15 papers
The Company You Keep: How LLMs Respond to Dark Triad Traits
Zeyi Lu, Angelica Henestrosa, Pavel Chizhov +1
Large Language Models (LLMs) often exhibit highly agreeable and reinforcing conversational styles, also known as AI-sycophancy. Although this pattern arises from training objective…
Common Corpus: The Largest Collection of Ethical Data for LLM Pre-Training
Pierre-Carl Langlais, Pavel Chizhov, Catherine Arnett +7
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…
Model in Distress: Sentiment Analysis on French Synthetic Social Media
Pierre-Carl Langlais, Pavel Chizhov, Yannick Detrois +3
Automated analysis of customer feedback on social media is hindered by three challenges: the high cost of annotated training data, the scarcity of evaluation sets, especially in mu…
From Where Words Come: Efficient Regularization of Code Tokenizers Through Source Attribution
Pavel Chizhov, Egor Bogomolov, Ivan P. Yamshchikov
Efficiency and safety of Large Language Models (LLMs), among other factors, rely on the quality of tokenization. A good tokenizer not only improves inference speed and language und…
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…