Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) and have recently gained significant attention in the domain of Recommendation Systems (RS). These models, trained on massive amounts of data using self-supervised learning, have demonstrated remarkable success in learning universal representations and have the potential to enhance various aspects of recommendation systems by some effective transfer techniques such as fine-tuning and prompt tuning, and so on. The crucial aspect of harnessing the power of language models in enhancing recommendation quality is the utilization of their high-quality representations of textual features and their extensive coverage of external knowledge to establish correlations between items and users. To provide a comprehensive understanding of the existing LLM-based recommendation systems, this survey presents a taxonomy that categorizes these models into two major paradigms, respectively Discriminative LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation (GLLM4Rec), with the latter being systematically sorted out for the first time. Furthermore, we systematically review and analyze existing LLM-based recommendation systems within each paradigm, providing insights into their methodologies, techniques, and performance. Additionally, we identify key challenges and several valuable findings to provide researchers and practitioners with inspiration. We have also created a GitHub repository to index relevant papers on LLMs for recommendation, https://github.com/WLiK/LLM4Rec.

The software development industry is amid another disruptive paradigm change - adopting the use of generative AI (GAI) assistants for programming. Whilst AI is already used in various areas of software engineering, GAI technologies, such as GitHub Copilot and ChatGPT, have ignited peoples' imaginations (and fears). It is unclear how the industry will adapt, but the move to integrate these technologies by large software companies, such as Microsoft (GitHub, Bing) and Google (Bard), is a clear indication of intent and direction. We performed exploratory interviews with industry professionals to understand current practice and challenges, which we incorporate into our vision of a future of software development education and make some pedagogical recommendations.

Many high-level skills that are required for computer vision tasks, such as parsing questions, comparing and contrasting semantics, and writing descriptions, are also required in other domains such as natural language processing. In this paper, we ask whether it is possible to learn those skills from text data and then transfer them to vision tasks without ever training on visual training data. Key to our approach is exploiting the joint embedding space of contrastively trained vision and language encoders. In practice, there can be systematic differences between embedding spaces for different modalities in contrastive models, and we analyze how these differences affect our approach and study strategies to mitigate this concern. We produce models using only text training data on four representative tasks: image captioning, visual entailment, visual question answering and visual news captioning, and evaluate them on standard benchmarks using images. We find these models perform close to models trained on images, while surpassing prior work for captioning and visual entailment in this text-only setting by over 9 points, and outperforming all prior work on visual news by over 30 points. We also showcase a variety of stylistic image captioning models that are trained using no image data and no human-curated language data, but instead using readily-available text data from books, the web, or language models.

Holidaymakers typically rely on experienced tour guides and local companies to recommend excursions to medieval castles and spectacular waterfalls, but the world's biggest tour operator has said it will entrust the service to artificial intelligence instead. The German travel company Tui has started using ChatGPT in its app to provide holiday recommendations, in the latest sign of traditional businesses racing to harness AI. ChatGPT was released in November by the startup OpenAI, and became the fastest-growing consumer app ever as users flocked to try it out. The rapidly improving technology can produce comprehensible language and even photos and videos by crunching through and synthesising huge amounts of data. Tui's feature has been released to half of UK app users, with the aim of introducing it to all "in the next weeks", a spokesperson said.

There's a big reason why every company hoping to deal in some way with artificial intelligence is either spending or raising billions upon billions of dollars right now, and it's not just investor hype. These stacks of cash are necessary for meeting the costs of building, training, and maintaining resource-intensive (and resource-lacking) power-sucking content generators like ChatGPT, as well as the resource-intensive power-sucking data sets, neural networks, and large language models, or LLMs, they're trained on--such as OpenAI's GPT-4, whose API was recently made public to paying customers with coding expertise. Someone who understands the energy issue all too well is OpenAI's CEO himself, Sam Altman. Back in May, while testifying to Congress about the challenges wrought by the A.I. arms race his company ushered into the world, Altman admitted something curious: that he'd prefer for his wildly popular ChatGPT bot, at that time the fastest-growing app in history, to have fewer users. "We're not trying to get them to use it more," he stated. "Actually, we'd love it if they use it less, because we don't have enough GPUs."

Department of Computer Science & Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India Abstract Information extraction and textual comprehension from materials literature are vital for developing an exhaustive knowledge base that enables accelerated materials discovery. Language models have demonstrated their capability to answer domain-specific questions and retrieve information from knowledge bases. However, there are no benchmark datasets in the materials domain that can evaluate the understanding of the key concepts by these language models. In this work, we curate a dataset of 650 challenging questions from the materials domain that require the knowledge and skills of a materials student who has cleared their undergraduate degree. We classify these questions based on their structure and the materials science domain-based subcategories. Further, we evaluate the performance of GPT-3.5 and GPT-4 models on solving these questions via zero-shot and chain of thought prompting. It is observed that GPT-4 gives the best performance (~62% accuracy) as compared to GPT-3.5. Interestingly, in contrast to the general observation, no significant improvement in accuracy is observed with the chain of thought prompting. To evaluate the limitations, we performed an error analysis, which revealed conceptual errors (~64%) as the major contributor compared to computational errors (~36%) towards the reduced performance of LLMs. We hope that the dataset and analysis performed in this work will promote further research in developing better materials science domain-specific LLMs and strategies for information extraction.

Large Language Models (LLMs) provide a possibility to make a great breakthrough in medicine. The establishment of a standardized medical benchmark becomes a fundamental cornerstone to measure progression. However, medical environments in different regions have their local characteristics, e.g., the ubiquity and significance of traditional Chinese medicine within China. Therefore, merely translating English-based medical evaluation may result in \textit{contextual incongruities} to a local region. To solve the issue, we propose a localized medical benchmark called CMB, a Comprehensive Medical Benchmark in Chinese, designed and rooted entirely within the native Chinese linguistic and cultural framework. While traditional Chinese medicine is integral to this evaluation, it does not constitute its entirety. Using this benchmark, we have evaluated several prominent large-scale LLMs, including ChatGPT, GPT-4, dedicated Chinese LLMs, and LLMs specialized in the medical domain. It is worth noting that our benchmark is not devised as a leaderboard competition but as an instrument for self-assessment of model advancements. We hope this benchmark could facilitate the widespread adoption and enhancement of medical LLMs within China. Check details in \url{https://cmedbenchmark.llmzoo.com/}.

The field of software security testing, more specifically penetration testing, is an activity that requires high levels of expertise and involves many manual testing and analysis steps. This paper explores the potential usage of large-language models, such as GPT3.5, to augment penetration testers with AI sparring partners. We explore the feasibility of supplementing penetration testers with AI models for two distinct use cases: high-level task planning for security testing assignments and low-level vulnerability hunting within a vulnerable virtual machine. For the latter, we implemented a closed-feedback loop between LLM-generated low-level actions with a vulnerable virtual machine (connected through SSH) and allowed the LLM to analyze the machine state for vulnerabilities and suggest concrete attack vectors which were automatically executed within the virtual machine. We discuss promising initial results, detail avenues for improvement, and close deliberating on the ethics of providing AI-based sparring partners.

Large language models (LLMs) have demonstrated impressive impact in the field of natural language processing, but they still struggle with several issues regarding, such as completeness, timeliness, faithfulness and adaptability. While recent efforts have focuses on connecting LLMs with external knowledge sources, the integration of knowledge bases (KBs) remains understudied and faces several challenges. In this paper, we introduce KnowledGPT, a comprehensive framework to bridge LLMs with various knowledge bases, facilitating both the retrieval and storage of knowledge. The retrieval process employs the program of thought prompting, which generates search language for KBs in code format with pre-defined functions for KB operations. Besides retrieval, KnowledGPT offers the capability to store knowledge in a personalized KB, catering to individual user demands. With extensive experiments, we show that by integrating LLMs with KBs, KnowledGPT properly answers a broader range of questions requiring world knowledge compared with vanilla LLMs, utilizing both knowledge existing in widely-known KBs and extracted into personalized KBs.

The integration of emotional support into various conversational scenarios presents profound societal benefits, such as social interactions, mental health counseling, and customer service. However, there are unsolved challenges that hinder real-world applications in this field, including limited data availability and the absence of well-accepted model training paradigms. This work endeavors to navigate these challenges by harnessing the capabilities of Large Language Models (LLMs). We introduce an innovative methodology that synthesizes human insights with the computational prowess of LLMs to curate an extensive emotional support dialogue dataset. Our approach is initiated with a meticulously designed set of dialogues spanning diverse scenarios as generative seeds. By utilizing the in-context learning potential of ChatGPT, we recursively generate an ExTensible Emotional Support dialogue dataset, named ExTES. Following this, we deploy advanced tuning techniques on the LLaMA model, examining the impact of diverse training strategies, ultimately yielding an LLM meticulously optimized for emotional support interactions. An exhaustive assessment of the resultant model showcases its proficiency in offering emotional support, marking a pivotal step in the realm of emotional support bots and paving the way for subsequent research and implementations.