On Behalf of the Stakeholders: Trends in NLP Model Interpretability in the Era of LLMs (2025.naacl-long)
Copied to clipboard
| Challenge: | Recent advances in NLP systems have led to widespread adoption by a broad spectrum of users across various domains, impacting decision-making, the job market, society, and scientific research. |
| Approach: | They examine existing interpretability paradigms, their properties, and their relevance to different stakeholders by analyzing trends from the past decade across multiple research fields. |
| Outcome: | The proposed models are complex and opaque and are often overlooked by technical surveys. |
Similar Papers
On the Gap between Adoption and Understanding in NLP (2021.findings-acl)
Copied to clipboard
| Challenge: | a recent paper argues that current publications foster a gap between adoption and understanding of models . it also makes it easier to meet publication demands with method papers, argues the paper . |
| Approach: | They argue that current NLP publication models foster a gap between adoption and understanding of models . they argue that everlarger models make it harder to explain how our methods work . |
| Outcome: | The authors argue that current publications foster a gap between adoption and understanding of models . they argue that the rise of everlarger models makes it harder to explain how our methods work . |
A Survey of the State of Explainable AI for Natural Language Processing (2020.aacl-main)
Copied to clipboard
| Challenge: | Recent years have seen significant advances in the quality of state-of-the-art models, but they have come at the expense of models becoming less interpretable. |
| Approach: | This survey examines the current state of Explainable AI within the domain of NLP . they detail the operations and explainability techniques currently available for generating explanations for NLP models . |
| Outcome: | This survey examines the state of explainable AI (XAI) within the domain of natural language processing . it focuses on the operations and explainability techniques currently available for NLP models . |
Interpretability and Analysis in Neural NLP (2020.acl-tutorials)
Copied to clipboard
| Challenge: | a tutorial aims to introduce the nascent field of interpretability and analysis of neural networks in NLP . |
| Approach: | This tutorial will introduce the nascent field of interpretability and analysis of neural networks in NLP. |
| Outcome: | This tutorial will introduce the nascent field of interpretability and analysis of neural networks in NLP. |
From Insights to Actions: The Impact of Interpretability and Analysis Research on NLP (2024.emnlp-main)
Copied to clipboard
| Challenge: | Interpretability and analysis (IA) research is a growing subfield within NLP . a criticism of this work is that it lacks actionable insights and therefore has little impact on NLP. |
| Approach: | They propose to quantify the impact of interpretation and analysis research on NLP . they use citation graphs and a survey to find out what is missing in IA research . |
| Outcome: | The proposed study shows that IA research is well-cited outside of IA and central in the NLP citation graph. |
Towards Intrinsic Interpretability of Large Language Models: A Survey of Design Principles and Architectures (2026.acl-long)
Copied to clipboard
| Challenge: | Existing studies on explainable AI focus on post-hoc explanation methods that interpret trained models through external approximations. |
| Approach: | They propose to categorize existing approaches into five design paradigms: functional transparency, concept alignment, representational decomposability, explicit modularization, and latent sparsity induction. |
| Outcome: | The proposed approaches are categorized into five design paradigms: functional transparency, concept alignment, representational decomposability, explicit modularization, and latent sparsity induction. |
Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challenges (2025.acl-long)
Copied to clipboard
Bolei Ma, Yuting Li, Wei Zhou, Ziwei Gong, Yang Janet Liu, Katja Jasinskaja, Annemarie Friedrich, Julia Hirschberg, Frauke Kreuter, Barbara Plank
| Challenge: | linguistics studies how context influences meaning of language and how people use it to convey implied meanings, emotions, and intentions. |
| Approach: | They analyze task designs, data collection methods, evaluation approaches and their relevance to real-world applications. |
| Outcome: | The findings highlight emerging trends, challenges, and gaps in existing benchmarks . the findings will contribute to more nuanced and context-aware NLP models . |
Neuron-level Interpretation of Deep NLP Models: A Survey (2022.tacl-1)
Copied to clipboard
| Challenge: | Existing work on deep neural networks has focused on representation analysis, but recent work focused on analyzing neurons within these models. |
| Approach: | They propose to analyze neural networks to uncover linguistic concepts captured by the network . they propose to use a granular approach to analyze neurons within these models . |
| Outcome: | The proposed method combines methods to discover and understand neurons in a network with evaluation methods. |
Interpretation Meets Safety: A Survey on Interpretation Methods and Tools for Improving LLM Safety (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing surveys focus on interpretation or safety, but safety and understanding are core motivations for interpretation research. |
| Approach: | They propose a framework that connects interpretation methods, enhancements they inform, and tools that operationalize them. |
| Outcome: | The proposed framework summarizes nearly 70 studies at their intersections and concludes with open challenges and future directions. |
Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness? (2020.acl-main)
Copied to clipboard
| Challenge: | Current approaches to interpretability evaluation focus on faithfulness criteria . current approaches focus on readability, plausibility and faithfulness . |
| Approach: | They argue that current binary definition of faithfulness sets unrealistic standards . they argue that a more graded definition would be of greater practical utility . |
| Outcome: | The proposed approach is based on three assumptions and lacks a graded definition of faithfulness. |
Interpreting Predictions of NLP Models (2020.emnlp-tutorials)
Copied to clipboard
| Challenge: | This tutorial will provide a background on interpretation techniques for neural NLP models. |
| Approach: | This tutorial will provide a background on interpretation techniques for NLP models . it will examine saliency maps, input perturbations, adversarial attacks and influence functions . |
| Outcome: | This tutorial will provide a background on interpretation techniques . examples-specific interpretations include saliency maps, input perturbations, adversarial attacks, influence functions . |