The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models

Challenge:	Existing tools for modeling and understanding models are limited . existing tools can assist practitioners in understanding and evaluating models .
Approach:	They present an open-source platform for visualization and understanding of NLP models.
Outcome:	The language interpretability tool (lit) is an open-source platform for visualization and understanding of NLP models.

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Eric Wallace, Jens Tuyls, Junlin Wang, Sanjay Subramanian, Matt Gardner, Sameer Singh

Explainability & Interpretability in NLP

Challenge:	Existing interpretation codebases make it difficult to apply these methods to new models and tasks.
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Interpretability and Analysis in Neural NLP (2020.acl-tutorials)

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Yonatan Belinkov, Sebastian Gehrmann, Ellie Pavlick

Responsible & Trustworthy NLP

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.
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Towards Intrinsic Interpretability of Large Language Models: A Survey of Design Principles and Architectures (2026.acl-long)

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Yutong Gao, Qinglin Meng, Yuan Zhou, Liangming Pan

Explainability & Interpretability in NLP

Challenge:	Existing studies on explainable AI focus on post-hoc explanation methods that interpret trained models through external approximations.
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Towards Explainable Evaluation of Language Models on the Semantic Similarity of Visual Concepts (2022.coling-1)

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Maria Lymperaiou, George Manoliadis, Orfeas Menis Mastromichalakis, Edmund G. Dervakos, Giorgos Stamou

Semantic Text Processing

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A Survey of the State of Explainable AI for Natural Language Processing (2020.aacl-main)

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Marina Danilevsky, Kun Qian, Ranit Aharonov, Yannis Katsis, Ban Kawas, Prithviraj Sen

Explainability & Interpretability in NLP

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.
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Simplifying Outcomes of Language Model Component Analyses with ELIA (2026.eacl-demo)

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Aaron Louis Eidt, Nils Feldhus

Explainability & Interpretability in NLP

Challenge:	ELIA is an interactive web application that simplifies the outputs of various language model component analyses for a broader audience.
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SHAP-Based Explanation Methods: A Review for NLP Interpretability (2022.coling-1)

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Edoardo Mosca, Ferenc Szigeti, Stella Tragianni, Daniel Gallagher, Georg Groh

Responsible & Trustworthy NLP

Challenge:	Existing models with opacity problems have been proposed to address this problem.
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Proto-lm: A Prototypical Network-Based Framework for Built-in Interpretability in Large Language Models (2023.findings-emnlp)

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Sean Xie, Soroush Vosoughi, Saeed Hassanpour

Explainability & Interpretability in NLP

Challenge:	Existing methods for interpreting LLMs are post hoc and focus on low-level features and lack of explainability at higher-level text units.
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Inseq: An Interpretability Toolkit for Sequence Generation Models (2023.acl-demo)

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Gabriele Sarti, Nils Feldhus, Ludwig Sickert, Oskar van der Wal

Semantic Text Processing

Challenge:	Recent studies focused on classification tasks while largely overlooking generation settings due to a lack of dedicated tools.
Approach:	They propose to use Inseq to democratize access to interpretability analyses of sequence generation models by enabling intuitive extraction of models’ internal information and feature importance scores for popular decoder-only and encoder-decoder Transformers architectures.
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Transparency at the Source: Evaluating and Interpreting Language Models With Access to the True Distribution (2023.findings-emnlp)

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Jaap Jumelet, Willem Zuidema

Explainability & Interpretability in NLP

Challenge:	a new approach to train, evaluate and interpret neural language models uses artificial, language-like data.
Approach:	They propose a setup for training, evaluating and interpreting neural language models that uses artificial, language-like data.
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