| Challenge: | Large language models (LLMs) are increasingly used as reasoning engines in autonomous driving, yet their decision-making remains opaque. |
| Approach: | They propose to use gradient-based optimization on continuous embeddings to identify the input shifts required to flip a model’s decision. |
| Outcome: | The proposed method exposes latent biases and provides concrete insights to improve the robustness of LLM-based driving agents. |
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Can LLMs Explain Themselves Counterfactually? (2025.emnlp-main)
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| Challenge: | Explanations are an important tool for gaining insights into model behavior, calibrating user trust, and ensuring compliance. |
| Approach: | They propose to use self-explanation to prompt models to explain outputs . they find that LLMs struggle to generate SCEs - their prediction often does not agree with their own counterfactual reasoning. |
| Outcome: | The proposed methods can generate SCEs across families, sizes, temperatures, and datasets. |
LLMs Don’t Know Their Own Decision Boundaries: The Unreliability of Self-Generated Counterfactual Explanations (2025.emnlp-main)
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Harry Mayne, Ryan Othniel Kearns, Yushi Yang, Andrew M. Bean, Eoin D. Delaney, Chris Russell, Adam Mahdi
| Challenge: | Existing studies on language models' ability to explain their decisions in natural language have focused on self-generated counterfactual explanations (SCEs). |
| Approach: | They evaluate whether LLMs can generate valid counterfactuals and minimal ones . authors suggest that SCEs are, at best, an ineffective explainability tool . |
| Outcome: | The proposed language models can explain their decisions in natural language, the study finds . the models can produce valid counterfactual explanations, but make small edits that fail to change predictions. |
Parallel Universes, Parallel Languages: A Comprehensive Study on LLM-based Multilingual Counterfactual Example Generation (2026.acl-long)
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Qianli Wang, Van Bach Nguyen, Yihong Liu, Fedor Splitt, Nils Feldhus, Christin Seifert, Hinrich Schuetze, Sebastian Möller, Vera Schmitt
| Challenge: | Large language models excel at generating English counterfactuals but their effectiveness in generating multilingual counterfacts remains unclear. |
| Approach: | They conduct automatic evaluations on both directly generated and derived counterfactuals in six languages and find that cross-lingual perturbations follow common strategic principles. |
| Outcome: | The proposed models show that translation-based counterfactuals offer higher validity than their directly generated counterparts, but still fall short of matching the quality of the original English counterf actuals. |
LLMs for Generating and Evaluating Counterfactuals: A Comprehensive Study (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have shown remarkable performance in NLP tasks, but their efficacy in generating high-quality CFs remains uncertain. |
| Approach: | They compare LLMs' ability to generate CFs that flip the original label and human CF's. |
| Outcome: | The proposed models generate fluent CFs, but struggle to keep the induced changes minimal. |
CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and Calibration (2024.eacl-long)
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| Challenge: | Large language models (LLMs) have shown remarkable generalization capabilities, performing well on various tasks such as question answering (QA), complex reasoning, and code generation. |
| Approach: | They propose to augment training data of smaller language models with automatically generated counterfactuals (CF) instances to improve out-of-domain (OOD) performance of SLMs in extractive question answering setup. |
| Outcome: | The proposed approach improves out-of-domain (OOD) performance of small language models in extractive question answering setup. |
Dually Self-Improved Counterfactual Data Augmentation Using Large Language Model (2025.acl-long)
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| Challenge: | Existing approaches to generate counterfactual data augmentation are limited due to imbalance and biases in real-world training data. |
| Approach: | They propose a self-improved method for generating high-quality counterfacts using large language models. |
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Counterfactuals of Counterfactuals: a back-translation-inspired approach to analyse counterfactual editors (2023.findings-acl)
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George Filandrianos, Edmund Dervakos, Orfeas Menis Mastromichalakis, Chrysoula Zerva, Giorgos Stamou
| Challenge: | Existing explanations for classifiers are counterfactual or contrastive . lack of universal ground truth for counterf actual edits hinders their evaluation . |
| Approach: | They propose a back translation-inspired evaluation methodology that utilises earlier outputs of the explainer as ground truth proxies to investigate the consistency of explainers. |
| Outcome: | The proposed method can provide valuable insights into the behaviour of predictor and explainer models and infer patterns that would otherwise be obscured. |
DoCoGen: Domain Counterfactual Generation for Low Resource Domain Adaptation (2022.acl-long)
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| Challenge: | Existing domain adaptation (DA) algorithms are not able to handle out-of-distribution examples due to the costly and labor-intensive data labeling process. |
| Approach: | They propose a controllable generation approach to deal with domain adaptation challenge by generating a domain-counterfactual textual example from an input text. |
| Outcome: | The proposed approach outperforms baselines and improves accuracy of state-of-the-art unsupervised DA algorithm. |
SAGE: An Agentic Explainer Framework for Interpreting SAE Features in Language Models (2026.eacl-industry)
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| Challenge: | Large language models (LLMs) have achieved remarkable progress, yet their internal mechanisms remain largely opaque. |
| Approach: | They propose an agent-based framework that recasts feature interpretation from a passive, single-pass generation task into an explanation-driven process. |
| Outcome: | The proposed framework produces explanations with significantly higher generative and predictive accuracy compared to state-of-the-art baselines. |
Shifting Perspectives: Steering Vectors for Robust Bias Mitigation in LLMs (2026.findings-eacl)
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| Challenge: | Despite efforts to mitigate social bias in large language models, representational harms such as stereotyping continue to exist in both open and closed-source models. |
| Approach: | They propose a method to modify model activations in forward passes by applying steering vectors to a BBQ dataset and comparing their results to bias mitigation methods. |
| Outcome: | The proposed method outperforms 3 other bias mitigation methods on the BBQ dataset and shows the lowest impact on MMLU scores. |