Effective diabetes management is crucial for maintaining health in diabetic patients. Large Language Models (LLMs) have opened new avenues for diabetes management, facilitating their efficacy. However, current LLM-based approaches are limited by their dependence on general sources and lack of integration with domain-specific knowledge, leading to inaccurate responses. In this paper, we propose a knowledge-infused LLM-powered conversational health agent (CHA) for diabetic patients. We customize and leverage the open-source openCHA framework, enhancing our CHA with external knowledge and analytical capabilities. This integration involves two key components: 1) incorporating the American Diabetes Association dietary guidelines and the Nutritionix information and 2) deploying analytical tools that enable nutritional intake calculation and comparison with the guidelines. We compare the proposed CHA with GPT4. Our evaluation includes 100 diabetes-related questions on daily meal choices and assessing the potential risks associated with the suggested diet. Our findings show that the proposed agent demonstrates superior performance in generating responses to manage essential nutrients.

The emergence of Large Language Models (LLMs) has brought to light promising language generation capabilities, particularly in performing tasks like complex reasoning and creative writing. Consequently, distillation through imitation of teacher responses has emerged as a popular technique to transfer knowledge from LLMs to more accessible, Small Language Models (SLMs). While this works well for simpler tasks, there is a substantial performance gap on tasks requiring intricate language comprehension and creativity, such as humor generation. We hypothesize that this gap may stem from the fact that creative tasks might be hard to learn by imitation alone and explore whether an approach, involving supplementary guidance from the teacher, could yield higher performance. To address this, we study the effect of assigning a dual role to the LLM - as a "teacher" generating data, as well as a "critic" evaluating the student's performance. Our experiments on humor generation reveal that the incorporation of feedback significantly narrows the performance gap between SLMs and their larger counterparts compared to merely relying on imitation. As a result, our research highlights the potential of using feedback as an additional dimension to data when transferring complex language abilities via distillation.

The distinction between humans and animals lies in the unique ability of humans to use and create tools. Tools empower humans to overcome physiological limitations, fostering the creation of magnificent civilizations. Similarly, enabling foundational models like Large Language Models (LLMs) with the capacity to learn external tool usage may serve as a pivotal step toward realizing artificial general intelligence. Previous studies in this field have predominantly pursued two distinct approaches to augment the tool invocation capabilities of LLMs. The first approach emphasizes the construction of relevant datasets for model fine-tuning. The second approach, in contrast, aims to fully exploit the inherent reasoning abilities of LLMs through in-context learning strategies. In this work, we introduce a novel tool invocation pipeline designed to control massive real-world APIs. This pipeline mirrors the human task-solving process, addressing complicated real-life user queries. At each step, we guide LLMs to summarize the achieved results and determine the next course of action. We term this pipeline `from Summary to action', Sum2Act for short. Empirical evaluations of our Sum2Act pipeline on the ToolBench benchmark show significant performance improvements, outperforming established methods like ReAct and DFSDT. This highlights Sum2Act's effectiveness in enhancing LLMs for complex real-world tasks.

Large Language Model (LLM) systems are inherently compositional, with individual LLM serving as the core foundation with additional layers of objects such as plugins, sandbox, and so on. Along with the great potential, there are also increasing concerns over the security of such probabilistic intelligent systems. However, existing studies on LLM security often focus on individual LLM, but without examining the ecosystem through the lens of LLM systems with other objects (e.g., Frontend, Webtool, Sandbox, and so on). In this paper, we systematically analyze the security of LLM systems, instead of focusing on the individual LLMs. To do so, we build on top of the information flow and formulate the security of LLM systems as constraints on the alignment of the information flow within LLM and between LLM and other objects. Based on this construction and the unique probabilistic nature of LLM, the attack surface of the LLM system can be decomposed into three key components: (1) multi-layer security analysis, (2) analysis of the existence of constraints, and (3) analysis of the robustness of these constraints. To ground this new attack surface, we propose a multi-layer and multi-step approach and apply it to the state-of-art LLM system, OpenAI GPT4. Our investigation exposes several security issues, not just within the LLM model itself but also in its integration with other components. We found that although the OpenAI GPT4 has designed numerous safety constraints to improve its safety features, these safety constraints are still vulnerable to attackers. To further demonstrate the real-world threats of our discovered vulnerabilities, we construct an end-to-end attack where an adversary can illicitly acquire the user's chat history, all without the need to manipulate the user's input or gain direct access to OpenAI GPT4. Our demo is in the link: https://fzwark.github.io/LLM-System-Attack-Demo/

Despite the predominance of English in their training data, English-centric Large Language Models (LLMs) like GPT-3 and LLaMA display a remarkable ability to perform multilingual tasks, raising questions about the depth and nature of their cross-lingual capabilities. This paper introduces the decomposed prompting approach to probe the linguistic structure understanding of these LLMs in sequence labeling tasks. Diverging from the single text-to-text prompt, our method generates for each token of the input sentence an individual prompt which asks for its linguistic label. We assess our method on the Universal Dependencies part-of-speech tagging dataset for 38 languages, utilizing both English-centric and multilingual LLMs. Our findings show that decomposed prompting surpasses the iterative prompting baseline in efficacy and efficiency under zero- and few-shot settings. Further analysis reveals the influence of evaluation methods and the use of instructions in prompts. Our multilingual investigation shows that English-centric language models perform better on average than multilingual models. Our study offers insights into the multilingual transferability of English-centric LLMs, contributing to the understanding of their multilingual linguistic knowledge.

Employing Large Language Models (LLM) in various downstream applications such as classification is crucial, especially for smaller companies lacking the expertise and resources required for fine-tuning a model. Fairness in LLMs helps ensure inclusivity, equal representation based on factors such as race, gender and promotes responsible AI deployment. As the use of LLMs has become increasingly prevalent, it is essential to assess whether LLMs can generate fair outcomes when subjected to considerations of fairness. In this study, we introduce a framework outlining fairness regulations aligned with various fairness definitions, with each definition being modulated by varying degrees of abstraction. We explore the configuration for in-context learning and the procedure for selecting in-context demonstrations using RAG, while incorporating fairness rules into the process. Experiments conducted with different LLMs indicate that GPT-4 delivers superior results in terms of both accuracy and fairness compared to other models. This work is one of the early attempts to achieve fairness in prediction tasks by utilizing LLMs through in-context learning.

Next-token prediction (NTP), the go-to training paradigm in training large language models, involves predicting the next token in a sequence. Departing from traditional one-hot classification, in NTP, multiple tokens with varying frequencies follow each given context. This work frames NTP training as cross-entropy minimization over distinct contexts, each associated with a sparse empirical probability vector across a finite vocabulary. It then addresses the following question: do gradient-based optimizers exhibit a bias towards solutions with specific structure as the NTP training loss reaches its lower bound (entropy)? Specifically, for linear NTP models trained using gradient descent (GD), we make the following contributions: Firstly, we determine NTP-separability conditions on the data, under which GD can attain its lower bound. We also demonstrate that these conditions hold under overparameterization. Secondly, we establish that the parameters of GD projected onto an appropriate data subspace converge to the unique solution of a system of linear equations, which requires the logits' difference of in-support tokens to be equal to the log-ratio of their respective probabilities. Meanwhile, on the orthogonal subspace, the parameters diverge and converge in the direction of the solution of a max-margin quadratic program, minimizing the Euclidean norm of parameters satisfying the \NTP-separability conditions. Akin to prior research on implicit bias of one-hot classification, our work opens exciting avenues for future research that can lead to better understanding optimization, generalization and robustness principles of models trained with NTP.

Tumblr and WordPress are reportedly set to strike deals to sell user data to artificial intelligence companies OpenAI and Midjourney. It isn't clear which data will be included, but the report suggests Automattic may have overreached initially. An alleged internal post from Tumblr product manager Cyle Gage suggests Automattic prepared to send private or partner-related data that wasn't supposed to be included in the deal. The questionable content reportedly included private posts on public blog posts, deleted or suspended blogs, unanswered (therefore, not publicly posted) questions, private answers, posts marked explicit and content from premium partner blogs (like Apple's former music site). The internal post suggests Automattic's engineers are preparing a list of post IDs that should have been excluded.

OpenAI said in a filing in Manhattan federal court on Monday that the Times caused the technology to reproduce its material through "deceptive prompts that blatantly violate OpenAI's terms of use". "The allegations in the Times's complaint do not meet its famously rigorous journalistic standards," OpenAI said. "The truth, which will come out in the course of this case, is that the Times paid someone to hack OpenAI's products." OpenAI did not name the "hired gun" whom it said the Times used to manipulate its systems and did not accuse the newspaper of breaking any anti-hacking laws. Representatives for the New York Times and OpenAI did not immediately respond to requests for comment on the filing. The Times sued OpenAI and its largest financial backer, Microsoft, in December, accusing them of using millions of its articles without permission to train chatbots to provide information to users.

Microsoft Corp.'s Mistral AI investment is set to be analyzed by the European Union's competition watchdog at the same time that its deep ties to OpenAI Inc come under regulatory scrutiny. Mistral announced a "strategic partnership" with Microsoft on Monday that includes making the startup's latest artificial intelligence models available to customers of Microsoft's Azure cloud. Microsoft said the investment amounted to 15 million ( 16.3 million.) Mistral develops algorithmic models similar to those from OpenAI used for chatbots and other AI services, but Mistral models are shared openly. Microsoft's investments will convert into equity as part of Mistral's next funding round.