red-Teaming for Challenging LLMs' (Lack of) Multicultural Knowledge](https://mlsanity.com/paper/2404.06664/) (2024)
- [Is Conversational XAI All You Need? Human-AI Decision Making With a Conversational XAI Assistant](https://mlsanity.com/paper/2501.17546/) (2025)
- [LLMs for XAI: Future Directions for Explaining Explanations](https://mlsanity.com/paper/2405.06064/) (2024)
- [LLMCheckup: Conversational Examination of Large Language Models via Interpretability Tools and Self-Explanations](https://mlsanity.com/paper/2401.12576/) (2024)
- [PersonaFlow: Designing LLM-Simulated Expert Perspectives for Enhanced Research Ideation](https://mlsanity.com/paper/2409.12538/) (2024)
- [Not Just Novelty: A Longitudinal Study on Utility and Customization of an AI Workflow](https://mlsanity.com/paper/2402.09894/) (2024)
- [Co-Writing with AI, on Human Terms: Aligning Research with User Demands Across the Writing Process](https://mlsanity.com/paper/2504.12488/) (2025)
- [Collage is the New Writing: Exploring the Fragmentation of Text and User Interfaces in AI Tools](https://mlsanity.com/paper/2405.17217/) (2024)
- [InterroLang: Exploring NLP Models and Datasets through Dialogue-based Explanations](https://mlsanity.com/paper/2310.05592/) (2023)
- [Where Do I 'Add the Egg'?: Exploring Agency and Ownership in AI Creative Co-Writing Systems](https://mlsanity.com/paper/2509.15440/) (2025)
- [Explingo: Explaining AI Predictions using Large Language Models](https://mlsanity.com/paper/2412.05145/) (2024)
- [May I Ask a Follow-up Question? Understanding the Benefits of Conversations in Neural Network Explainability](https://mlsanity.com/paper/2309.13965/) (2023)
- [CoXQL: A Dataset for Parsing Explanation Requests in Conversational XAI Systems](https://mlsanity.com/paper/2406.08101/) (2024)
- [StorySage: Conversational Autobiography Writing Powered by a Multi-Agent Framework](https://mlsanity.com/paper/2506.14159/) (2025)
- [An Agentic Approach to Generating XAI-Narratives](https://mlsanity.com/paper/2603.20003/) (2026)
- [Narrix: Remixing Narrative Strategies from Examples for Story Writing](https://mlsanity.com/paper/2604.07643/) (2026)
- [Fine-Grained Appropriate Reliance: Human-AI Collaboration with a Multi-Step Transparent Decision Workflow for Complex Task Decomposition](https://mlsanity.com/paper/2501.10909/) (2025)
- [Show Me How: Benefits and Challenges of Agent-Augmented Counterfactual Explanations for Non-Expert Users](https://mlsanity.com/paper/2504.13897/) (2025)
- [Let's Get You Hired: A Job Seeker's Perspective on Multi-Agent Recruitment Systems for Explaining Hiring Decisions](https://mlsanity.com/paper/2505.20312/) (2025)
- [Talk2Care: Facilitating Asynchronous Patient-Provider Communication with Large-Language-Model](https://mlsanity.com/paper/2309.09357/) (2023)
- [Conversational Explanations: Discussing Explainable AI with Non-AI Experts](https://mlsanity.com/paper/2503.16444/) (2025)
- [Designing Proactive Thought Partners for Writing](https://mlsanity.com/paper/2609.01588/) (2026)
- [AI Ethics and Social Norms: Exploring ChatGPT's Capabilities From What to How](https://mlsanity.com/paper/2504.18044/) (2025)
- [Multilingual Datasets for Custom Input Extraction and Explanation Requests Parsing in Conversational XAI Systems](https://mlsanity.com/paper/2508.14982/) (2025)
- [Exploring Plan Space through Conversation: An Agentic Framework for LLM-Mediated Explanations in Planning](https://mlsanity.com/paper/2603.02070/) (2026)
- [Persistent and Conversational Multi-Method Explainability for Trustworthy Financial AI](https://mlsanity.com/paper/2605.11687/) (2026)
- [Can Conversational XAI Improve User Performance? An Experimental Study](https://mlsanity.com/paper/2605.20439/) (2026)

## Abstract (arXiv)

Despite a surge collection of XAI methods, users still struggle to obtain required AI explanations. Previous research suggests chatbots as dynamic solutions, but the effective design of conversational XAI agents for practical human needs remains under-explored. This paper focuses on Conversational XAI for AI-assisted scientific writing tasks. Drawing from human linguistic theories and formative studies, we identify four design rationales: "multifaceted", "controllability", "mix-initiative", "context-aware drill-down". We incorporate them into an interactive prototype, ConvXAI, which facilitates heterogeneous AI explanations for scientific writing through dialogue. In two studies with 21 users, ConvXAI outperforms a GUI-based baseline on improving human-perceived understanding and writing improvement. The paper further discusses the practical human usage patterns in interacting with ConvXAI for scientific co-writing.