Natural language processing

Language is one of the key forms of communication. We study methods of language representation and understanding to simplify human-computer interactions.

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Posts

Publications

  • SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

    Large-scale machine learningNatural language processing
    Tim Dettmers
    Ruslan Svirschevski
    Vage Egiazarian
    Denis Kuznedelev
    Elias Frantar
    Saleh Ashkboos
    Alexander Borzunov
    Torsten Hoefler
    Dan Alistarh
    ICLR, 2024

    Recent advances in large language model (LLM) pretraining have led to high-quality LLMs with impressive abilities. By compressing such LLMs via quantization to 3-4 bits per parameter, they can fit into memory-limited devices such as laptops and mobile phones, enabling personalized use. Quantizing models to 3-4 bits per parameter can lead to moderate to high accuracy losses, especially for smaller models (1-10B parameters), which are suitable for edge deployment. To address this accuracy issue, we introduce the Sparse-Quantized Representation (SpQR), a new compressed format and quantization technique that enables for the first time near-lossless compression of LLMs across model scales while reaching similar compression levels to previous methods. SpQR works by identifying and isolating outlier weights, which cause particularly large quantization errors, and storing them in higher precision while compressing all other weights to 3-4 bits, and achieves relative accuracy losses of less than 1 in perplexity for highly-accurate LLaMA and Falcon LLMs. This makes it possible to run a 33B parameter LLM on a single 24 GB consumer GPU without performance degradation at 15% speedup, thus making powerful LLMs available to consumers without any downsides. SpQR comes with efficient algorithms for both encoding weights into its format, as well as decoding them efficiently at runtime. Specifically, we provide an efficient GPU inference algorithm for SpQR, which yields faster inference than 16-bit baselines at similar accuracy while enabling memory compression gains of more than 4x.

  • Distributed Inference and Fine-tuning of Large Language Models Over The Internet

    Large-scale machine learningNatural language processing
    Alexander Borzunov
    Max Ryabinin
    Artem Chumachenko
    Dmitry Baranchuk
    Tim Dettmers
    Younes Belkada
    Pavel Samygin
    Colin Raffel
    NeurIPS, 2023

    Large language models (LLMs) are useful in many NLP tasks and become more capable with size, with the best open-source models having over 50 billion parameters. However, using these 50B+ models requires high-end hardware, making them inaccessible to most researchers. In this work, we investigate methods for cost-efficient inference and fine-tuning of LLMs, comparing local and distributed strategies. We observe that a large enough model (50B+) can run efficiently even on geodistributed devices in a consumer-grade network. This could allow running LLM efficiently by pooling together idle compute resources of multiple research groups and volunteers. We address two open problems: (1) how to perform inference and fine-tuning reliably if any device can disconnect abruptly and (2) how to partition LLMs between devices with uneven hardware, joining and leaving at will. In order to do that, we develop special fault-tolerant inference algorithms and load-balancing protocols that automatically assign devices to maximize the total system throughput. We showcase these algorithms in Petals — a decentralized system that runs Llama 2 (70B) and BLOOM (176B) over the Internet up to 10х faster than offloading for interactive generation. We evaluate the performance of our system in simulated conditions and a real-world setup spanning two continents.

  • CrowdSpeech and Vox DIY: Benchmark Dataset for Crowdsourced Audio Transcription

    Natural language processing Speech processing
    Nikita Pavlichenko
    Ivan Stelmakh
    Dmitry Ustalov
    NeurIPS Benchmarks, 2021

    Domain-specific data is the crux of the successful transfer of machine learning systems from benchmarks to real life. In simple problems such as image classification, crowdsourcing has become one of the standard tools for cheap and time-efficient data collection: thanks in large part to advances in research on aggregation methods. However, the applicability of crowdsourcing to more complex tasks (e.g., speech recognition) remains limited due to the lack of principled aggregation methods for these modalities. The main obstacle towards designing aggregation methods for more advanced applications is the absence of training data, and in this work, we focus on bridging this gap in speech recognition. For this, we collect and release CROWDSPEECH — the first publicly available large-scale dataset of crowdsourced audio transcriptions. Evaluation of existing and novel aggregation methods on our data shows room for improvement, suggesting that our work may entail the design of better algorithms. At a higher level, we also contribute to the more general challenge of developing the methodology for reliable data collection via crowdsourcing. In that, we design a principled pipeline for constructing datasets of crowdsourced audio transcriptions in any novel domain. We show its applicability on an under-resourced language by constructing VOXDIY — a counterpart of CROWDSPEECH for the Russian language. We also release the code that allows a full replication of our data collection pipeline and share various insights on best practices of data collection via crowdsourcing.

Datasets

  • Shifts Dataset

    Distributional shiftUncertainty estimation Tabular dataMachine translationNatural language processing
    Andrey Malinin
    Neil Band
    Yarin Gal
    Mark J. F. Gales
    Alexander Ganshin
    German Chesnokov
    Alexey Noskov
    Andrey Ploskonosov
    Liudmila Prokhorenkova
    Ivan Provilkov
    Vatsal Raina
    Vyas Raina
    Denis Roginskiy
    Mariya Shmatova
    Panos Tigas
    Boris Yangel

    The Shifts Dataset contains curated and labeled examples of real, 'in-the-wild' distributional shifts across three large-scale tasks. Specifically, it contains tabular weather prediction, machine translation, and vehicle motion prediction tasks' data used in Shifts Challenge 2021. Dataset shift is ubiquitous in all of these tasks and modalities.

  • Text-to-Image dataset for billion-scale similarity search

    Nearest neighbor searchNatural language processing Computer vision
    Dmitry Baranchuk
    Artem Babenko

    Yandex Text-to-Image (T2I) dataset is collected to foster the research in billion-scale nearest neighbor search (NNS) when query distribution differs from the indexing one. In particular, this dataset addresses the cross-domain setting: a user specifies a textual query and requests the search engine to retrieve the most relevant images to the query. Notably, current large-scale indexing methods perform poorly in this setting. Therefore, novel highly-performant indexing solutions robust to out-of-domain queries are in high demand.

    The dataset represents a snapshot of the Yandex visual search engine and contains 1 billion 200-dimensional image embeddings for indexing. The image embeddings are produced by the Se-ResNext-101 model. The embeddings for textual queries are extracted by a variant of the DSSM model.

    Read more about the data format and how to download the dataset in the related post.