18 citations · 29 across the 13 of their papers we have counts for
13 papers
Not All Explanations Simulate Equally: Comparing Verbalized Feature Attributions and Self-Generated Rationales
Pingjun Hong, Benjamin Roth
Natural-language explanations are often treated as a unified interface for understanding model behavior, but different explanation sources may support simulation in different ways.…
Human Label Variation as Stable Signal: Learning Annotator-Specific Explanation Behavior via Cross-Annotator Preference Optimization
Beiduo Chen, Pingjun Hong, Ziyun Zhang +3
Free-text explanations extend human label variation (HLV) beyond label disagreement by revealing the reasoning and preferences behind annotators' decisions. We study whether large…
Explaining Generalization of AI-Generated Text Detectors Through Linguistic Analysis
Yuxi Xia, Kinga Stańczak, Benjamin Roth
AI-text detectors achieve high accuracy on in-domain benchmarks, but often struggle to generalize across different generation conditions such as unseen prompts, model families, or…
Persistent Personas? Role-Playing, Instruction Following, and Safety in Extended Interactions
Pedro Henrique Luz de Araujo, Michael A. Hedderich, Ali Modarressi +2
Persona-assigned large language models (LLMs) are used in domains such as education, healthcare, and sociodemographic simulation. Yet, they are typically evaluated only in short, s…
Calibration Is Not Enough: Evaluating Confidence Estimation Under Language Variations
Yuxi Xia, Dennis Ulmer, Terra Blevins +3
Confidence estimation (CE) indicates how reliable the answers of large language models are and impacts user trust and decision-making. Existing evaluations mainly concern the align…
Principled Personas: Defining and Measuring the Intended Effects of Persona Prompting on Task Performance
Pedro Henrique Luz de Araujo, Paul Röttger, Dirk Hovy +1
Expert persona prompting -- assigning roles such as expert in math to language models -- is widely used for task improvement. However, prior work shows mixed results on its effecti…
Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles
Yuxi Xia, Pedro Henrique Luz de Araujo, Klim Zaporojets +1
Calibration, the alignment between model confidence and prediction accuracy, is critical for the reliable deployment of large language models (LLMs). Existing works neglect to meas…
An Evaluation of Explanation Methods for Black-Box Detectors of Machine-Generated Text
Loris Schoenegger, Yuxi Xia, Benjamin Roth
The increasing difficulty to distinguish language-model-generated from human-written text has led to the development of detectors of machine-generated text (MGT). However, in many…
Black-box Model Ensembling for Textual and Visual Question Answering via Information Fusion
Yuxi Xia, Kilm Zaporojets, Benjamin Roth
A diverse range of large language models (LLMs), e.g., ChatGPT, and visual question answering (VQA) models, e.g., BLIP, have been developed for solving textual and visual question…
Helpful assistant or fruitful facilitator? Investigating how personas affect language model behavior
Pedro Henrique Luz de Araujo, Benjamin Roth
One way to personalize and steer generations from large language models (LLM) is to assign a persona: a role that describes how the user expects the LLM to behave (e.g., a helpful…
Exploring prompts to elicit memorization in masked language model-based named entity recognition
Yuxi Xia, Anastasiia Sedova, Pedro Henrique Luz de Araujo +3
Training data memorization in language models impacts model capability (generalization) and safety (privacy risk). This paper focuses on analyzing prompts' impact on detecting the…
Specification Overfitting in Artificial Intelligence
Benjamin Roth, Pedro Henrique Luz de Araujo, Yuxi Xia +2
Machine learning (ML) and artificial intelligence (AI) approaches are often criticized for their inherent bias and for their lack of control, accountability, and transparency. Cons…
Functionality learning through specification instructions
Pedro Henrique Luz de Araujo, Benjamin Roth
Test suites assess natural language processing models' performance on specific functionalities: cases of interest involving model robustness, fairness, or particular linguistic cap…