14 citations · 27 across the 35 of their papers we have counts for
10 papers · 2 filters
Context and System Fusion in Post-ASR Emotion Recognition with Large Language Models
Pavel Stepachev, Pinzhen Chen, Barry Haddow
Large language models (LLMs) have started to play a vital role in modelling speech and text. To explore the best use of context and multiple systems' outputs for post-ASR speech em…
Pitfalls and Outlooks in Using COMET
Vilém Zouhar, Pinzhen Chen, Tsz Kin Lam +2
The COMET metric has blazed a trail in the machine translation community, given its strong correlation with human judgements of translation quality. Its success stems from being a…
EMMA-500: Enhancing Massively Multilingual Adaptation of Large Language Models
Shaoxiong Ji, Zihao Li, Jaakko Paavola +7
In this work, we introduce EMMA-500, a large-scale multilingual language model continue-trained on texts across 546 languages designed for enhanced multilingual performance, focusi…
Quality or Quantity? On Data Scale and Diversity in Adapting Large Language Models for Low-Resource Translation
Vivek Iyer, Bhavitvya Malik, Pavel Stepachev +3
Despite the recent popularity of Large Language Models (LLMs) in Machine Translation (MT), their performance in low-resource languages (LRLs) still lags significantly behind Neural…
Is It Good Data for Multilingual Instruction Tuning or Just Bad Multilingual Evaluation for Large Language Models?
Pinzhen Chen, Simon Yu, Zhicheng Guo +1
Multilingual large language models are designed, claimed, and expected to cater to speakers of varied languages. We hypothesise that the current practices of fine-tuning and evalua…
Fine-Tuning Large Language Models to Translate: Will a Touch of Noisy Data in Misaligned Languages Suffice?
Dawei Zhu, Pinzhen Chen, Miaoran Zhang +3
Traditionally, success in multilingual machine translation can be attributed to three key factors in training data: large volume, diverse translation directions, and high quality.…
How Many Languages Make Good Multilingual Instruction Tuning? A Case Study on BLOOM
Shaoxiong Ji, Pinzhen Chen
Instruction tuning a large language model with multiple languages can prepare it for multilingual downstream tasks. Nonetheless, it is yet to be determined whether having a handful…
UniArk: Improving Generalisation and Consistency for Factual Knowledge Extraction through Debiasing
Yijun Yang, Jie He, Pinzhen Chen +2
Several recent papers have investigated the potential of language models as knowledge bases as well as the existence of severe biases when extracting factual knowledge. In this wor…
EEE-QA: Exploring Effective and Efficient Question-Answer Representations
Zhanghao Hu, Yijun Yang, Junjie Xu +2
Current approaches to question answering rely on pre-trained language models (PLMs) like RoBERTa. This work challenges the existing question-answer encoding convention and explores…
Fine-tuning Large Language Models with Sequential Instructions
Hanxu Hu, Simon Yu, Pinzhen Chen +1
Despite the success of existing instruction-tuned models, we find that they usually struggle to respond to queries with multiple instructions. This impairs their performance in com…