LLMs have the tendency to hallucinate, i.e., to sporadically generate false or fabricated information, and users generally lack the tools to detect when this happens. Uncertainty quantification (UQ) provides a framework for assessing the reliability of model outputs, aiding in the identification of potential hallucinations. In this work, we introduce pre-trained UQ heads: supervised auxiliary modules for LLMs that substantially enhance their ability to capture uncertainty compared to unsupervised UQ methods. Their strong performance stems from the transformer architecture in their design, in the form of informative features derived from LLM attention maps and logits. Our experiments show that these heads are highly robust and achieve state-of-the-art performance in claim-level hallucination detection across both in-domain and out-of-domain prompts. Moreover, these modules demonstrate strong generalization to languages they were not explicitly trained on. We pre-train a collection of UQ heads for popular LLM series, including Mistral, Llama, and Gemma. We publicly release both the code and the pre-trained heads.

There has been a surge in the development of various Large Language Models (LLMs). However, text generation for languages other than English often faces significant challenges, including poor generation quality and reduced computational performance due to the disproportionate representation of tokens in the model’s vocabulary. In this work, we address these issues by developing a pipeline for adaptation of English-oriented pre-trained models to other languages and constructing efficient bilingual LLMs. Using this pipeline, we construct Vikhr, a state-of-the-art bilingual open-source instruction-following LLM designed specifically for the Russian language. “Vikhr” refers to the name of the Mistral LLM series and means a “strong gust of wind.”Unlike previous Russian-language models that typically rely on LoRA adapters on top of English-oriented models, sacrificing performance for lower training costs, Vikhr features an adapted tokenizer vocabulary and undergoes the continued pre-training and instruction tuning of all weights. This not only enhances the model’s performance but also significantly improves its computational and contextual efficiency.The remarkable performance of Vikhr across various Russian-language benchmarks can also be attributed to our efforts in expanding instruction datasets and corpora for continued pre-training. Vikhr not only sets the new state of the art among open-source LLMs for Russian but even outperforms some proprietary closed-source models on certain benchmarks. The model weights, instruction sets, and code are publicly available.