7 citations · 8 across the 7 of their papers we have counts for
8 papers · 1 filter
Toolken+: Improving LLM Tool Usage with Reranking and a Reject Option
Konstantin Yakovlev, Sergey Nikolenko, Andrey Bout
The recently proposed ToolkenGPT tool learning paradigm demonstrates promising performance but suffers from two major issues: first, it cannot benefit from tool documentation, and…
Efficient Grammatical Error Correction Via Multi-Task Training and Optimized Training Schedule
Andrey Bout, Alexander Podolskiy, Sergey Nikolenko +1
Progress in neural grammatical error correction (GEC) is hindered by the lack of annotated training data. Sufficient amounts of high-quality manually annotated data are not availab…
GEC-DePenD: Non-Autoregressive Grammatical Error Correction with Decoupled Permutation and Decoding
Konstantin Yakovlev, Alexander Podolskiy, Andrey Bout +2
Grammatical error correction (GEC) is an important NLP task that is currently usually solved with autoregressive sequence-to-sequence models. However, approaches of this class are…
Sinkhorn Transformations for Single-Query Postprocessing in Text-Video Retrieval
Konstantin Yakovlev, Gregory Polyakov, Ilseyar Alimova +4
A recent trend in multimodal retrieval is related to postprocessing test set results via the dual-softmax loss (DSL). While this approach can bring significant improvements, it usu…
PanGu-Σ: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing
Xiaozhe Ren, Pingyi Zhou, Xinfan Meng +14
The scaling of large language models has greatly improved natural language understanding, generation, and reasoning. In this work, we develop a system that trained a trillion-param…
Template-based Approach to Zero-shot Intent Recognition
Dmitry Lamanov, Pavel Burnyshev, Ekaterina Artemova +3
The recent advances in transfer learning techniques and pre-training of large contextualized encoders foster innovation in real-life applications, including dialog assistants. Prac…
A Single Example Can Improve Zero-Shot Data Generation
Pavel Burnyshev, Valentin Malykh, Andrey Bout +2
Sub-tasks of intent classification, such as robustness to distribution shift, adaptation to specific user groups and personalization, out-of-domain detection, require extensive and…
Revisiting Mahalanobis Distance for Transformer-Based Out-of-Domain Detection
Alexander Podolskiy, Dmitry Lipin, Andrey Bout +2
Real-life applications, heavily relying on machine learning, such as dialog systems, demand out-of-domain detection methods. Intent classification models should be equipped with a…