Recently, the demand for psychological counseling has significantly increased as more individuals express concerns about their mental health. This surge has accelerated efforts to improve the accessibility of counseling by using large language models (LLMs) as counselors. To ensure client privacy, training open-source LLMs faces a key challenge: the absence of realistic counseling datasets. To address this, we introduce Cactus, a multi-turn dialogue dataset that emulates real-life interactions using the goal-oriented and structured approach of Cognitive Behavioral Therapy (CBT). We create a diverse and realistic dataset by designing clients with varied, specific personas, and having counselors systematically apply CBT techniques in their interactions. To assess the quality of our data, we benchmark against established psychological criteria used to evaluate real counseling sessions, ensuring alignment with expert evaluations. Experimental results demonstrate that Camel, a model trained with Cactus, outperforms other models in counseling skills, highlighting its effectiveness and potential as a counseling agent. We make our data, model, and code publicly available.

The ability to perform complex tasks from detailed instructions is a key to many remarkable achievements of our species. As humans, we are not only capable of performing a wide variety of tasks but also very complex ones that may entail hundreds or thousands of steps to complete. Large language models and their more recent multimodal counterparts that integrate textual and visual inputs have achieved unprecedented success in performing complex tasks. Yet, most existing benchmarks are largely confined to single-modality inputs (either text or vision), narrowing the scope of multimodal assessments, particularly for instruction-following in multimodal contexts. To bridge this gap, we introduce the instructed-Virtual VISual Decision Making (iWISDM) environment engineered to generate a limitless array of vision-language tasks of varying complexity. Using iWISDM, we compiled three distinct benchmarks of instruction following visual tasks across varying complexity levels and evaluated several newly developed multimodal models on these benchmarks. Our findings establish iWISDM as a robust benchmark for assessing the instructional adherence of both existing and emergent multimodal models and highlight a large gap between these models' ability to precisely follow instructions with that of humans. A typical day in most people's lives involves hundreds or thousands of tasks. Most of which are performed without explicit attention. Just in between getting up and getting to work, one may have already performed 5-15 tasks (taking a shower, shaving, making coffee, getting dressed, etc.). Teaching artificial agents to perform similar seemingly mundane tasks has proven to be an extremely difficult computational problem (Konar, 2018). The challenge becomes more apparent when one realizes that each of these seemingly mundane tasks such as making coffee involves tens of steps/actions (Figure 1). The challenge becomes increasingly more significant once we consider more complex tasks such as operating a device or assembling a piece of furniture from its instruction manual. And yet, these tasks are performed proficiently by most individuals in most situations. Large Language Models (LLMs) have become increasingly capable of comprehending natural language across wide topics and contexts, allowing them to hold meaningful conversations, give expert advice, and analyze data among other features (Brown et al., 2020; Ouyang et al., 2022; Radford et al., 2019). In the meantime, their multimodal counterparts are starting to emerge, signalling broader application of such models across industries. Large Multimodal Models (LMMs) are generally capable of receiving and responding in a range of possible modalities including visual, text, and audio (Alayrac et al., 2022; Liu et al., 2023b; Achiam et al., 2023). These authors contributed equally to this work.

In this work, we focus on the alignment problem of diffusion models with a continuous reward function, which represents specific objectives for downstream tasks, such as improving human preference. The central goal of the alignment problem is to adjust the distribution learned by diffusion models such that the generated samples maximize the target reward function. We propose a novel alignment approach, named Direct Noise Optimization (DNO), that optimizes the injected noise during the sampling process of diffusion models. By design, DNO is tuning-free and prompt-agnostic, as the alignment occurs in an online fashion during generation. We rigorously study the theoretical properties of DNO and also propose variants to deal with non-differentiable reward functions. Furthermore, we identify that naive implementation of DNO occasionally suffers from the out-of-distribution reward hacking problem, where optimized samples have high rewards but are no longer in the support of the pretrained distribution. To remedy this issue, we leverage classical high-dimensional statistics theory and propose to augment the DNO loss with certain probability regularization. We conduct extensive experiments on several popular reward functions trained on human feedback data and demonstrate that the proposed DNO approach achieves state-of-the-art reward scores as well as high image quality, all within a reasonable time budget for generation.

Recently, various encoder-only and encoder-decoder pre-trained models like BERT and T5 have been applied to automatic essay scoring (AES) as small language models. However, existing studies have primarily treated this task akin to a classification problem, focusing solely on outputting scores in the target text without offering interpretations for the generated scores. Departing from the approaches, we introduce Reasoning Distillation-Based Evaluation (RDBE), which integrates interpretability to elucidate the rationale behind model scores while enhancing performance through initial reasoning. This interpretive capability is acquired during training by leveraging generated reasoning from a large language model (LLM) to distill a small language model (SLM). Our experimental results demonstrate the efficacy of RDBE across all scoring rubrics considered in the dataset. RDBE outperforms both zero-shot LLM generation and generation from a baseline fine-tuned model, establishing itself as state-of-the-art in the corresponding dataset. This highlights its practical interpretative output and enhanced performance.

AI agents have drawn increasing attention mostly on their ability to perceive environments, understand tasks, and autonomously achieve goals. To advance research on AI agents in mobile scenarios, we introduce the Android Multi-annotation EXpo (AMEX), a comprehensive, large-scale dataset designed for generalist mobile GUI-control agents. Their capabilities of completing complex tasks by directly interacting with the graphical user interface (GUI) on mobile devices are trained and evaluated with the proposed dataset. AMEX comprises over 104K high-resolution screenshots from 110 popular mobile applications, which are annotated at multiple levels. Unlike existing mobile device-control datasets, e.g., MoTIF, AitW, etc., AMEX includes three levels of annotations: GUI interactive element grounding, GUI screen and element functionality descriptions, and complex natural language instructions, each averaging 13 steps with stepwise GUI-action chains. We develop this dataset from a more instructive and detailed perspective, complementing the general settings of existing datasets. Additionally, we develop a baseline model SPHINX Agent and compare its performance across state-of-the-art agents trained on other datasets. To facilitate further research, we open-source our dataset, models, and relevant evaluation tools. The project is available at https://yuxiangchai.github.io/AMEX/

The rapid advancement of foundation models in medical imaging represents a significant leap toward enhancing diagnostic accuracy and personalized treatment. However, the deployment of foundation models in healthcare necessitates a rigorous examination of their trustworthiness, encompassing privacy, robustness, reliability, explainability, and fairness. The current body of survey literature on foundation models in medical imaging reveals considerable gaps, particularly in the area of trustworthiness. Additionally, extant surveys on the trustworthiness of foundation models fail to address their specific variations and applications within the medical imaging domain. This survey paper reviews the current research on foundation models in the major medical imaging applications, with a focus on segmentation, medical report generation, medical question and answering (Q&A), and disease diagnosis, which includes trustworthiness discussion in their manuscripts. We explore the complex challenges of making foundation models for medical image analysis trustworthy, associated with each application, and summarize the current concerns and strategies to enhance trustworthiness. Furthermore, we explore the future promises of these models in revolutionizing patient care. Our analysis underscores the imperative for advancing towards trustworthy AI in medical image analysis, advocating for a balanced approach that fosters innovation while ensuring ethical and equitable healthcare delivery.

Large Language Models (LLMs) have revolutionised natural language processing, exhibiting impressive human-like capabilities. In particular, LLMs are capable of "lying", knowingly outputting false statements. Hence, it is of interest and importance to develop methods to detect when LLMs lie. Indeed, several authors trained classifiers to detect LLM lies based on their internal model activations. However, other researchers showed that these classifiers may fail to generalise, for example to negated statements. In this work, we aim to develop a robust method to detect when an LLM is lying. To this end, we make the following key contributions: (i) We demonstrate the existence of a two-dimensional subspace, along which the activation vectors of true and false statements can be separated. Notably, this finding is universal and holds for various LLMs, including Gemma-7B, LLaMA2-13B and LLaMA3-8B. Our analysis explains the generalisation failures observed in previous studies and sets the stage for more robust lie detection; (ii) Building upon (i), we construct an accurate LLM lie detector. Empirically, our proposed classifier achieves state-of-the-art performance, distinguishing simple true and false statements with 94% accuracy and detecting more complex real-world lies with 95% accuracy.

In this work, we systematically expose and measure the inconsistency and knowledge gaps of Large Language Models (LLMs). Specifically, we propose an automated testing framework (called KONTEST) which leverages a knowledge graph to construct test cases. KONTEST probes and measures the inconsistencies in the LLM's knowledge of the world via a combination of semantically-equivalent queries and test oracles (metamorphic or ontological oracle). KONTEST further mitigates knowledge gaps via a weighted LLM model ensemble. Using four state-of-the-art LLMs (Falcon, Gemini, GPT3.5, and Llama2), we show that KONTEST generates 19.2% error inducing inputs (1917 errors from 9983 test inputs). It also reveals a 16.5% knowledge gap across all tested LLMs. KONTEST's mitigation method reduces LLM knowledge gap by 32.48%. Our ablation study further shows that GPT3.5 is not suitable for knowledge-based consistency testing because it is only 60%-68% effective in knowledge construction.

Another substantial as key historical resources, contain a diverse project is the "Digging into Data Challenge". A range of information about political, economic, part of the Transatlantic Partnership for Social Sciences and cultural processes and are abundant due to and Humanities 2016, this initiative yielded focused efforts to preserve them within national a vast collection of 19th-century press materials archives. Indeed, the discipline of Digital Humanities, known as "Atlas - Oceanic Exchanges. Tracing which emphasizes the incorporation of digital Global Information Networks in Historical Papers" tools in humanities and social sciences research, (Exchanges). Other significant works include "Viral has spent much of the past three decades on the Texts: Mapping Networks of Reprinting in 19th-task of digitization, resulting in a wealth of curated Century Newspapers and Magazines" (Cordell and digital collections (Berry and Fagerjord, 2017; Dobson, Smith), a project that investigates 19th-century journalistic 2019). However, digitizing these corpora has reports to understand the culture of reprinting brought plenty of challenges in transcribing the in the United States before the Civil War, and images into machine-readable texts.

Although engagement in self-directed wellness exercises typically declines over time, integrating social support such as coaching can sustain it. However, traditional forms of support are often inaccessible due to the high costs and complex coordination. Large Language Models (LLMs) show promise in providing human-like dialogues that could emulate social support. Yet, in-depth, in situ investigations of LLMs to support behavior change remain underexplored. We conducted two randomized experiments to assess the impact of LLM agents on user engagement with mindfulness exercises. First, a single-session study, involved 502 crowdworkers; second, a three-week study, included 54 participants. We explored two types of LLM agents: one providing information and another facilitating self-reflection. Both agents enhanced users' intentions to practice mindfulness. However, only the information-providing LLM, featuring a friendly persona, significantly improved engagement with the exercises. Our findings suggest that specific LLM agents may bridge the social support gap in digital health interventions.