44 citations · 111 across the 45 of their papers we have counts for
48 papers
Uncertainty-Aware Generation and Decision-Making Under Ambiguity
Nico Daheim, Iryna Gurevych
With rapidly improving capabilities, Large Language Models (LLMs) are increasingly used in many complex real-world tasks. Beyond requiring in-depth knowledge and reasoning skills,…
Decision-Making with Deliberation: Meta-reviewing as a Document-grounded Dialogue
Sukannya Purkayastha, Nils Dycke, Anne Lauscher +1
Meta-reviewing is a pivotal stage in the peer-review process, serving as the final step in determining whether a paper is recommended for acceptance. Prior research on meta-reviewi…
From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement Learning
David Dinucu-Jianu, Jakub Macina, Nico Daheim +3
Large language models (LLMs) can transform education, but their optimization for direct question-answering often undermines effective pedagogy which requires strategically withhold…
In-depth Research Impact Summarization through Fine-Grained Temporal Citation Analysis
Hiba Arnaout, Noy Sternlicht, Tom Hope +1
Understanding the impact of scientific publications is crucial for identifying breakthroughs and guiding future research. Traditional metrics based on citation counts often miss th…
An Efficient Quantum Classifier Based on Hamiltonian Representations
Federico Tiblias, Anna Schroeder, Yue Zhang +2
Quantum machine learning (QML) is a discipline that seeks to transfer the advantages of quantum computing to data-driven tasks. However, many studies rely on toy datasets or heavy…
LazyReview A Dataset for Uncovering Lazy Thinking in NLP Peer Reviews
Sukannya Purkayastha, Zhuang Li, Anne Lauscher +2
Peer review is a cornerstone of quality control in scientific publishing. With the increasing workload, the unintended use of `quick' heuristics, referred to as lazy thinking, has…
Token Weighting for Long-Range Language Modeling
Falko Helm, Nico Daheim, Iryna Gurevych
Many applications of large language models (LLMs) require long-context understanding, but models continue to struggle with such tasks. We hypothesize that conventional next-token p…
Uncertainty-Aware Decoding with Minimum Bayes Risk
Nico Daheim, Clara Meister, Thomas Möllenhoff +1
Despite their outstanding performance in the majority of scenarios, contemporary language models still occasionally generate undesirable outputs, for example, hallucinated text. Wh…
Aligned Probing: Relating Toxic Behavior and Model Internals
Andreas Waldis, Vagrant Gautam, Anne Lauscher +2
We introduce aligned probing, a novel interpretability framework that aligns the behavior of language models (LMs), based on their outputs, and their internal representations (inte…
GRITHopper: Decomposition-Free Multi-Hop Dense Retrieval
Justus-Jonas Erker, Nils Reimers, Iryna Gurevych
Decomposition-based multi-hop retrieval methods rely on many autoregressive steps to break down complex queries, which breaks end-to-end differentiability and is computationally ex…
MathTutorBench: A Benchmark for Measuring Open-ended Pedagogical Capabilities of LLM Tutors
Jakub Macina, Nico Daheim, Ido Hakimi +3
Evaluating the pedagogical capabilities of AI-based tutoring models is critical for making guided progress in the field. Yet, we lack a reliable, easy-to-use, and simple-to-run eva…
Variational Model Merging for Pareto Front Estimation in Multitask Finetuning
Hugo Monzón Maldonado, Nico Daheim, Thomas Möllenhoff +2
Pareto fronts are useful to find good task-mixing strategies for multitask finetuning, but they are also costly to compute. To reduce costs, recent works have used existing model m…
The Lou Dataset -- Exploring the Impact of Gender-Fair Language in German Text Classification
Andreas Waldis, Joel Birrer, Anne Lauscher +1
Gender-fair language, an evolving German linguistic variation, fosters inclusion by addressing all genders or using neutral forms. Nevertheless, there is a significant lack of reso…
Problem Solving Through Human-AI Preference-Based Cooperation
Subhabrata Dutta, Timo Kaufmann, Goran Glavaš +7
While there is a widespread belief that artificial general intelligence (AGI) -- or even superhuman AI -- is imminent, complex problems in expert domains are far from being solved.…
Overview of PerpectiveArg2024: The First Shared Task on Perspective Argument Retrieval
Neele Falk, Andreas Waldis, Iryna Gurevych
Argument retrieval is the task of finding relevant arguments for a given query. While existing approaches rely solely on the semantic alignment of queries and arguments, this first…
Stepwise Verification and Remediation of Student Reasoning Errors with Large Language Model Tutors
Nico Daheim, Jakub Macina, Manu Kapur +2
Large language models (LLMs) present an opportunity to scale high-quality personalized education to all. A promising approach towards this means is to build dialog tutoring models…
Modular Sentence Encoders: Separating Language Specialization from Cross-Lingual Alignment
Yongxin Huang, Kexin Wang, Goran Glavaš +1
Multilingual sentence encoders (MSEs) are commonly obtained by training multilingual language models to map sentences from different languages into a shared semantic space. As such…
Constrained C-Test Generation via Mixed-Integer Programming
Ji-Ung Lee, Marc E. Pfetsch, Iryna Gurevych
This work proposes a novel method to generate C-Tests; a deviated form of cloze tests (a gap filling exercise) where only the last part of a word is turned into a gap. In contrast…
Holmes: A Benchmark to Assess the Linguistic Competence of Language Models
Andreas Waldis, Yotam Perlitz, Leshem Choshen +2
We introduce Holmes, a new benchmark designed to assess language models (LMs) linguistic competence - their unconscious understanding of linguistic phenomena. Specifically, we use…
Socratic Reasoning Improves Positive Text Rewriting
Anmol Goel, Nico Daheim, Christian Montag +1
Reframing a negative into a positive thought is at the crux of several cognitive approaches to mental health and psychotherapy that could be made more accessible by large language…
Zero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon
Fajri Koto, Tilman Beck, Zeerak Talat +2
Improving multilingual language models capabilities in low-resource languages is generally difficult due to the scarcity of large-scale data in those languages. In this paper, we r…
Dive into the Chasm: Probing the Gap between In- and Cross-Topic Generalization
Andreas Waldis, Yufang Hou, Iryna Gurevych
Pre-trained language models (LMs) perform well in In-Topic setups, where training and testing data come from the same topics. However, they face challenges in Cross-Topic scenarios…
Variational Learning is Effective for Large Deep Networks
Yuesong Shen, Nico Daheim, Bai Cong +8
We give extensive empirical evidence against the common belief that variational learning is ineffective for large neural networks. We show that an optimizer called Improved Variati…
Triple-Encoders: Representations That Fire Together, Wire Together
Justus-Jonas Erker, Florian Mai, Nils Reimers +2
Search-based dialog models typically re-encode the dialog history at every turn, incurring high cost. Curved Contrastive Learning, a representation learning method that encodes rel…