# Grow-and-Clip: Informative-yet-Concise Evidence Distillation for Answer Explanation

arXiv: https://arxiv.org/abs/2201.05088 · HTML: https://mlsanity.com/paper/2201.05088/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 57 references that are in the map.
- First posted on arXiv 13 Jan 2022.

## Builds on (most cited first, 30 of 33)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [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)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Big Bird: Transformers for Longer Sequences](https://mlsanity.com/paper/2007.14062/) (2020)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [Reading Wikipedia to Answer Open-Domain Questions](https://mlsanity.com/paper/1704.00051/) (2017)
- [Adversarial Examples for Evaluating Reading Comprehension Systems](https://mlsanity.com/paper/1707.07328/) (2017)
- [Explain Yourself! Leveraging Language Models for Commonsense Reasoning](https://mlsanity.com/paper/1906.02361/) (2019)
- [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://mlsanity.com/paper/2010.01057/) (2020)
- [A Diagnostic Study of Explainability Techniques for Text Classification](https://mlsanity.com/paper/2009.13295/) (2020)
- [End-to-End Open-Domain Question Answering with BERTserini](https://mlsanity.com/paper/1902.01718/) (2019)
- [Generating Fact Checking Explanations](https://mlsanity.com/paper/2004.05773/) (2020)
- [A Discrete Hard EM Approach for Weakly Supervised Question Answering](https://mlsanity.com/paper/1909.04849/) (2019)
- [Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering](https://mlsanity.com/paper/1911.03868/) (2019)
- [ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs](https://mlsanity.com/paper/1512.05193/) (2015)
- [Efficient and Robust Question Answering from Minimal Context over Documents](https://mlsanity.com/paper/1805.08092/) (2018)
- [Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering](https://mlsanity.com/paper/1711.05116/) (2017)
- [ReasoNet: Learning to Stop Reading in Machine Comprehension](https://mlsanity.com/paper/1609.05284/) (2016)
- [Hierarchical Question Answering for Long Documents](https://mlsanity.com/paper/1611.01839/) (2016)
- [Posterior Differential Regularization with f-divergence for Improving Model Robustness](https://mlsanity.com/paper/2010.12638/) (2020)
- [Probabilistic Assumptions Matter: Improved Models for Distantly-Supervised Document-Level Question Answering](https://mlsanity.com/paper/2005.01898/) (2020)
- [Evidence Sentence Extraction for Machine Reading Comprehension](https://mlsanity.com/paper/1902.08852/) (2019)
- [F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question Answering](https://mlsanity.com/paper/2010.06283/) (2020)

## Built on by (most cited first, 16 of 16)

- [Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models](https://mlsanity.com/paper/2407.04121/) (2024)
- [MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization](https://mlsanity.com/paper/2407.04118/) (2024)
- [Enhance Image-to-Image Generation with LLaVA-generated Prompts](https://mlsanity.com/paper/2406.01956/) (2024)
- [XMeCap: Meme Caption Generation with Sub-Image Adaptability](https://mlsanity.com/paper/2407.17152/) (2024)
- [Can Pre-trained Language Models Understand Chinese Humor?](https://mlsanity.com/paper/2407.04105/) (2024)
- [Hadamard Adapter: An Extreme Parameter-Efficient Adapter Tuning Method for Pre-trained Language Models](https://mlsanity.com/paper/2407.11033/) (2024)
- [Recent Advancement of Emotion Cognition in Large Language Models](https://mlsanity.com/paper/2409.13354/) (2024)
- [HOTVCOM: Generating Buzzworthy Comments for Videos](https://mlsanity.com/paper/2409.15196/) (2024)
- [Comprehensive Overview of Artificial Intelligence Applications in Modern Industries](https://mlsanity.com/paper/2409.13059/) (2024)
- [A Review of Electromagnetic Elimination Methods for low-field portable MRI scanner](https://mlsanity.com/paper/2406.17804/) (2024)
- [Enhancing Exchange Rate Forecasting with Explainable Deep Learning Models](https://mlsanity.com/paper/2410.19241/) (2024)
- [When More Words Say Less: Decoupling Length and Specificity in Image Description Evaluation](https://mlsanity.com/paper/2601.04609/) (2026)
- [Pre-trained Graphformer-based Ranking at Web-scale Search (Extended Abstract)](https://mlsanity.com/paper/2409.16590/) (2024)
- [Generative Pre-trained Ranking Model with Over-parameterization at Web-Scale (Extended Abstract)](https://mlsanity.com/paper/2409.16594/) (2024)
- [Guaranteeing Knowledge Integration with Joint Decoding for Retrieval-Augmented Generation](https://mlsanity.com/paper/2604.08046/) (2026)
- [From Memorization to Creation: Evaluating the Cognitive Depth of LLM-Generated Educational Questions](https://mlsanity.com/paper/2606.18257/) (2026)

## Abstract (arXiv)

Interpreting the predictions of existing Question Answering (QA) models is critical to many real-world intelligent applications, such as QA systems for healthcare, education, and finance. However, existing QA models lack interpretability and provide no feedback or explanation for end-users to help them understand why a specific prediction is the answer to a question. In this research, we argue that the evidences of an answer is critical to enhancing the interpretability of QA models. Unlike previous research that simply extracts several sentence(s) in the context as evidence, we are the first to explicitly define the concept of evidence as the supporting facts in a context which are informative, concise, and readable. Besides, we provide effective strategies to quantitatively measure the informativeness, conciseness and readability of evidence. Furthermore, we propose Grow-and-Clip Evidence Distillation (GCED) algorithm to extract evidences from the contexts by trade-off informativeness, conciseness, and readability. We conduct extensive experiments on the SQuAD and TriviaQA datasets with several baseline models to evaluate the effect of GCED on interpreting answers to questions. Human evaluation are also carried out to check the quality of distilled evidences. Experimental results show that automatic distilled evidences have human-like informativeness, conciseness and readability, which can enhance the interpretability of the answers to questions.