44 citations · 111 across the 45 of their papers we have counts for
10 papers · 1 filter
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…