# Challenges and Opportunities in Text Generation Explainability

**Authors:** Kenza Amara, Rita Sevastjanova, Mennatallah El-Assady  
**arXiv:** [2405.08468](https://arxiv.org/abs/2405.08468)  
**Date:** 2024-05-14  
**Summary page:** https://www.summarizepaper.com/en/paper/2405.08468v1/

## Key points

- Importance of interpretability in Natural Language Processing (NLP) due to large language models and text generation tasks using autoregressive models
- Development of model-agnostic explainable artificial intelligence (xAI) methods tailored to text generation
- Challenges in attribution-based explainability methods, including tokenization issues, defining explanation similarity, determining token importance and prediction change metrics, level of human intervention required, and creating suitable test datasets
- Lack of comprehensive benchmark for rigorously characterizing xAI methods for text generation
- Opportunity for ML practitioners to craft a taxonomy delineating main properties and unveiling datasets tailored for better characterizing explainability methods
- Call to action for creating a standardized benchmark with specific perturbations to evaluate xAI methods across various dimensions in text generation tasks

## Summary

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The importance of interpretability in Natural Language Processing (NLP) has grown significantly due to the rise of large language models, particularly in tasks such as text generation using autoregressive models. The NLP community has shown a growing interest in understanding text generation, leading to the development of model-agnostic explainable artificial intelligence (xAI) methods tailored to this task. However, there are 17 challenges categorized into three groups that arise during the development and assessment of attribution-based explainability methods. These challenges include issues related to tokenization, defining explanation similarity, determining token importance and prediction change metrics, the level of human intervention required, and creating suitable test datasets. One specific challenge is benchmark completeness as it is impractical to assess explainability across all variations due to the vast number of potential perturbations in syntax, semantics, grammar, and other linguistic elements. While diverse perturbed datasets could shed light on method characteristics and linguistic robustness, there is currently no comprehensive benchmark available for rigorously characterizing xAI methods for text generation. <break>

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This presents an opportunity for ML practitioners to identify directions for better characterizing explainability methods by crafting a taxonomy delineating main properties and unveiling datasets tailored for this purpose. By establishing a standardized benchmark with specific perturbations, important properties of existing explainability methods can be identified and thorough comparisons conducted. In conclusion,<break>
 
explaining next-token generation poses challenges at each stage of the explanation process for text generation.<break>

Human intervention is often necessary to address these challenges,<break>
 
and opportunities exist to propose more robust xAI evaluation through well-designed perturbed datasets.<break>

This paper serves as a call to action for creating a comprehensive benchmark to evaluate xAI methods across various dimensions such as semantic comprehension, syntactic robustness, and grammatical fidelity in text generation tasks.