14 citations · 27 across the 35 of their papers we have counts for
7 papers · 1 filter
The Ups and Downs of Large Language Model Inference with Vocabulary Trimming by Language Heuristics
Nikolay Bogoychev, Pinzhen Chen, Barry Haddow +1
Deploying large language models (LLMs) encounters challenges due to intensive computational and memory requirements. Our research examines vocabulary trimming (VT) inspired by rest…
Towards Effective Disambiguation for Machine Translation with Large Language Models
Vivek Iyer, Pinzhen Chen, Alexandra Birch
Resolving semantic ambiguity has long been recognised as a central challenge in the field of Machine Translation. Recent work on benchmarking translation performance on ambiguous s…
Terminology-Aware Translation with Constrained Decoding and Large Language Model Prompting
Nikolay Bogoychev, Pinzhen Chen
Terminology correctness is important in the downstream application of machine translation, and a prevalent way to ensure this is to inject terminology constraints into a translatio…
Monolingual or Multilingual Instruction Tuning: Which Makes a Better Alpaca
Pinzhen Chen, Shaoxiong Ji, Nikolay Bogoychev +3
Foundational large language models (LLMs) can be instruction-tuned to perform open-domain question answering, facilitating applications like chat assistants. While such efforts are…
Iterative Translation Refinement with Large Language Models
Pinzhen Chen, Zhicheng Guo, Barry Haddow +1
We propose iteratively prompting a large language model to self-correct a translation, with inspiration from their strong language understanding and translation capability as well…
PMIndiaSum: Multilingual and Cross-lingual Headline Summarization for Languages in India
Ashok Urlana, Pinzhen Chen, Zheng Zhao +3
This paper introduces PMIndiaSum, a multilingual and massively parallel summarization corpus focused on languages in India. Our corpus provides a training and testing ground for fo…
Exploring Data Augmentation for Code Generation Tasks
Pinzhen Chen, Gerasimos Lampouras
Advances in natural language processing, such as transfer learning from pre-trained language models, have impacted how models are trained for programming language tasks too. Previo…