Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of task-specific training examples needed to adapt the model to a particular application. To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model (PaLM).We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML system which enables highly efficient training across multiple TPU Pods. We demonstrate continued benefits of scaling by achieving state-ofthe-art few-shot learning results on hundreds of language understanding and generation benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough performance, outperforming the finetuned stateof-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark. A significant number of BIG-bench tasks showed discontinuous improvements from model scale, meaning that performance steeply increased as we scaled to our largest model. PaLM also has strong capabilities in multilingual tasks and source code generation, which we demonstrate on a wide array of benchmarks. We additionally provide a comprehensive analysis on bias and toxicity, and study the extent of training data memorization with respect to model scale. Finally, we discuss the ethical considerations related to large language models and discuss potential mitigation strategies. * Equal Contribution. Author contributions and ordering details are listed in Appendix A.

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Machin e lear nin g (ML) is incr easingly being use d in image retrieval systems for medical decision making. On e app lication of ML is to retrieve visually similar medical images from pas t patients (e.g. tissue from biops ies) to reference whe n making a medical decision with a new pat ient. Howeve r, no algorithm can perfectly captu re an expert ' s ideal notion of similarity for every case: an image th at is algorithmi cally determin ed to be similar may not be medically relevant to a doctor' s specific diagnostic needs. In this pape r, we identified the needs of patho logists whe n searchin g for similar images retrieved usin g a deep lear nin g algorithm , and develope d tools that empower use rs to cope with the search algorithm on-the -fly, communi cating what types of similarity are most import ant at different moment s in time. In two evaluations with path ologists, we found th at th ese refinement tools increased the diagnos tic utility of images found and increased user trus t in the algorithm. Th e tools we re preferred over a traditi onal interface, without a loss in diagnostic accuracy. We also observe d that users adopted new str ategies whe n using refinement tools, re-purpos ing th em to test and understand the underlying algorithm and to disambiguate ML errors from their own errors. Taken togethe r, these findings inform futur e hum an-ML collabo rative systems for expe rt decision-m aking. CCS CONCEPTS• Human-centered computing --> Human computer interaction (HCI); KEYWORDS Human -AI int eraction ; machin e learnin g; clinical healthPermission to mak e digital or har d copies of part or all of this work for personal or classroom use is grant ed without fee provi ded that copies are not made or distributed for profit or commercial advanta ge and that copies bear this notice an d the full citation on the first page. Figure 1: Medical images contain a wide range of clinical features , such as cellular (1) and glandular morphology (2), interaction between components (3), processing artifacts (4), and many more. It can be difficult for a similar -image search algorithm to perfectly capture an expert's notion of similarity ,

The increasing availability of large institutional and public histopathology image datasets is enabling the searching of these datasets for diagnosis, research, and education. Although these datasets typically have associated metadata such as diagnosis or clinical notes, even carefully curated datasets rarely contain annotations of the location of regions of interest on each image. As pathology images are extremely large (up to 100,000 pixels in each dimension), further laborious visual search of each image may be needed to find the feature of interest. In this paper, we introduce a deep-learning-based reverse image search tool for histopathology images: Similar Medical Images Like Yours (SMILY). We assessed SMILY’s ability to retrieve search results in two ways: using pathologist-provided annotations, and via prospective studies where pathologists evaluated the quality of SMILY search results. As a negative control in the second evaluation, pathologists were blinded to whether search results were retrieved by SMILY or randomly. In both types of assessments, SMILY was able to retrieve search results with similar histologic features, organ site, and prostate cancer Gleason grade compared with the original query. SMILY may be a useful general-purpose tool in the pathologist’s arsenal, to improve the efficiency of searching large archives of histopathology images, without the need to develop and implement specific tools for each application.

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