Papers by Samuel Kiegeland
Reverse-Engineering the Reader (2024.emnlp-main)
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| Challenge: | Existing studies have sought to determine to what extent language models can serve as useful models of human cognition by aligning them to human psychometric data. |
| Approach: | They propose a method to fine-tune a language model to implicitly optimize parameters of a linear regressor that directly predicts humans’ reading times of in-context linguistic units. |
| Outcome: | The proposed technique improves language models’ psychometric predictive power but also its perplexity on held-out test data. |
The Harmonic Structure of Information Contours (2025.acl-long)
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Eleftheria Tsipidi, Samuel Kiegeland, Franz Nowak, Tianyang Xu, Ethan Wilcox, Alex Warstadt, Ryan Cotterell, Mario Giulianelli
| Challenge: | Language typically does not maintain a uniform information rate, but it fluctuates around a global average . a new study suggests periodicity may be a factor in information rate oscillations . |
| Approach: | They propose a hypothesis that language does not maintain a uniform information rate . they apply harmonic regression and introduce a new extension to detect periodicity . |
| Outcome: | The proposed method reveals that language oscillates at periodic intervals across frequencies . it also offers a framework for uncovering structural pressures at various levels of linguistic granularity. |
On the Proper Treatment of Units in Surprisal Theory (2026.acl-long)
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| Challenge: | empirical work often leaves the notion of a unit underspecified . empirical work has sought to characterize the processing difficulty comprehenders experience . |
| Approach: | They propose a framework for reasoning about surprisal over arbitrary unit inventories . they argue that surprises should be explicit and treat tokenization as implementation detail . |
| Outcome: | The proposed framework disentangles the models' definitions and the regions of interest and treats tokenization as an implementation detail rather than a scientific primitive. |
Probing for Reading Times (2026.acl-long)
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Eleftheria Tsipidi, Samuel Kiegeland, Francesco Ignazio Re, Tianyang Xu, Mario Giulianelli, Karolina Stanczak, Ryan Cotterell
| Challenge: | a large body of work on probing has demonstrated that language model representations encode a wealth of linguistic information, but it remains unclear whether they also capture cognitive signals about human processing. |
| Approach: | They use regularized linear regression to compare language model representations against scalar predictors. |
| Outcome: | The representations from early layers outperform surprisal in predicting early-pass measures such as first fixation and gaze duration. |
Revisiting the Weaknesses of Reinforcement Learning for Neural Machine Translation (2021.naacl-main)
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| Challenge: | In neural sequence-to-sequence learning, Reinforcement Learning (RL) has gained popularity due to the suitability of Policy Gradient (PG) methods for the end-to end training paradigm. |
| Approach: | They propose to let the model explore the output space beyond the reference output that is used for standard cross-entropy minimization by reinforcing model outputs according to their quality, effectively increasing the likelihood of higher-quality samples. |
| Outcome: | The proposed model explores the output space beyond the reference output that is used for cross-entropy minimization, increasing the likelihood of higher-quality samples. |