Search Results for author:
Found 8 papers, 4 papers with code
Large Language Models are Parallel Multilingual Learners
In this study, we reveal an in-context learning (ICL) capability of multilingual large language models (LLMs): by translating the input to several languages, we provide Parallel Input in Multiple Languages (PiM) to LLMs, which significantly enhances their comprehension abilities.
Learning Evaluation Models from Large Language Models for Sequence Generation
Large language models achieve state-of-the-art performance on sequence generation evaluation, but typically have a large number of parameters.
ESRL: Efficient Sampling-based Reinforcement Learning for Sequence Generation
Applying Reinforcement Learning (RL) to sequence generation models enables the direct optimization of long-term rewards (\textit{e. g.,} BLEU and human feedback), but typically requires large-scale sampling over a space of action sequences.
Towards Fully 8-bit Integer Inference for the Transformer Model
8-bit integer inference, as a promising direction in reducing both the latency and storage of deep neural networks, has made great progress recently.
Does Multi-Encoder Help? A Case Study on Context-Aware Neural Machine Translation
In encoder-decoder neural models, multiple encoders are in general used to represent the contextual information in addition to the individual sentence.
Learning Architectures from an Extended Search Space for Language Modeling
Neural architecture search (NAS) has advanced significantly in recent years but most NAS systems restrict search to learning architectures of a recurrent or convolutional cell.
Neural Machine Translation with Joint Representation
Though early successes of Statistical Machine Translation (SMT) systems are attributed in part to the explicit modelling of the interaction between any two source and target units, e. g., alignment, the recent Neural Machine Translation (NMT) systems resort to the attention which partially encodes the interaction for efficiency.
Sharing Attention Weights for Fast Transformer
This is even 16 times faster than the baseline with no use of the attention cache.
