Recitation-Augmented Language Models

We propose a new paradigm to help Large Language Models (LLMs) generate more accurate factual knowledge without retrieving from an external corpus, called RECITation-augmented gEneration (RECITE). Different from retrieval-augmented language models that retrieve relevant documents before generating the outputs, given an input, RECITE first recites one or several relevant passages from LLMs' own memory via sampling, and then produces the final answers. We show that RECITE is a powerful paradigm for knowledge-intensive NLP tasks. Specifically, we show that by utilizing recitation as the intermediate step, a recite-and-answer scheme can achieve new state-of-the-art performance in various closed-book question answering (CBQA) tasks. In experiments, we verify the effectiveness of RECITE on three pre-trained models (PaLM, UL2, and OPT) and three CBQA tasks (Natural Questions, TriviaQA, and HotpotQA)

A Trustworthy Automated Short-Answer Scoring System Using a New Dataset and Hybrid Transfer Learning Method

To measure the quality of student learning, teachers must conduct evaluations. One of the most efficient modes of evaluation is the short answer question. However, there can be inconsistencies in teacher-performed manual evaluations due to an excessive number of students, time demands, fatigue, etc. Consequently, teachers require a trustworthy system capable of autonomously and accurately evaluating student answers. Using hybrid transfer learning and student answer dataset, we aim to create a reliable automated short answer scoring system called Hybrid Transfer Learning for Automated Short Answer Scoring (HTL-ASAS). HTL-ASAS combines multiple tokenizers from a pretrained model with the bidirectional encoder representations from transformers. Based on our evaluation of the training model, we determined that HTL-ASAS has a higher evaluation accuracy than models used in previous studies. The accuracy of HTL-ASAS for datasets containing responses to questions pertaining to introductory information technology courses reaches 99.6%. With an accuracy close to one hundred percent, the developed model can undoubtedly serve as the foundation for a trustworthy ASAS system

TEMPERA: Test-Time Prompting via Reinforcement Learning

Careful prompt design is critical to the use of large language models in zero-shot or few-shot learning. As a consequence, there is a growing interest in automated methods to design optimal prompts. In this work, we propose Test-time Prompt Editing using Reinforcement learning (TEMPERA). In contrast to prior prompt generation methods, TEMPERA can efficiently leverage prior knowledge, is adaptive to different queries and provides an interpretable prompt for every query. To achieve this, we design a novel action space that allows flexible editing of the initial prompts covering a wide set of commonly-used components like instructions, few-shot exemplars, and verbalizers. The proposed method achieves significant gains compared with recent SoTA approaches like prompt tuning, AutoPrompt, and RLPrompt, across a variety of tasks including sentiment analysis, topic classification, natural language inference, and reading comprehension. Our method achieves 5.33x on average improvement in sample efficiency when compared to the traditional fine-tuning methods

Analysis of electromagnetic interference from power system processing and transmission components for Space Station Freedom

The goal of this research project was to analyze the potential effects of electromagnetic interference (EMI) originating from power system processing and transmission components for Space Station Freedom. The approach consists of four steps: (1) developing analytical tools (models and computer programs); (2) conducting parameterization (what if?) studies; (3) predicting the global space station EMI environment; and (4) providing a basis for modification of EMI standards

Geometry-based Direct Simulation for Multi-Material Soft Robots

Robots fabricated by soft materials can provide higher flexibility and thus better safety while interacting with natural objects with low stiffness such as food and human beings. However, as many more degrees of freedom are introduced, the motion simulation of a soft robot becomes cumbersome, especially when large deformations are presented. Moreover, when the actuation is defined by geometry variation, it is not easy to obtain the exact loads and material properties to be used in the conventional methods of deformation simulation. In this paper, we present a direct approach to take the geometric actuation as input and compute the deformed shape of soft robots by numerical optimization using a geometry-based algorithm. By a simple calibration, the properties of multiple materials can be modeled geometrically in the framework. Numerical and experimental tests have been conducted to demonstrate the performance of our approach on both cable-driven and pneumatic actuators in soft robotics

Enhancing Student Performance Prediction on Learnersourced Questions with SGNN-LLM Synergy

As an emerging education strategy, learnersourcing offers the potential for personalized learning content creation, but also grapples with the challenge of predicting student performance due to inherent noise in student-generated data. While graph-based methods excel in capturing dense learner-question interactions, they falter in cold start scenarios, characterized by limited interactions, as seen when questions lack substantial learner responses. In response, we introduce an innovative strategy that synergizes the potential of integrating Signed Graph Neural Networks (SGNNs) and Large Language Model (LLM) embeddings. Our methodology employs a signed bipartite graph to comprehensively model student answers, complemented by a contrastive learning framework that enhances noise resilience. Furthermore, LLM's contribution lies in generating foundational question embeddings, proving especially advantageous in addressing cold start scenarios characterized by limited graph data interactions. Validation across five real-world datasets sourced from the PeerWise platform underscores our approach's effectiveness. Our method outperforms baselines, showcasing enhanced predictive accuracy and robustness

Reduced acquisition and reactivation of human papillomavirus infections among older women treated with cryotherapy: results from a randomized trial in South Africa

<p>Abstract</p> <p>Background</p> <p>Treatment of women for high-grade cervical cancer precursors frequently results in clearance of the associated high-risk human papillomavirus (hrHPV) infection but the role of treatment among women without hrHPV is unknown. We investigated whether cervical cryotherapy reduces newly detected hrHPV infections among HIV-positive and HIV-negative women who were hrHPV negative when treated.</p> <p>Methods</p> <p>The impact of cryotherapy on newly detected hrHPV infections was examined among 612 women of known HIV serostatus, aged 35 to 65 years, who were negative for hrHPV DNA, and randomized to either undergo cryotherapy (n = 309) or not (n = 303). All women underwent repeat hrHPV DNA testing 6, 12, 24, and 36 months later.</p> <p>Results</p> <p>Among 540 HIV-negative women, cryotherapy was associated with a significant reduction in newly detected hrHPV infections. Women in the cryotherapy group were 55% less likely to have newly detected hrHPV than women in the control group (95% CI 0.28 to 0.71). This association was independent of the influence of changes in sexual behaviors following therapy (adjusted hazards ratio (HR) = 0.49, 95% CI 0.29 to 0.81). Among 72 HIV-positive women, similar reductions were not observed (HR = 1.10, 95% CI 0.53 to 2.29).</p> <p>Conclusions</p> <p>Cervical cryotherapy significantly reduced newly detected hrHPV infections among HIV-negative, but not HIV-positive women. These results raise intriguing questions about immunological responses and biological mechanisms underlying the apparent prophylactic benefits of cryotherapy.</p