29 citations · 55 across the 27 of their papers we have counts for
30 papers
From Concept Alignment to Causal Grounding: An Intervention Test of Chain-of-Thought Faithfulness
Qianli Wang, Yilong Wang, Dennis Wei +5
Chain-of-thought (CoT) can sound plausible yet be unfaithful to the model's underlying reasoning. Most prior work probes CoT faithfulness through input--output behavior or input at…
Limitations of Automated Simulatability: LLM Simulators Can Bypass Explanations
Antonin Poché, Fanny Jourdan, Nils Feldhus +6
Simulatability is an evaluation protocol for explanations that quantifies their usefulness by how well they help a user predict a task model's outputs. Since human evaluation is co…
Judge Circuits Explain Format-Induced Inconsistency in LLM-as-a-Judge
Nils Feldhus, Tanja Baeumel, Elena Golimblevskaia +10
LLM-as-a-judge has become the dominant paradigm for grading model outputs at scale, yet the same model assigns systematically different scores when its output format changes (e.g.,…
Simplifying Outcomes of Language Model Component Analyses with ELIA
Aaron Louis Eidt, Nils Feldhus
While mechanistic interpretability has developed powerful tools to analyze the internal workings of Large Language Models (LLMs), their complexity has created an accessibility gap,…
Persona Prompting as a Lens on LLM Social Reasoning
Jing Yang, Moritz Hechtbauer, Elisabeth Khalilov +3
For socially sensitive tasks like hate speech detection, the quality of explanations from Large Language Models (LLMs) is crucial for factors like user trust and model alignment. W…
What Are We Measuring in NLG? A Meta-Analysis of Evaluation Trends 2020-2025
Jing Yang, Nils Feldhus, Salar Mohtaj +10
As Natural Language Generation (NLG) dominates modern NLP, scalable evaluation remains a critical bottleneck. Consequently, LLM-as-a-judge (LaaJ) adoption has accelerated rapidly,…
iFlip: Iterative Feedback-driven Counterfactual Example Refinement
Yilong Wang, Qianli Wang, Nils Feldhus
Counterfactual examples are minimal edits to an input that alter a model's prediction. They are widely employed in explainable AI to probe model behavior and in natural language pr…
Can Large Language Models Still Explain Themselves? Investigating the Impact of Quantization on Self-Explanations
Qianli Wang, Nils Feldhus, Pepa Atanasova +4
Quantization is widely used to accelerate inference and streamline the deployment of large language models (LLMs), yet its effects on self-explanations (SEs) remain unexplored. SEs…
Parallel Universes, Parallel Languages: A Comprehensive Study on LLM-based Multilingual Counterfactual Example Generation
Qianli Wang, Van Bach Nguyen, Yihong Liu +6
Counterfactuals refer to minimally edited inputs that cause a model's prediction to change, serving as a promising approach to explaining the model's behavior. Large language model…
Interpreting Language Models Through Concept Descriptions: A Survey
Nils Feldhus, Laura Kopf
Understanding the decision-making processes of neural networks is a central goal of mechanistic interpretability. In the context of Large Language Models (LLMs), this involves unco…
Multilingual Datasets for Custom Input Extraction and Explanation Requests Parsing in Conversational XAI Systems
Qianli Wang, Tatiana Anikina, Nils Feldhus +6
Conversational explainable artificial intelligence (ConvXAI) systems based on large language models (LLMs) have garnered considerable attention for their ability to enhance user co…
Table Understanding and (Multimodal) LLMs: A Cross-Domain Case Study on Scientific vs. Non-Scientific Data
Ekaterina Borisova, Fabio Barth, Nils Feldhus +5
Tables are among the most widely used tools for representing structured data in research, business, medicine, and education. Although LLMs demonstrate strong performance in downstr…
Capturing Polysemanticity with PRISM: A Multi-Concept Feature Description Framework
Laura Kopf, Nils Feldhus, Kirill Bykov +4
Automated interpretability research aims to identify concepts encoded in neural network features to enhance human understanding of model behavior. Within the context of large langu…
Gender Bias in Explainability: Investigating Performance Disparity in Post-hoc Methods
Mahdi Dhaini, Ege Erdogan, Nils Feldhus +1
While research on applications and evaluations of explanation methods continues to expand, fairness of the explanation methods concerning disparities in their performance across su…
Truth or Twist? Optimal Model Selection for Reliable Label Flipping Evaluation in LLM-based Counterfactuals
Qianli Wang, Van Bach Nguyen, Nils Feldhus +4
Counterfactual examples are widely employed to enhance the performance and robustness of large language models (LLMs) through counterfactual data augmentation (CDA). However, the s…
Through a Compressed Lens: Investigating The Impact of Quantization on Factual Knowledge Recall
Qianli Wang, Mingyang Wang, Nils Feldhus +5
Quantization methods are widely used to accelerate inference and streamline the deployment of large language models (LLMs). Although quantization's effects on various LLM capabilit…
Proceedings of the ISCA/ITG Workshop on Diversity in Large Speech and Language Models
Sebastian Möller, Pia Knoeferle, Britta Schulte +1
Machine learning techniques have conquered many different tasks in speech and natural language processing, such as speech recognition, information extraction, text and speech gener…
FitCF: A Framework for Automatic Feature Importance-guided Counterfactual Example Generation
Qianli Wang, Nils Feldhus, Simon Ostermann +3
Counterfactual examples are widely used in natural language processing (NLP) as valuable data to improve models, and in explainable artificial intelligence (XAI) to understand mode…
Cross-Refine: Improving Natural Language Explanation Generation by Learning in Tandem
Qianli Wang, Tatiana Anikina, Nils Feldhus +3
Natural language explanations (NLEs) are vital for elucidating the reasoning behind large language model (LLM) decisions. Many techniques have been developed to generate NLEs using…
Free-text Rationale Generation under Readability Level Control
Yi-Sheng Hsu, Nils Feldhus, Sherzod Hakimov
Free-text rationales justify model decisions in natural language and thus become likable and accessible among approaches to explanation across many tasks. However, their effectiven…
CoXQL: A Dataset for Parsing Explanation Requests in Conversational XAI Systems
Qianli Wang, Tatiana Anikina, Nils Feldhus +2
Conversational explainable artificial intelligence (ConvXAI) systems based on large language models (LLMs) have garnered significant interest from the research community in natural…
LLMCheckup: Conversational Examination of Large Language Models via Interpretability Tools and Self-Explanations
Qianli Wang, Tatiana Anikina, Nils Feldhus +3
Interpretability tools that offer explanations in the form of a dialogue have demonstrated their efficacy in enhancing users' understanding (Slack et al., 2023; Shen et al., 2023),…
InterroLang: Exploring NLP Models and Datasets through Dialogue-based Explanations
Nils Feldhus, Qianli Wang, Tatiana Anikina +3
While recently developed NLP explainability methods let us open the black box in various ways (Madsen et al., 2022), a missing ingredient in this endeavor is an interactive tool of…
Inseq: An Interpretability Toolkit for Sequence Generation Models
Gabriele Sarti, Nils Feldhus, Ludwig Sickert +3
Past work in natural language processing interpretability focused mainly on popular classification tasks while largely overlooking generation settings, partly due to a lack of dedi…