publications

2026

2026

1. Where does output diversity collapse in post-training?

   Constantinos Karouzos, Xingwei Tan, and Nikolaos Aletras

   Apr 2026

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   Post-trained language models produce less varied outputs than their base counterparts. This output diversity collapse undermines inference-time scaling methods that rely on varied samples, and risks homogenizing model outputs on creative and value-laden tasks. Prior work attributes collapse to specific post-training methods, without separating the role of training data composition from the method, or the generation format from the model weights. We trace output diversity through three parallel post-training lineages of Olmo 3, Think (chain-of-thought distillation), Instruct (broad multi-source data), and RL-Zero, across 15 tasks and four text diversity metrics. We find that the location of collapse co-varies with data composition: the Think lineage loses most semantic diversity at supervised fine-tuning, and the effect of DPO is larger in Instruct than in Think. Suppressing chain-of-thought reasoning at inference in Think models drops accuracy on hard tasks, yet leaves answer-level diversity unchanged, showing that the collapse is embedded in the model weights by training data, not imposed by the generation format. Decomposing diversity loss on six verifiable tasks into a quality-control component (removal of incorrect outputs) and a residual component (genuine narrowing among correct outputs) reveals that the split is task-dependent, and Think models retain more correct-answer diversity than Instruct despite collapsing more in aggregate. Our results indicate that diversity collapse is determined during training by data composition and cannot be addressed at inference time alone.

2. An Empirical Study on Preference Tuning Generalization and Diversity Under Domain Shift

   Constantinos Karouzos, Xingwei Tan, and Nikolaos Aletras

   Jan 2026

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   Preference tuning aligns pretrained language models to human judgments of quality, helpfulness, or safety by optimizing over explicit preference signals rather than likelihood alone. Prior work has shown that preference-tuning degrades performance and reduces helpfulness when evaluated outside the training domain. However, the extent to which adaptation strategies mitigate this domain shift remains unexplored. We address this challenge by conducting a comprehensive and systematic study of alignment generalization under domain shift. We compare five popular alignment objectives and various adaptation strategies from source to target, including target-domain supervised fine-tuning and pseudo-labeling, across summarization and question-answering helpfulness tasks. Our findings reveal systematic differences in generalization across alignment objectives under domain shift. We show that adaptation strategies based on pseudo-labeling can substantially reduce domain-shift degradation.

2022

2022

1. ACII 2022

   Audio and ASR-based Filled Pause Detection

   Aggelina Chatziagapi, Dimitris Sgouropoulos, Constantinos Karouzos, and 4 more authors

   Oct 2022

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   Filled pauses (or fillers) are the most common form of speech disfluencies and they can be recognized as hesitation markers ("um", "uh" and "er") made by speakers, usually to gain extra time while thinking their next words. Filled pauses are very frequent in spontaneous speech. Their detection is therefore rather important for two basic reasons: (a) their existence influences the performance of individual components, like Automatic Speech Recognition system (ASR), in human-machine interaction and (b) their frequency can characterize the overall speech quality of a particular speaker, as it can be strongly associated with the speaker’s confidence. Despite that, only limited work has been published for the detection of filled pauses in speech, especially through audio. In this work, we propose a framework for filled pause detection using both audio and textual information. For the audio modality, we transfer knowledge from a plethora of supervised tasks, such as emotion or speaking rate, using Convolutional Neural Networks (CNNs). For the text modality, we develop a temporal Recurrent Neural Network (RNN) method that takes into account textual information derived from an ASR system. In addition, the proposed transfer learning approach for the audio classifier leads to better results when benchmarked on our internal dataset for which the text is not transcribed but estimated by an ASR system. In this case, a simple late fusion approach boosts the performance even further. This proves that the audio approach is suitable for real-world applications where the transcribed text is not available and has to leverage imperfect ASR results, or even the absence of textual information (to reduce computational cost).

2021

2021

1. NAACL 2021

   UDALM: Unsupervised Domain Adaptation through Language Modeling

   Constantinos Karouzos, Georgios Paraskevopoulos, and Alexandros Potamianos

   Jun 2021

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   In this work we explore Unsupervised Domain Adaptation (UDA) of pretrained language models for downstream tasks. We introduce UDALM, a fine-tuning procedure, using a mixed classification and Masked Language Model loss, that can adapt to the target domain distribution in a robust and sample efficient manner. Our experiments show that performance of models trained with the mixed loss scales with the amount of available target data and the mixed loss can be effectively used as a stopping criterion during UDA training. Furthermore, we discuss the relationship between A-distance and the target error and explore some limitations of the Domain Adversarial Training approach. Our method is evaluated on twelve domain pairs of the Amazon Reviews Sentiment dataset, yielding 91.74% accuracy, which is an 1.11% absolute improvement over the state-of-the-art.