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Found 11 papers, 5 papers with code
Prior Constraints-based Reward Model Training for Aligning Large Language Models
Reinforcement learning with human feedback for aligning large language models (LLMs) trains a reward model typically using ranking loss with comparison pairs. However, the training procedure suffers from an inherent problem: the uncontrolled scaling of reward scores during reinforcement learning due to the lack of constraints while training the reward model. This paper proposes a Prior Constraints-based Reward Model (namely PCRM) training method to mitigate this problem.
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.
Soft Alignment of Modality Space for End-to-end Speech Translation
End-to-end Speech Translation (ST) aims to convert speech into target text within a unified model.
Rethinking and Improving Multi-task Learning for End-to-end Speech Translation
Significant improvements in end-to-end speech translation (ST) have been achieved through the application of multi-task learning.
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.
Augmenting Large Language Model Translators via Translation Memories
Using translation memories (TMs) as prompts is a promising approach to in-context learning of machine translation models.
Improving End-to-end Speech Translation by Leveraging Auxiliary Speech and Text Data
We present a method for introducing a text encoder into pre-trained end-to-end speech translation systems.
Learning Light-Weight Translation Models from Deep Transformer
We proposed a novel group-permutation based knowledge distillation approach to compressing the deep Transformer model into a shallow model.
Improved Differentiable Architecture Search for Language Modeling and Named Entity Recognition
In this paper, we study differentiable neural architecture search (NAS) methods for natural language processing.
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