The report evaluates the performance of ChatGPT for machine translation in terms of translation prompt, multilingual translation, and translation robustness. Overall, ChatGPT performs competitively with commercial translation products on high-resource European languages but lags behind on low-resource or distant languages. However, an interesting strategy called pivot prompting improves the translation performance significantly for distant languages. ChatGPT does not perform as well as commercial systems on biomedical abstracts or Reddit comments but has potential as a good translator for spoken language. In conclusion, while ChatGPT shows promise for machine translation, especially for high-resource European languages, there is still room for improvement in its performance on low-resource or distant languages and specialized domains.

The release of OpenAI’s ChatGPT-3 has caused concern among educators who fear it may be used to cheat on exams. However, the commentary proposes an alternative view that educators can leverage AI to build supportive learning environments for students who have demonstrated good character. The use of ChatGPT-3 for academic dishonesty has already been addressed in the literature on plagiarism and academic integrity, but the commentary suggests that with appropriate support mechanisms, character development, and authentic assessment, ChatGPT-3 can be used to support deeper learning and better learning outcomes. The authors suggest that students who demonstrate good character and know how to use ChatGPT-3 for good can engage effectively with the application. The commentary offers opportunities for practitioners to use AI to support their students' learning and for scholars to research the potential of AI in education. Rather than seeing ChatGPT-3 as a threat to education, the authors suggest that educators can use it as a tool to create a supportive learning environment.

Recommendations have been issued by the World Association of Medical Editors (WAME) regarding the use of chatbots such as ChatGPT as co-authors on published papers. The aim is to inform editors about the capabilities and limitations of these chatbots, helping them to develop policies that address how the use of such technology might be attributed in submitted work, and providing access to manuscript screening tools. The development of these guidelines is a response to the fact that several journals have begun to publish articles in which chatbots have been co-authors. WAME recognizes that the field is evolving rapidly, and expects the recommendations to evolve too. Although the publication of papers in which chatbots are co-authors is relatively new, there is some evidence to suggest that this practice may become more common in the future. It is therefore essential that editors are aware of this technology, its strengths and limitations, and that policies are put in place to ensure that the use of chatbots is transparent and consistent across all journals.

A natural language processing model called ChatGPT has been evaluated in its ability to answer questions within the scope of the United States Medical Licensing Examination (USMLE) Step 1 and Step 2 exams. Two sets of multiple-choice questions were used to test ChatGPT, with one set derived from AMBOSS, a popular question bank for medical students, and the second set being the National Board of Medical Examiners (NBME) Free 120-question exams. ChatGPT achieved accuracies of over 60% on the NBME-Free-Step-1 dataset, showing comparable performance to a third-year medical student. Additionally, the model demonstrated logical justification and informational context across the majority of its answers. The authors suggest the potential applications of ChatGPT as a medical education tool. However, the model demonstrated a significant decrease in performance as question difficulty increased within the AMBOSS-Step1 dataset. Overall, ChatGPT marks a significant improvement in natural language processing models in the field of medical question answering.

The paper provides an overview of ChatGPT, a public tool developed by OpenAI, and its underlying technology, Generative Pretrained Transformer. It discusses the benefits of ChatGPT such as improving search and discovery, reference and information services, cataloging and metadata generation, and content creation. The paper also highlights ethical considerations that need to be taken into account, such as privacy and bias. The interview with ChatGPT also emphasizes the need to use this technology responsibly and ethically, and work alongside it to improve our work, rather than letting it abuse us in the race to create new scholarly knowledge and educate future professionals. The paper discusses the history and technology of GPT, including its ability to perform a wide range of language-based tasks and how ChatGPT uses this technology to function as a sophisticated chatbot. The conclusion suggests that while ChatGPT has considerable power to advance academia and librarianship, it is essential to consider the ethical implications and use this technology responsibly to improve our work.

OpenAI's Chat Generative Pre-trained Transformer (ChatGPT) has revolutionized artificial intelligence by improving human-model interaction. While earlier studies have shown ChatGPT to have detailed and precise answers, these studies were mostly non-automated and tested on a limited scale. However, a recent study examined ChatGPT's abilities on 25 different analytical natural language processing tasks, many of which are subjective even for humans, such as sentiment analysis, emotion recognition, offensiveness and stance detection, and natural language inference. ChatGPT's querying process for the study was automated, and over 38k responses were analyzed. Comparing ChatGPT's results with existing State-of-the-Art solutions showed that the average loss in ChatGPT's quality was around 25% for zero-shot and few-shot evaluation. The more difficult the task, the higher the ChatGPT loss. The study also revealed a ChatGPT bias, most likely due to the OpenAI-imposed rules on human trainers. The study's results show that there should be a discussion on the usefulness of predictive NLP models for society and how to establish learning and validation procedures for these systems.

ChatGPT is an AI-powered chatbot that can provide solutions to transportation issues in North America. The analysis of its answers to four prompts related to transport issues and solutions in the United States and Canada shows that ChatGPT generally aligns with transport researchers’ expectations. However, limitations in its training data might limit its ability to provide trustworthy or sound solutions. The potential issues could include geographic biases, inaccuracy, and others. Therefore, ChatGPT can be a useful starting point for discussing transportation issues and solutions. However, users need to be aware of its limitations.

A study has found that a natural language processing model called Background Chat Generative Pre-trained Transformer (ChatGPT) achieved a passing score equivalent to that of a third-year medical student on the National Board of Medical Examiners (NBME) Free-Step-1 data set. ChatGPT is a 175-billion-parameter model that can generate conversation-style responses to user input. The study evaluated ChatGPT's ability to answer medical exam questions and provide answers that are logical and informative. The model was tested on two sets of multiple-choice questions pertaining to the United States Medical Licensing Examination Step 1 and Step 2 exams, with question difficulty taken into account. ChatGPT outperformed another large language model, InstructGPT, by 8.15% on average. The study also found that ChatGPT provided logical justification for answer selection in 100% of outputs and had information internal to the question in 96.8% of all questions. The presence of information external to the question was lower for incorrect answers compared to correct answers. The study suggests that ChatGPT has potential applications as an interactive medical education tool to support learning.

The COVID-19 pandemic has forced many workers to integrate their work and personal lives due to mandatory telecommuting. As a result, researchers set out to understand how boundary enactment could mitigate work-life conflict. The study examined two key features of work design, autonomy, and job feedback, which can help employees balance their work and personal lives. The study found that creating an inflexible and impermeable boundary between work and personal roles can help individuals minimise conflict between the two. Although segmentation may not always play a mediating role, it consistently lessens inter-role conflict. The findings suggest that work design is crucial in supporting employees in achieving a work-life balance, particularly through segmentation. Therefore, organisations should consider creating a supportive work environment and strategic planning to help employees balance their professional and personal lives for enhanced job satisfaction, productivity, and overall well-being.

The transition to remote work due to COVID-19 has impacted the work-related stress and quality of life of postsecondary educators in the United States, according to a new study. The cross-sectional survey of 1,575 postsecondary teachers found that those who worked in a hybrid programme throughout 2020 believed they had more control at work and an overall higher quality of working life, while those who worked remotely only or on-campus only experienced more stress at work. The study also found that less time spent working from home contributed to higher stress and lower perceived control at work. The results highlight the importance of remote work policies in easing stress and increasing quality of life for faculty members in higher education. The findings can guide the implementation of future work-from-home or return-to-campus policies.