Cappy: Outperforming and boosting large multi-task language models with a small scorer - Related to large, cappy:, boosting, language, scorer

Cappy: Outperforming and boosting large multi-task language models with a small scorer

Due to the complexity of understanding and solving various tasks solely using instructions, the size of multi-task LLMs typically spans from several billion parameters to hundreds of billions ([website], FLAN-11B, T0-11B and OPT-IML-175B). As a result, operating such sizable models poses significant challenges because they demand considerable computational power and impose substantial requirements on the memory capacities of GPUs and TPUs, making their training and inference expensive and inefficient. Extensive storage is required to maintain a unique LLM copy for each downstream task. Moreover, the most powerful multi-task LLMs ([website], FLAN-PaLM-540B) are closed-sourced, making them impossible to be adapted. However, in practical applications, harnessing a single multi-task LLM to manage all conceivable tasks in a zero-shot manner remains difficult, particularly when dealing with complex tasks, personalized tasks and those that cannot be succinctly defined using instructions. On the other hand, the size of downstream training data is usually insufficient to train a model well without incorporating rich prior knowledge. Hence, it is long desired to adapt LLMs with downstream supervision while bypassing storage, memory, and access issues.

Certain parameter-efficient tuning strategies, including prompt tuning and adapters, substantially diminish storage requirements, but they still perform back-propagation through LLM parameters during the tuning process, thereby keeping their memory demands high. Additionally, some in-context learning techniques circumvent parameter tuning by integrating a limited number of supervised examples into the instruction. However, these techniques are constrained by the model's maximum input length, which permits only a few samples to guide task resolution.

In “Cappy: Outperforming and Boosting Large Multi-Task LMs with a Small Scorer”, presented at NeurIPS 2023, we propose a novel approach that enhances the performance and efficiency of multi-task LLMs. We introduce a lightweight pre-trained scorer, Cappy, based on continual pre-training on top of RoBERTa with merely 360 million parameters. Cappy takes in an instruction and a candidate response as input, and produces a score between 0 and 1, indicating an estimated correctness of the response with respect to the instruction. Cappy functions either independently on classification tasks or serves as an auxiliary component for LLMs, boosting their performance. Moreover, Cappy efficiently enables downstream supervision without requiring any finetuning, which avoids the need for back-propagation through LLM parameters and reduces memory requirements. Finally, adaptation with Cappy doesn’t require access to LLM parameters as it is compatible with closed-source multi-task LLMs, such as those only accessible via WebAPIs.

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Computer-aided diagnosis for lung cancer screening

Lung cancer is the leading cause of cancer-related deaths globally with [website] million deaths reported in 2020. Late diagnosis dramatically reduces the chances of survival. Lung cancer screening via computed tomography (CT), which provides a detailed 3D image of the lungs, has been shown to reduce mortality in high-risk populations by at least 20% by detecting potential signs of cancers earlier. In the US, screening involves annual scans, with some countries or cases recommending more or less frequent scans.

The United States Preventive Services Task Force lately expanded lung cancer screening recommendations by roughly 80%, which is expected to increase screening access for women and racial and ethnic minority groups. However, false positives ([website], incorrectly reporting a potential cancer in a cancer-free patient) can cause anxiety and lead to unnecessary procedures for patients while increasing costs for the healthcare system. Moreover, efficiency in screening a large number of individuals can be challenging depending on healthcare infrastructure and radiologist availability.

At Google we have previously developed machine learning (ML) models for lung cancer detection, and have evaluated their ability to automatically detect and classify regions that show signs of potential cancer. Performance has been shown to be comparable to that of specialists in detecting possible cancer. While they have achieved high performance, effectively communicating findings in realistic environments is necessary to realize their full potential.

To that end, in “Assistive AI in Lung Cancer Screening: A Retrospective Multinational Study in the US and Japan”, , we investigate how ML models can effectively communicate findings to radiologists. We also introduce a generalizable user-centric interface to help radiologists leverage such models for lung cancer screening. The system takes CT imaging as input and outputs a cancer suspicion rating using four categories (no suspicion, probably benign, suspicious, highly suspicious) along with the corresponding regions of interest. We evaluate the system’s utility in improving clinician performance through randomized reader studies in both the US and Japan, using the local cancer scoring systems (Lung-RADSs [website] and Sendai Score) and image viewers that mimic realistic settings. We found that reader specificity increases with model assistance in both reader studies. To accelerate progress in conducting similar studies with ML models, we have open-sourced code to process CT images and generate images compatible with the picture archiving and communication system (PACS) used by radiologists.

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Health-specific embedding tools for dermatology and pathology

In “Domain-specific optimization and diverse evaluation of self-supervised models for histopathology”, we showed that self-supervised learning (SSL) models for pathology images outperform traditional pre-training approaches and enable efficient training of classifiers for downstream tasks. This effort focused on hematoxylin and eosin (H&E) stained slides, the principal tissue stain in diagnostic pathology that enables pathologists to visualize cellular features under a microscope. The performance of linear classifiers trained using the output of the SSL models matched that of prior DL models trained on orders of magnitude more labeled data.

Due to substantial differences between digital pathology images and “natural image” photos, this work involved several pathology-specific optimizations during model training. One key element is that whole-slide images (WSIs) in pathology can be 100,000 pixels across (thousands of times larger than typical smartphone photos) and are analyzed by experts at multiple magnifications (zoom levels). As such, the WSIs are typically broken down into smaller tiles or patches for computer vision and DL applications. The resulting images are information dense with cells or tissue structures distributed throughout the frame instead of having distinct semantic objects or foreground vs. background variations, thus creating unique challenges for robust SSL and feature extraction. Additionally, physical ([website], cutting) and chemical ([website], fixing and staining) processes used to prepare the samples can influence image appearance dramatically.

Taking these key aspects into consideration, pathology-specific SSL optimizations included helping the model learn stain-agnostic aspects, generalizing the model to patches from multiple magnifications, augmenting the data to mimic scanning and image post processing, and custom data balancing to improve input heterogeneity for SSL training. These approaches were extensively evaluated using a broad set of benchmark tasks involving 17 different tissue types over 12 different tasks.

Utilizing the vision transformer (ViT-S/16) architecture, Path Foundation was selected as the best performing model from the optimization and evaluation process described above (and illustrated in the figure below). This model thus provides an key balance between performance and model size to enable valuable and scalable use in generating embeddings over the many individual image patches of large pathology WSIs.

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