29 citations · 55 across the 27 of their papers we have counts for
9 papers · 1 filter
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…