Bing Chat: The Future of Search Engines?

Kelly, Dominique; Chen, Yimin; Cornwell, Sarah E.; Delellis, Nicole S.; Mayhew, Alex; Onaolapo, Sodiq; Rubin, Victoria L.

doi:10.1002/pra2.927

Cited by 8 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Noteworthy, there is a sheer infinite number of further LLMs and several significant extensions to these existing approaches ( Yang et al, 2019 ; Brown et al, 2020 ; Stiennon et al, 2020 ; Chen et al, 2021 ; Neelakantan et al, 2022 ; Ouyang et al, 2022 ). Thanks to the generality of language, these models have been applied in various applications, ranging from search engines and chat bots ( Kelly et al, 2023 ), general problem-solving and knowledge extraction ( Petroni et al, 2019 ), medical diagnosis ( Hirosawa et al, 2023 ; Nori et al, 2023 ; Rao et al, 2023 ; Shea et al, 2023 ; Waisberg et al, 2023 ), education ( Tack and Piech, 2022 ; Ausat et al, 2023 ; Firat, 2023 ), law ( Cyphert, 2021 ; Perlman, 2022 ; Trozze et al, 2023 ) and robotics, as we will discuss in the next section.…”

Section: Introductionmentioning

confidence: 99%

Exploring a GPT-based large language model for variable autonomy in a VR-based human-robot teaming simulation

Lakhnati,

Pascher,

Gerken

2024

Front. Robot. AI

View full text Add to dashboard Cite

In a rapidly evolving digital landscape autonomous tools and robots are becoming commonplace. Recognizing the significance of this development, this paper explores the integration of Large Language Models (LLMs) like Generative pre-trained transformer (GPT) into human-robot teaming environments to facilitate variable autonomy through the means of verbal human-robot communication. In this paper, we introduce a novel simulation framework for such a GPT-powered multi-robot testbed environment, based on a Unity Virtual Reality (VR) setting. This system allows users to interact with simulated robot agents through natural language, each powered by individual GPT cores. By means of OpenAI’s function calling, we bridge the gap between unstructured natural language input and structured robot actions. A user study with 12 participants explores the effectiveness of GPT-4 and, more importantly, user strategies when being given the opportunity to converse in natural language within a simulated multi-robot environment. Our findings suggest that users may have preconceived expectations on how to converse with robots and seldom try to explore the actual language and cognitive capabilities of their simulated robot collaborators. Still, those users who did explore were able to benefit from a much more natural flow of communication and human-like back-and-forth. We provide a set of lessons learned for future research and technical implementations of similar systems.

show abstract

Section: Introductionmentioning

confidence: 99%

Exploring a GPT-based large language model for variable autonomy in a VR-based human-robot teaming simulation

Lakhnati,

Pascher,

Gerken

2024

Front. Robot. AI

View full text Add to dashboard Cite

show abstract

“…Numerous LLMs have emerged in recent years, including GPT (Radford et al, 2018), BERT (Devlin et al, 2018), XLNet (Yang et al, 2019), T5 (Raffel et al, 2020), RoBERTa (Liu et al, 2019), GPT-3 (Floridi & Chiriatti, 2020;Scao et al, 2022), Bing Chat (Kelly et al, 2023), Google Bard (Urman & Makhortykh, 2023), and the widely recognized GPT-3.5 and GPT-4 (Rosenfeld & Lazebnik, 2024). ChatGPT, an AI-driven conversational agent powered by a large language models (LLMs) trained on rich Internet text data, is expected to address numerous limitations of previous chatbot technology and reshape learning dynamics (Heidt, 2023).…”

Section: Introductionmentioning

confidence: 99%

Empowering Doctoral Academic Research: Artificial Intelligence-driven Insights from Large Language Models

Yuan,

Harun

2024

Preprint

View full text Add to dashboard Cite

The ever-expanding volume and complexity of academic research pose significant challenges for researchers, particularly doctoral students. In response to these challenges, utilizing Large Language Models (LLMs) has emerged as a promising alternative solution. Such LLMs as ChatGPT, Bing Chat and Google Bard are applied in academic research. This study conducted semi-structured interviews with 50 PhD students and used thematic analysis to explore the application of LLMs in academic research. The results indicate that LLMs assist literature reading by extracting main content, providing research topics, and making reading convenient; assist research design by generating research design ideas; assist academic writing by generating writing ideas, polishing writing, analyzing and visualizing data; assist knowledge construction by offering subject matter expertise and promoting science; assist admin works by writing admin emails. Based on these, a five-dimensional framework of AI-assisted academic research (AIAAR) has been established to explain the assistance of LLMs in academic research. This research not only sheds light on the practical benefits of integrating LLMs in academic research but also provides insights into optimizing their usage for enhanced scholarly productivity and knowledge advancement.

show abstract

A Survey of Text Classification With Transformers: How Wide? How Large? How Long? How Accurate? How Expensive? How Safe?

Fields,

Chovanec,

Madiraju

2024

IEEE Access

View full text Add to dashboard Cite

Text classification is a basic task in natural language processing (NLP) with applications from sentiment analysis to question-answering with chat bots. In recent years, transformer-based models have emerged as the prevailing framework in NLP, demonstrating excellent results across many benchmarks. This paper recommends an expanded taxonomy of applications and provides a review of the performance of different models across these applications. The use of traditional research techniques plus co-citation and bibliographic coupling provides a comprehensive view of the current and past research in this area. The study begins by providing an overview of the history of transformer-based models with an emphasis on recent large language models (LLM). Next, uni-modal (text only) inputs and the emerging area of multi-modal classification are discussed to provide a comparison of current and emerging research in this area. Gaps are highlighted in the use of multi-modal text/numeric/columnar data and recommendations for future research are provided. Finally, the length of text input variables (tokens) is reviewed to explore the evolution from short-text to longer document applications. Furthermore, the accuracy on 358 datasets across 20 applications is reviewed and unexpected results emerge which show that LLMs are not always the most accurate or least expensive option. In addition to model performance, the safety implications of transformer-based models are reviewed, and a summary of issues related to ethics, bias, social implications, and copyright are explored.

show abstract

Bing Chat: The Future of Search Engines?

Cited by 8 publications

References 9 publications

Exploring a GPT-based large language model for variable autonomy in a VR-based human-robot teaming simulation

Exploring a GPT-based large language model for variable autonomy in a VR-based human-robot teaming simulation

Empowering Doctoral Academic Research: Artificial Intelligence-driven Insights from Large Language Models

A Survey of Text Classification With Transformers: How Wide? How Large? How Long? How Accurate? How Expensive? How Safe?

Contact Info

Product

Resources

About