Natural Language Explanation Generation

Latest papers

13 papers1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Understanding Confabulation and Rethinking Reconstruction in Activation Explanations
Sep 27, 2026Gert Lek, Zixuan Xia, Pin-Yu Chen +1Transformer Interpretability Faithfulness of Language Model Explanations
Universit´e de Neuchˆatel · Universität Bern · IBM Research +1
ExpArt-KG: Artwork Image Description Generation through Iterative Exploration of Knowledge Graphs
Sep 1, 2026Yuta Kato, Shintaro Ozaki, Kazuki Hayashi +4Retrieval-Augmented Generation Image Captioning
The University of Tokyo · Nara Institute of Science and Technology (NAIST)
Evaluating Explanation-Driven Vision-Language Reasoning via Generation Order Interventions
Aug 26, 2026Siting Liang, Luca Rippe, Omar Adjali +1VLM Reasoning Reasoning for VLMs
German Research Center for Artificial Intelligence · University of Oldenburg
Detection, Attribution, Narration: An End-to-End Pipeline for Explainable Money Mule Identification
Jul 20, 2026Yuge Zhang, Yuanxing Zhang, Yichao Jin +7Natural Language Explanation Generation Financial Services
OCBC, Singapore
CIExplainer++: Generating Causal and Interpretable Explanations for Graph Neural Networks
Jun 17, 2026Francisco Caldas, Sahil Satish Kumar, Ruben Belo +1Natural Language Explanation Generation Causal Interpretability
NOVA LINCS, NOVA School of Science and Technology, Lisbon, Portugal
Generative Explainability for Next-Generation Networks: LLM-Augmented XAI with Mutual Feature Interactions
Jun 9, 2026Kiarash Rezaei, Omran Ayoub, Sebastian Troia +3Natural Language Explanation Generation Explainable Artificial Intelligence
Department of Electrical Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden · University of Applied Sciences and Arts of Southern Switzerland, 6928 Lugano, Switzerland · Politecnico di Milano, 20133 Milan, Italy +1
Human Label Variation as Stable Signal: Learning Annotator-Specific Explanation Behavior via Cross-Annotator Preference Optimization
May 27, 2026Beiduo Chen, Pingjun Hong, Ziyun Zhang +3Natural Language Explanation Generation LLM-Assisted Annotation
U MaiNLP, Center for Information and Language Processing, LMU Munich, Germany · Æ Munich Center for Machine Learning (MCML), Munich, Germany · [ University of Vienna, Austria +1
Lost in Interpretation: The Plausibility-Faithfulness Trade-off in Cross-Lingual Explanations
May 19, 2026Somnath Banerjee, Pranav Jha, Rima Hazra +1Language Model Auditing Cross-Lingual Reasoning in Language Models
Indian Institute of Technology Kharagpur · TCG Crest · National University of Singapore
Toward Template-Free Explainability for Monte Carlo Tree Search
May 15, 2026Siqi Lu, Mirsaleh Bahavarnia, Hiba Baroud +3Natural Language Explanation Generation Explainable Artificial Intelligence
The College of William & Mary · Vanderbilt University · George Mason University
On the Importance and Evaluation of Narrativity in Natural Language AI Explanations
Apr 20, 2026Mateusz Cedro, David MartensNatural Language Explanation Generation Explainable Artificial Intelligence
University of Antwerp, Belgium
WeNLEX: Weakly Supervised Natural Language Explanations for Multilabel Chest X-ray Classification
Mar 19, 2026Isabel Rio-Torto, Jaime S. Cardoso, Luís F. TeixeiraNatural Language Explanation Generation Explainable Medical Image Analysis
INESC TEC · Universidade do Porto
Improving Implicit Discourse Relation Recognition with Natural Language Explanations from LLMs
Feb 25, 2026Heng Wang, Changxing WuNatural Language Explanation Generation
School of Information and Software Engineering, East China Jiaotong University, Nanchang, China
Simplifying Outcomes of Language Model Component Analyses with ELIA
Feb 20, 2026Aaron Louis Eidt, Nils FeldhusNatural Language Explanation Generation LLM Interpretability
Technische Universität Berlin · Fraunhofer Heinrich Hertz Institute · BIFOLD – Berlin Institute for the Foundations of Learning and Data

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Understanding Confabulation and Rethinking Reconstruction in Activation Explanations

Gert Lek, Zixuan Xia, Pin-Yu Chen, Lydia Y. Chen

Universit´e de Neuchˆatel · Universität Bern · IBM Research+1

Transformer Interpretability Faithfulness of Language Model Explanations Autoencoders Neural Network Interpretability Natural Language Explanation Generation

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Natural Language Autoencoders (NLAs) produce unsupervised text explanations of a model's activations: a verbalizer describes an activation and a reconstructor learns to recover it from this text. Under the established point-reconstruction NLA training recipe, explanations become more useful for predicting model behavior while also increasingly introducing unsupported details and exhibiting writing defects. To assess these changes separately, we introduce a standardized evaluation framework for unstructured NLA explanations, measuring information recoverable from explanations, contextual support for their claims, and writing quality. To address confabulation and writing defects, we move beyond predicting a single activation: explanations can distinguish distributions of possible activations even when their means and optimal point-reconstruction rewards are identical. We introduce Flow-NLA, which models the distribution of activations compatible with an explanation and trains the verbalizer using a diffusion likelihood bound. Across Qwen, Gemma, and Apertus, this richer signal retains the utility gains of point reconstruction while curbing the growth of confabulation and writing defects, opening up a direction for improving activation-derived training to encourage more informative, supported, and readable explanations. Code and evaluation prompts will be made publicly available upon acceptance.