Papers by Vésteinn Snæbjarnarson
Activation Scaling for Steering and Interpreting Language Models (2024.findings-emnlp)
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| Challenge: | a successful intervention should flip the correct with the wrong token, while remaining sparse. |
| Approach: | They propose to use activation scaling to flip the correct with the wrong token . they use gradient-based optimization to learn and evaluate a specific kind of efficient intervention . |
| Outcome: | The proposed method performs comparable with steering vectors but is much less minimal. |
Context versus Prior Knowledge in Language Models (2024.acl-long)
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| Challenge: | Existing studies have investigated how often a model will rely on prior knowledge over conflicting contextual information in answering questions. |
| Approach: | They propose two mutual information-based metrics to measure a model’s dependency on a context and on its prior about an entity. |
| Outcome: | The proposed metrics show that language models can integrate prior knowledge and new information in a predictable way across different questions and contexts. |
A Warm Start and a Clean Crawled Corpus - A Recipe for Good Language Models (2022.lrec-1)
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Vésteinn Snæbjarnarson, Haukur Barri Símonarson, Pétur Orri Ragnarsson, Svanhvít Lilja Ingólfsdóttir, Haukur Jónsson, Vilhjalmur Thorsteinsson, Hafsteinn Einarsson
| Challenge: | Pre-trained neural language models have shown impressive results when adapted for a variety of classification and text generation tasks. |
| Approach: | They propose to use Icelandic's Icelandic Common Crawl Corpus to train language models that achieve state-of-the-art performance in downstream tasks. |
| Outcome: | The proposed models achieve state-of-the-art in a variety of downstream tasks including part-of speech tagging, named entity recognition and constituency parsing. |
Byte-Level Grammatical Error Correction Using Synthetic and Curated Corpora (2023.acl-long)
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Svanhvít Lilja Ingólfsdóttir, Petur Ragnarsson, Haukur Jónsson, Haukur Simonarson, Vilhjalmur Thorsteinsson, Vésteinn Snæbjarnarson
| Challenge: | Spelling mistakes due to typos and rushed writing, nonstandard punctuation and spelling, and grammatical and stylistic issues are common to almost everyone who writes any kind of text. |
| Approach: | They propose to use a common subword unit vocabulary and byte-level encoding to fine tune two subword-level models and one byte level model on hand-corrected error corpora. |
| Outcome: | The proposed model improves accuracy for spelling and grammatical errors and more complex errors. |
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. |
Natural Questions in Icelandic (2022.lrec-1)
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| Challenge: | Developing such datasets is important for the development and evaluation of Icelandic QA systems. |
| Approach: | They present the first extractive question answering dataset for Icelandic, Natural Questions in Icelandic. |
| Outcome: | The proposed dataset is a valuable resource for Icelandic which is being evaluated by a team of researchers. |