18 citations · 54 across the 33 of their papers we have counts for
11 papers · 1 filter
Teaching Old Tokenizers New Words: Efficient Tokenizer Adaptation for Pre-trained Models
Taido Purason, Pavel Chizhov, Ivan P. Yamshchikov +1
Tokenizer adaptation plays an important role in adapting pre-trained language models to new domains or languages. In this work, we address two complementary aspects of this process…
app.build: A Production Framework for Scaling Agentic Prompt-to-App Generation with Environment Scaffolding
Evgenii Kniazev, Arseny Kravchenko, Igor Rekun +5
We present app.build (https://github.com/neondatabase/appdotbuild-agent), an open-source framework that improves LLM-based application generation through systematic validation and…
ComicScene154: A Scene Dataset for Comic Analysis
Sandro Paval, Ivan P. Yamshchikov, Pascal Meißner
Comics offer a compelling yet under-explored domain for computational narrative analysis, combining text and imagery in ways distinct from purely textual or audiovisual media. We i…
Better Call Claude: Can LLMs Detect Changes of Writing Style?
Johannes Römisch, Svetlana Gorovaia, Mariia Halchynska +2
This article explores the zero-shot performance of state-of-the-art large language models (LLMs) on one of the most challenging tasks in authorship analysis: sentence-level style c…
Team "better_call_claude": Style Change Detection using a Sequential Sentence Pair Classifier
Gleb Schmidt, Johannes Römisch, Mariia Halchynska +2
Style change detection - identifying the points in a document where writing style shifts - remains one of the most important and challenging problems in computational authorship an…
Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices
Luís Cruz, João Paulo Fernandes, Maja H. Kirkeby +22
The environmental impact of Artificial Intelligence (AI)-enabled systems is increasing rapidly, and software engineering plays a critical role in developing sustainable solutions.…
Surface Fairness, Deep Bias: A Comparative Study of Bias in Language Models
Aleksandra Sorokovikova, Pavel Chizhov, Iuliia Eremenko +1
Modern language models are trained on large amounts of data. These data inevitably include controversial and stereotypical content, which contains all sorts of biases related to ge…
Common Corpus: The Largest Collection of Ethical Data for LLM Pre-Training
Pierre-Carl Langlais, Pavel Chizhov, Catherine Arnett +8
Large Language Models (LLMs) are pre-trained on large amounts of data from different sources and domains. Such datasets often contain trillions of tokens, including large portions…
Transfer of Structural Knowledge from Synthetic Languages
Mikhail Budnikov, Ivan Yamshchikov
This work explores transfer learning from several synthetic languages to English. We investigate the structure of the embeddings in the fine-tuned models, the information they cont…
Even Small Reasoners Should Quote Their Sources: Introducing the Pleias-RAG Model Family
Pierre-Carl Langlais, Pavel Chizhov, Mattia Nee +7
We introduce a new generation of small reasoning models for RAG, search, and source summarization. Pleias-RAG-350m and Pleias-RAG-1B are mid-trained on a large synthetic dataset em…
Who Benchmarks the Benchmarks? Towards Comprehensive Evaluation of Commonsense Reasoning Benchmarks
Pavel Chizhov, Mattia Nee, Anton Changalidis +4
Commonsense reasoning is a key language model capability, as it is purportedly a prerequisite for many basic tasks, unlike specific factual knowledge. It is often measured with mul…