ParroT: Translating during Chat using Large Language Models tuned with Human Translation and Feedback
Tencent AI Lab（腾讯人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）；Shanghai Jiao Tong University（上海交通大学）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）
Large language models (LLMs) like ChatGPT have exhibited remarkable abilities on a wide range of natural language processing~(NLP) tasks, including various machine translation abilities accomplished during chat. However, these models are only accessible through restricted APIs, which creates barriers to new research and advancements in the field. Therefore, we propose ParroT, a framework to enhance and regulate the translation abilities during chat based on open-source LLMs (e.g., LLaMA), human-written translation and feedback data. Specifically, ParroT reformulates translation data into the instruction-following style, and introduces a "$\mathbf{Hint}$" field for incorporating extra requirements to regulate the translation process. Accordingly, we propose three instruction types for finetuning ParroT models, including translation instruction, contrastive instruction, and error-guided instruction. Experiments on Flores subsets and WMT22 test sets suggest that translation instruction improves the translation performance of vanilla LLMs significantly while error-guided instruction can lead to further improvement, which demonstrates the importance of learning from low-quality translations annotated by humans. We also demonstrate the potential of automatic evaluation tools in providing quality information of translations, when constructing error-guided instructions for directions that lack human annotation data. Please refer to our Github project for more implementation details: https://github.com/wxjiao/ParroT
Do Stochastic Parrots have Feelings Too? Improving Neural Detection of Synthetic Text via Emotion Recognition
School of Computing, Dublin City University（都柏林城市大学计算学院）；SFI Centre for Research Training in Machine Learning at Dublin City University（都柏林城市大学SFI机器学习研究培训中心）；School of Computer Science and Statistics, Trinity College Dublin（都柏林圣三一学院计算机科学与统计学院）
In spoken dialogue systems, we aim to deploy artificial intelligence to build automated dialogue agents that can converse with humans. Dialogue systems are increasingly being designed to move beyond just imitating conversation and also improve from such interactions over time. In this survey, we present a broad overview of methods developed to build dialogue systems over the years. Different use cases for dialogue systems ranging from task-based systems to open domain chatbots motivate and necessitate specific systems. Starting from simple rule-based systems, research has progressed towards in…
A blind spot for large language models: Supradiegetic linguistic information
University of Vermont（佛蒙特大学）；Vermont Advanced Computing Center（佛蒙特高级计算中心）；Vermont Complex Systems Center（佛蒙特复杂系统中心）；MassMutual Center of Excellence in Complex Systems and Data Science（MassMutual复杂系统与数据科学卓越中心）；Department of Computer Science, University of Vermont（佛蒙特大学计算机科学系）
X-Instruction: Aligning Language Model in Low-resource Languages with Self-curated Cross-lingual Instructions
State Key Laboratory of Multimodal Artificial Intelligence Systems（多模态人工智能系统国家重点实验室）；Institute of Automation, CAS（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
ChatGPT Perpetuates Gender Bias in Machine Translation and Ignores Non-Gendered Pronouns: Findings across Bengali and Five other Low-Resource Languages
Neural machine translation systems typically are trained on curated corpora and break when faced with non-standard orthography or punctuation. Resilience to spelling mistakes and typos, however, is crucial as machine translation systems are used to translate texts of informal origins, such as chat conversations, social media posts and web pages. We propose a simple generative noise model to generate adversarial examples of ten different types. We use these to augment machine translation systems' training data and show that, when tested on noisy data, systems trained using adversarial examples …
The Effect of Human v/s Synthetic Test Data and Round-tripping on Assessment of Sentiment Analysis Systems for Bias
AI Institute University of South Carolina（南卡罗来纳大学人工智能研究所）；Dept. of Computer Science Netaji Subhas University of Technology（Netaji Subhas 科技大学计算机科学系）；Dept. of Computer Science and Engineering University of South Carolina（南卡罗来纳大学计算机科学与工程系）；Dept. of Integrated Information Technology University of South Carolina（南卡罗来纳大学综合信息技术系）
Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis
Nanjing University（南京大学）；Shanghai AI Lab（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；Peking University（北京大学）；The University of Hong Kong（香港大学）；Carnegie Mellon University（卡内基梅隆大学）
