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Found 16 papers, 9 papers with code
A Study on Manual and Automatic Evaluation for Text Style Transfer: The Case of Detoxification
It is often difficult to reliably evaluate models which generate text.
The Russian-focused embedders' exploration: ruMTEB benchmark and Russian embedding model design
Embedding models play a crucial role in Natural Language Processing (NLP) by creating text embeddings used in various tasks such as information retrieval and assessing semantic text similarity.
RuBLiMP: Russian Benchmark of Linguistic Minimal Pairs
Minimal pairs are a well-established approach to evaluating the grammatical knowledge of language models.
MERA: A Comprehensive LLM Evaluation in Russian
To address these issues, we introduce an open Multimodal Evaluation of Russian-language Architectures (MERA), a new instruction benchmark for evaluating foundation models oriented towards the Russian language.
A Family of Pretrained Transformer Language Models for Russian
Transformer language models (LMs) are fundamental to NLP research methodologies and applications in various languages.
A Methodology for Generative Spelling Correction via Natural Spelling Errors Emulation across Multiple Domains and Languages
Our research mainly focuses on exploring natural spelling errors and mistypings in texts and studying the ways those errors can be emulated in correct sentences to effectively enrich generative models' pre-train procedure.
TAPE: Assessing Few-shot Russian Language Understanding
Recent advances in zero-shot and few-shot learning have shown promise for a scope of research and practical purposes.
Ranked #1 on
Ethics
on Ethics (per ethics)
Findings of the The RuATD Shared Task 2022 on Artificial Text Detection in Russian
The first task is framed as a binary classification problem.
mGPT: Few-Shot Learners Go Multilingual
Recent studies report that autoregressive language models can successfully solve many NLP tasks via zero- and few-shot learning paradigms, which opens up new possibilities for using the pre-trained language models.
Ranked #1 on
Natural Language Inference
on XWINO
Cross-Lingual Natural Language Inference
Cross-Lingual Paraphrase Identification
+5
RuCLIP -- new models and experiments: a technical report
In the report we propose six new implementations of ruCLIP model trained on our 240M pairs.
Russian SuperGLUE 1.1: Revising the Lessons not Learned by Russian NLP models
In the last year, new neural architectures and multilingual pre-trained models have been released for Russian, which led to performance evaluation problems across a range of language understanding tasks.
Russian Paraphrasers: Paraphrase with Transformers
This paper studies the generation methods for paraphrasing in the Russian language.
Read and Reason with MuSeRC and RuCoS: Datasets for Machine Reading Comprehension for Russian
The paper introduces two Russian machine reading comprehension (MRC) datasets, called MuSeRC and RuCoS, which require reasoning over multiple sentences and commonsense knowledge to infer the answer.
RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark
In this paper, we introduce an advanced Russian general language understanding evaluation benchmark -- RussianGLUE.
Ranked #1 on
Word Sense Disambiguation
on RUSSE
DaNetQA: a yes/no Question Answering Dataset for the Russian Language
The task is to take both the question and a paragraph as input and come up with a yes/no answer, i. e. to produce a binary output.
Humans Keep It One Hundred: an Overview of AI Journey
Artificial General Intelligence (AGI) is showing growing performance in numerous applications - beating human performance in Chess and Go, using knowledge bases and text sources to answer questions (SQuAD) and even pass human examination (Aristo project).
