Best-kk Search Algorithm for Neural Text Generation

Modern natural language generation paradigms require a good decoding strategy to obtain quality sequences out of the model. Beam search yields high-quality but low diversity outputs; stochastic approaches suffer from high variance and sometimes low quality, but the outputs tend to be more natural and creative. In this work, we propose a deterministic search algorithm balancing both quality and diversity. We first investigate the vanilla best-first search (BFS) algorithm and then propose the Best-$k$ Search algorithm. Inspired by BFS, we greedily expand the top $k$ nodes, instead of only the first node, to boost efficiency and diversity. Upweighting recently discovered nodes accompanied by heap pruning ensures the completeness of the search procedure. Experiments on four NLG tasks, including question generation, commonsense generation, text summarization, and translation, show that best-$k$ search yields more diverse and natural outputs compared to strong baselines, while our approach maintains high text quality. The proposed algorithm is parameter-free, lightweight, efficient, and easy to use.Comment: 17 page

Effectiveness of decision support tools on reducing antibiotic use for respiratory tract infections: a systematic review and meta-analysis

Background: Clinical decision support tools (CDSs) have been demonstrated to enhance the accuracy of antibiotic prescribing among physicians. However, their effectiveness in reducing inappropriate antibiotic use for respiratory tract infections (RTI) is controversial.Methods: A literature search in 3 international databases (Medline, Web of science and Embase) was conducted before 31 May 2023. Relative risk (RR) and corresponding 95% confidence intervals (CI) were pooled to evaluate the effectiveness of intervention. Summary effect sizes were calculated using a random-effects model due to the expected heterogeneity (I2 over 50%).Results: A total of 11 cluster randomized clinical trials (RCTs) and 5 before-after studies were included in this meta-analysis, involving 900,804 patients met full inclusion criteria. Among these studies, 11 reported positive effects, 1 reported negative results, and 4 reported non-significant findings. Overall, the pooled effect size revealed that CDSs significantly reduced antibiotic use for RTIs (RR = 0.90, 95% CI = 0.85 to 0.95, I2 = 96.10%). Subgroup analysis indicated that the intervention duration may serve as a potential source of heterogeneity. Studies with interventions duration more than 2 years were found to have non-significant effects (RR = 1.00, 95% CI = 0.96 to 1.04, I2 = 0.00%). Egger’s test results indicated no evidence of potential publication bias (p = 0.287).Conclusion: This study suggests that CDSs effectively reduce inappropriate antibiotic use for RTIs among physicians. However, subgroup analysis revealed that interventions lasting more than 2 years did not yield significant effects. These findings highlight the importance of considering intervention duration when implementing CDSs.Systematic Review Registration:https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023432584, Identifier: PROSPERO (CRD42023432584)

Open Vocabulary Object Detection with Pseudo Bounding-Box Labels

Despite great progress in object detection, most existing methods work only on a limited set of object categories, due to the tremendous human effort needed for bounding-box annotations of training data. To alleviate the problem, recent open vocabulary and zero-shot detection methods attempt to detect novel object categories beyond those seen during training. They achieve this goal by training on a pre-defined base categories to induce generalization to novel objects. However, their potential is still constrained by the small set of base categories available for training. To enlarge the set of base classes, we propose a method to automatically generate pseudo bounding-box annotations of diverse objects from large-scale image-caption pairs. Our method leverages the localization ability of pre-trained vision-language models to generate pseudo bounding-box labels and then directly uses them for training object detectors. Experimental results show that our method outperforms the state-of-the-art open vocabulary detector by 8% AP on COCO novel categories, by 6.3% AP on PASCAL VOC, by 2.3% AP on Objects365 and by 2.8% AP on LVIS. Code is available at https://github.com/salesforce/PB-OVD.Comment: ECCV 202