Large-scale pre-training has shown remarkable performance in building open-domain dialogue systems. However, previous works mainly focus on showing and evaluating the conversational performance of the released dialogue model, ignoring the discussion of some key factors towards a powerful human-like chatbot, especially in Chinese scenarios. In this paper, we conduct extensive experiments to investigate these under-explored factors, including data quality control, model architecture designs, training approaches, and decoding strategies. We propose EVA2.0, a large-scale pre-trained open-domain Chinese dialogue model with 2.8 billion parameters, and will make our models and codes publicly available. Automatic and human evaluations show that EVA2.0 significantly outperforms other open-source counterparts. We also discuss the limitations of this work by presenting some failure cases and pose some future research directions on large-scale Chinese open-domain dialogue systems.

A simultaneously familiar and slightly terrifying robot dog wanders through the audience of a session at the Sydney edition of South by South West. On stage, the panellists opine about a future increasingly defined by artificial intelligence and automation. "It's going to get much, much more significant," says Ed Santow, the former human rights commissioner and current director of policy and governance at the UTS Human Technology Institute. "And for many people that will be a good thing, [but] for a lot of people it'll be really, really hard." The robot is creepy but its fan is as noisy as a ps4 so it's not sneaking up on anyone.

Nintendo is having a very good year. The Legend of Zelda: Tears of the Kingdom was a record-breaking success; Switch sales continue to climb even in the console's sixth year. In February, the company opened Super Nintendo World at Universal Studios in California. All of this, though, looks small compared to the hype around the company's ubiquitous moustachioed plumber. This summer, The Super Mario Bros. Movie brought in nearly $1.4 billion globally at the box office, making it the second highest grossing animated film of all time.

My wife is a physician, so we often find ourselves in social engagements with other physicians. When asked what I do, many are intrigued by what it means to be a computer science professor. My graduate students do that now. Do you teach people how to program? No, though some of my colleagues do.

Like most newsreaders, Zae-In wears a microphone pinned to her collar and clutches a stack of notes – but unlike most, her face is entirely fake. A "virtual human" designed by South Korean artificial intelligence company Pulse9, Zae-In spent five months this year reading live news bulletins on national broadcaster SBS. That, you might think, is it then. To adapt the words of another animated newscaster: "I, for one, welcome our new AI overlords." The world belongs to the artificially intelligent and the News at Ten will never be the same again.

Until now, the only free and easy part had been the data. Widely used services like the nonprofit Common Crawl charge Google, Meta, OpenAI and others nothing to use its service, which crawls the internet in search of troves of online text and archives the information for others to download. To assemble the vast quantities of natural language and specialized information needed to train large AI systems, tech companies have combined those archives with online data sets, accessing information made available for research purposes, and increasingly straying from information clearly in the public domain.

Data Synthesis is a promising way to train a small model with very little labeled data. One approach for data synthesis is to leverage the rich knowledge from large language models to synthesize pseudo training examples for small models, making it possible to achieve both data and compute efficiency at the same time. However, a key challenge in data synthesis is that the synthesized dataset often suffers from a large distributional discrepancy from the real task data distribution. Thus, in this paper, we propose Synthesis Step by Step (S3), a data synthesis framework that shrinks this distribution gap by iteratively extrapolating the errors made Figure 1: Training and testing accuracy of DistilBert by a small model trained on the synthesized with ZeroGen (Ye et al., 2022b) on the IMDb dataset dataset on a small real-world validation dataset with 200k training datapoints. Also shown are the training using a large language model. Extensive experiments and testing accuracy of the model trained on Gold-on multiple NLP tasks show that our Data. We can see here that ZeroGen's training accuracy approach improves the performance of a small quickly reaches nearly 100%, but testing accuracy remains model by reducing the gap between the synthetic low.

There is a lack of research into capabilities of recent LLMs to generate convincing text in languages other than English and into performance of detectors of machine-generated text in multilingual settings. This is also reflected in the available benchmarks which lack authentic texts in languages other than English and predominantly cover older generators. To fill this gap, we introduce MULTITuDE, a novel benchmarking dataset for multilingual machine-generated text detection comprising of 74,081 authentic and machine-generated texts in 11 languages (ar, ca, cs, de, en, es, nl, pt, ru, uk, and zh) generated by 8 multilingual LLMs. Using this benchmark, we compare the performance of zero-shot (statistical and black-box) and fine-tuned detectors. Considering the multilinguality, we evaluate 1) how these detectors generalize to unseen languages (linguistically similar as well as dissimilar) and unseen LLMs and 2) whether the detectors improve their performance when trained on multiple languages.

Social cognitive theory explains how people learn and acquire knowledge through observing others. Recent years have witnessed the rapid development of large language models (LLMs), which suggests their potential significance as agents in the society. LLMs, as AI agents, can observe external information, which shapes their cognition and behaviors. However, the extent to which external information influences LLMs' cognition and behaviors remains unclear. This study investigates how external statements and opinions influence LLMs' thoughts and behaviors from a social cognitive perspective. Three experiments were conducted to explore the effects of external information on LLMs' memories, opinions, and social media behavioral decisions. Sociocognitive factors, including source authority, social identity, and social role, were analyzed to investigate their moderating effects. Results showed that external information can significantly shape LLMs' memories, opinions, and behaviors, with these changes mirroring human social cognitive patterns such as authority bias, in-group bias, emotional positivity, and emotion contagion. This underscores the challenges in developing safe and unbiased LLMs, and emphasizes the importance of understanding the susceptibility of LLMs to external influences.

Fake news has emerged as a critical global issue, magnified by the COVID-19 pandemic, underscoring the need for effective preventive tools. Leveraging machine learning, including deep learning techniques, offers promise in combatting fake news. This paper goes beyond by establishing BERT as the superior model for fake news detection and demonstrates its utility as a tool to empower the general populace. We have implemented a browser extension, enhanced with explainability features, enabling real-time identification of fake news and delivering easily interpretable explanations. To achieve this, we have employed two publicly available datasets and created seven distinct data configurations to evaluate three prominent machine learning architectures. Our comprehensive experiments affirm BERT's exceptional accuracy in detecting COVID-19-related fake news. Furthermore, we have integrated an explainability component into the BERT model and deployed it as a service through Amazon's cloud API hosting (AWS). We have developed a browser extension that interfaces with the API, allowing users to select and transmit data from web pages, receiving an intelligible classification in return. This paper presents a practical end-to-end solution, highlighting the feasibility of constructing a holistic system for fake news detection, which can significantly benefit society.