Active Prompting with Chain-of-Thought for Large Language Models

The increasing scale of large language models (LLMs) brings emergent abilities to various complex tasks requiring reasoning, such as arithmetic and commonsense reasoning. It is known that the effective design of task-specific prompts is critical for LLMs' ability to produce high-quality answers. In particular, an effective approach for complex question-and-answer tasks is example-based prompting with chain-of-thought (CoT) reasoning, which significantly improves the performance of LLMs. However, current CoT methods rely on a fixed set of human-annotated exemplars, which are not necessarily the most effective examples for different tasks. This paper proposes a new method, Active-Prompt, to adapt LLMs to different tasks with task-specific example prompts (annotated with human-designed CoT reasoning). For this purpose, we propose a solution to the key problem of determining which questions are the most important and helpful ones to annotate from a pool of task-specific queries. By borrowing ideas from the related problem of uncertainty-based active learning, we introduce several metrics to characterize the uncertainty so as to select the most uncertain questions for annotation. Experimental results demonstrate the superiority of our proposed method, achieving state-of-the-art on eight complex reasoning tasks. Further analyses of different uncertainty metrics, pool sizes, zero-shot learning, and accuracy-uncertainty relationship demonstrate the effectiveness of our method. Our code will be available at https://github.com/shizhediao/active-prompt.Comment: 20 pages, 3 figures, 11 table

On the Difference of BERT-style and CLIP-style Text Encoders

Masked language modeling (MLM) has been one of the most popular pretraining recipes in natural language processing, e.g., BERT, one of the representative models. Recently, contrastive language-image pretraining (CLIP) has also attracted attention, especially its vision models that achieve excellent performance on a broad range of vision tasks. However, few studies are dedicated to studying the text encoders learned by CLIP. In this paper, we analyze the difference between BERT-style and CLIP-style text encoders from three experiments: (i) general text understanding, (ii) vision-centric text understanding, and (iii) text-to-image generation. Experimental analyses show that although CLIP-style text encoders underperform BERT-style ones for general text understanding tasks, they are equipped with a unique ability, i.e., synesthesia, for the cross-modal association, which is more similar to the senses of humans.Comment: Natural Language Processing. 10 pages, 1 figure. Findings of ACL-202

Dentate Gyrus Integrity is Necessary for Behavioral Pattern Separation but not Statistical Learning

Pattern separation, the creation of distinct representations of similar inputs, and statistical learning, the rapid extraction of regularities across multiple inputs, have both been linked to hippocampal processing. It has been proposed that there may be functional differentiation within the hippocampus, such that the trisynaptic pathway (entorhinal cortex \u3e dentate gyrus \u3e CA3 \u3e CA1) supports pattern separation, whereas the monosynaptic pathway (entorhinal cortex \u3e CA1) supports statistical learning. To test this hypothesis, we investigated the behavioral expression of these two processes in BL, an individual with highly selective bilateral lesions in the dentate gyrus that presumably disrupts the trisynaptic pathway. We tested pattern separation with two novel auditory versions of the continuous Mnemonic Similarity Task, requiring the discrimination of similar environmental sounds and trisyllabic words. For statistical learning, participants were exposed to a continuous speech stream made up of repeating trisyllabic words. They were then tested implicitly through a reaction time-based task and explicitly through a rating task and a forced-choice recognition task. BL showed significant deficits in pattern separation on the Mnemonic Similarity Tasks and on the explicit rating measure of statistical learning. In contrast, BL showed intact statistical learning on the implicit measure and the familiarity-based forced-choice recognition measure. Together, these results suggest that dentate gyrus integrity is critical for high-precision discrimination of similar inputs, but not the implicit expression of statistical regularities in behaviour. Our findings offer unique new support for the view that pattern separation and statistical learning rely on distinct neural mechanisms

TeViS:Translating Text Synopses to Video Storyboards

A video storyboard is a roadmap for video creation which consists of shot-by-shot images to visualize key plots in a text synopsis. Creating video storyboards, however, remains challenging which not only requires cross-modal association between high-level texts and images but also demands long-term reasoning to make transitions smooth across shots. In this paper, we propose a new task called Text synopsis to Video Storyboard (TeViS) which aims to retrieve an ordered sequence of images as the video storyboard to visualize the text synopsis. We construct a MovieNet-TeViS dataset based on the public MovieNet dataset. It contains 10K text synopses each paired with keyframes manually selected from corresponding movies by considering both relevance and cinematic coherence. To benchmark the task, we present strong CLIP-based baselines and a novel VQ-Trans. VQ-Trans first encodes text synopsis and images into a joint embedding space and uses vector quantization (VQ) to improve the visual representation. Then, it auto-regressively generates a sequence of visual features for retrieval and ordering. Experimental results demonstrate that VQ-Trans significantly outperforms prior methods and the CLIP-based baselines. Nevertheless, there is still a large gap compared to human performance suggesting room for promising future work. The code and data are available at: \url{https://ruc-aimind.github.io/projects/TeViS/}Comment: Accepted to ACM Multimedia 202

Twenty-three medication-taking traits and stroke: A comprehensive Mendelian randomization study

BackgroundCertain medication categories may increase the risk of stroke. Nonetheless, the evidence regarding the causal relationship of medication-taking in promoting stroke and subtypes is deficient.MethodsWe evaluated the causal effect of a genetic predisposition for certain medication categories on stroke and subtypes (ischemic and hemorrhagic categories) by a two-sample Mendelian randomization (MR) analysis. Data for 23 medication categories were gathered from a genome-wide association study (GWAS) involving 318,177 patients. The Medical Research Council Integrative Epidemiology Unit Open GWAS database and the FinnGen consortium were used to gather GWAS data for stroke and subtypes. Inverse variance weighted, MR-Egger, and weighted median were used for the estimation of causal effects. Cochran's Q test, MR-Egger intercept test, and leave-one-out analysis were used for sensitivity analyses.ResultsTen medication categories were linked to a high stroke risk. Nine categories were linked to a high-risk ischemic stroke. Five categories were associated with small vessel ischemic stroke. Nine categories were positively associated with large artery atherosclerotic ischemic stroke. Three categories causally increased the possibility of cardioembolic ischemic stroke. Four categories were associated with intracerebral hemorrhage. Four categories were associated with nontraumatic intracranial hemorrhage. Three categories were causally associated with subarachnoid hemorrhage (SAH). Four categories were associated with the combination of SAH, unruptured cerebral aneurysm, and aneurysm operations SAH.ConclusionsThis study confirms that some medication categories lead to a greater risk of strokes. Meanwhile, it has an implication for stroke screening as well as direct clinical significance in the design of conduction of future randomized controlled trials