1 citations · 1 across the 14 of their papers we have counts for
15 papers
Ladders in Chaos: When, How, (and Perhaps Why) Does Test-Time Scaling Improve LLM Machine Translation
Di Wu, Sergey Troshin, Christof Monz +2
Two forms of test-time scaling for Large Language Models (LLMs) have emerged as effective and widely adopted paradigms: sequential, in which later answer attempts depend on earlier…
Safety Training Modulates Harmful Misalignment Under On-Policy RL, But Direction Depends on Environment Design
Leon Eshuijs, Shihan Wang, Antske Fokkens
Specification gaming under Reinforcement Learning (RL) is known to cause LLMs to develop sycophantic, manipulative, or deceptive behavior, yet the conditions under which this occur…
Not All Subjectivity Is the Same! Defining Desiderata for the Evaluation of Subjectivity in NLP
Urja Khurana, Michiel van der Meer, Enrico Liscio +2
Subjective judgments are part of several NLP datasets and recent work is increasingly prioritizing models whose outputs reflect this diversity of perspectives. Such responses allow…
Learning from Sufficient Rationales: Analysing the Relationship Between Explanation Faithfulness and Token-level Regularisation Strategies
Jonathan Kamp, Lisa Beinborn, Antske Fokkens
Human explanations of natural language, rationales, form a tool to assess whether models learn a label for the right reasons or rely on dataset-specific shortcuts. Sufficiency is a…
Asking a Language Model for Diverse Responses
Sergey Troshin, Irina Saparina, Antske Fokkens +1
Large language models increasingly rely on explicit reasoning chains and can produce multiple plausible responses for a given context. We study the candidate sampler that produces…
Control the Temperature: Selective Sampling for Diverse and High-Quality LLM Outputs
Sergey Troshin, Wafaa Mohammed, Yan Meng +3
Diversity is an essential metric for evaluating the creativity of outputs generated by language models. Temperature-based sampling is a common strategy to increase diversity. Howev…