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1 - 15 of 46 publications
QueryForm: A Simple Zero-shot Form Entity Query Framework
Jacob Devlin
Hao Zhang
Jennifer Dy
ACL (2023)
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Zero-shot transfer learning for document understanding is a crucial yet under-investigated scenario to help reduce the high cost involved in annotating document entities. We present a novel query-based framework, QueryForm, that extracts entity values from form-like documents in a zero-shot fashion. QueryForm contains a dual prompting mechanism that composes both the document schema and a specific entity type into a query, which is used to prompt a Transformer model to perform a single entity extraction task. Furthermore, we propose to leverage large-scale query-entity pairs generated from form-like webpages with weak HTML annotations to pre-train QueryForm. By unifying pre-training and fine-tuning into the same query-based framework, QueryForm enables models to learn from structured documents containing various entities and layouts, leading to better generalization to target document types without the need for target-specific training data. QueryForm sets new state-of-the-art average F1 score on both the XFUND (+4.6%~10.1%) and the Payment (+3.2%~9.5%) zero-shot benchmark, with a smaller model size and no additional image input.
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Prefix Conditioning Unifies Language and Label Supervision
Kuniaki Saito
Xiang Zhang
Chun-Liang Li
Kate Saenko
CVPR (2023)
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Vision-language contrastive learning suggests a new learning paradigm by leveraging a large amount of image-caption-pair data. The caption supervision excels at providing wide coverage in vocabulary that enables strong zero-shot image recognition performance. On the other hand, label supervision offers to learn more targeted visual representations that are label-oriented and can cover rare categories. To gain the complementary advantages of both kinds of supervision for contrastive image-caption pre-training, recent works have proposed to convert class labels into a sentence with pre-defined templates called prompts. However, a naive unification of the real caption and the prompt sentences could lead to a complication in learning, as the distribution shift in text may not be handled properly in the language encoder. In this work, we propose a simple yet effective approach to unify these two types of supervision using prefix tokens that inform a language encoder of the type of the input sentence (e.g., caption or prompt) at training time. Our method is generic and can be easily integrated into existing VL pre-training objectives such as CLIP or UniCL. In experiments, we show that this simple technique dramatically improves the performance in zero-shot image recognition accuracy of the pre-trained model.
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FormNetV2: Inductive Multimodal Graph Contrastive Learning for Form Document Information Extraction
Chun-Liang Li
Hao Zhang
Xiang Zhang
Nikolai Glushnev
Joshua Ainslie
Nan Hua
ACL (2023)
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The recent advent of self-supervised pre-training techniques has led to a surge in the use of multimodal learning in form document understanding. However, existing approaches that extend the mask language modeling to other modalities require careful multi-task tuning, complex reconstruction target designs, or additional pre-training data. In FormNetV2, we introduce a centralized multimodal graph contrastive learning strategy to unify self-supervised pre-training for all modalities in one loss. The graph contrastive objective maximizes the agreement of multimodal representations, providing a natural interplay for all modalities without special customization. In addition, we extract image features within the bounding box that joins a pair of tokens connected by a graph edge, capturing more targeted visual cues without loading a sophisticated and separately pre-trained image embedder. FormNetV2 establishes new state-of-the-art performance on FUNSD, CORD, SROIE and Payment benchmarks with a more compact model size.
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Accurate estimation of output quantiles is crucial in many use cases, where it is desired to model the range of possibility. Modeling target distribution at arbitrary quantile levels and at arbitrary input attribute levels are important to offer a comprehensive picture of the data, and requires the quantile function to be expressive enough. The quantile function describing the target distribution using quantile levels is critical for quantile regression. Althought various parametric forms for the distributions (that the quantile function specifies) can be adopted, an everlasting problem is selecting the most appropriate one that can properly approximate the data distributions. In this paper, we propose a non-parametric and data-driven approach, Neural Spline Search (NSS), to represent the observed data distribution without parametric assumptions. NSS is flexible and expressive for modeling data distributions by transforming the inputs with a series of monotonic spline regressions guided by symbolic operators. We demonstrate that NSS outperforms previous methods on synthetic, real-world regression and time-series forecasting tasks.
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SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch
Chun-Liang Li
Transactions on Machine Learning Research (TMLR) (2023)
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Semi-supervised anomaly detection is a common problem, as often the datasets containing anomalies are partially labeled. We propose a canonical framework: Semi-supervised Pseudo-labeler Anomaly Detection with Ensembling (SPADE) that isn't limited by the assumption that labeled and unlabeled data come from the same distribution. Indeed, the assumption is often violated in many applications -- for example, the labeled data may contain only anomalies unlike unlabeled data, or unlabeled data may contain different types of anomalies, or labeled data may contain only `easy-to-label' samples. SPADE utilizes an ensemble of one class classifiers as the pseudo-labeler to improve the robustness of pseudo-labeling with distribution mismatch. Partial matching is proposed to automatically select the critical hyper-parameters for pseudo-labeling without validation data, which is crucial with limited labeled data. SPADE shows state-of-the-art semi-supervised anomaly detection performance across a wide range of scenarios with distribution mismatch in both tabular and image domains. In some common real-world settings such as model facing new types of unlabeled anomalies, SPADE outperforms the state-of-the-art alternatives by 5% AUC in average.
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Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image Retrieval
Kuniaki Saito
Xiang Zhang
Chun-Liang Li
Kate Saenko
CVPR (2023)
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In Composed Image Retrieval (CIR), a user combines a query image with text to describe their intended target. Existing methods rely on supervised learning of CIR models using labeled triplets consisting of the query image, text specification, and the target image. Labeling such triplets is expensive and hinders broad applicability of CIR. In this work, we propose to study an important task, Zero-Shot Composed Image Retrieval (ZS-CIR), whose goal is to build a CIR model without requiring labeled triplets for training. To this end, we propose a novel method, called Pic2Word, that requires only weakly labeled image-caption pairs and unlabeled image datasets to train. Unlike existing supervised CIR models, our model trained on weakly labeled or unlabeled datasets shows strong generalization across diverse ZS-CIR tasks, e.g., attribute editing, object composition, and domain conversion. Our approach outperforms several supervised CIR methods on the common CIR benchmark, CIRR and Fashion-IQ.
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Better Zero-Shot Reasoning with Self-Adaptive Prompting
Findings of the Association for Computational Linguistics (ACL) (2023)
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Modern large language models (LLMs) have demonstrated impressive capabilities at sophisticated tasks, often through step-by-step reasoning similar to humans. This is made possible by their strong few-shot and zero shot abilities: they either learn from a handful of handcrafted, completed responses (“in context examples”), or are prompted to reason spontaneously through specially designed triggers. Nonetheless, few-shot performance is sensitive to the choice of the examples, for which artisanal hand-crafted selection would require extensive effort, and in some cases, it might not even be possible to obtain relevant examples a-priori without expertise about the downstream tasks. On the other hand, most general and handcrafting-free, zero-shot performance is limited by the lack of guidance to the LLM. To address this, we propose Consistency-based Self-adaptive Prompting (COSP), a novel prompt design method for LLMs. Requiring neither handcrafted responses nor ground-truth labels, COSP selects & builds the set of examples from the LLM’s own zero-shot outputs via carefully designed criteria combining consistency, diversity and repetition. In zero-shot setting, with only LLM predictions, COSP significantly improves performance (up to 2× compared to zero-shot baselines and matching or exceeding few-shot baselines) in a range of reasoning tasks in 3 LLMs. Moreover, COSP can be generalized to few-shot setting and can take advantage of few labeled examples in an efficient way
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In this paper, we propose a novel deep sequence model based on the Koopman theory for time series forecasting with distribution shifts. Our model, Koopman Neural Forecaster (KNF), leverages DNNs to learn the linear Koopman space and the measurement functions, and imposes inductive biases for improved robustness against distributional shifts. KNF employs both a global operator to learn shared characteristics, and a local operator to capture changing dynamics. KNF also includes a judiciously-designed feedback loop to continuously update the learnt operators over time for rapidly varying behaviors. To the best of our knowledge, this is the first time that Koopman theory is applied to real-world time series without known governing laws. We demonstrate that KNF achieves the state-of-the-art performance on wide range of time series datasets that are particularly known to suffer from distribution shifts.
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Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes
Cheng-Yu Hsieh
Chun-Liang Li
Alexander Ratner
Ranjay Krishna
ACL 2023
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Deploying large language models (LLMs) is challenging because they are memory inefficient and compute-intensive for practical applications. In reaction, researchers train smaller task-specific models by either finetuning with human labels or distilling using LLM-generated labels. However, finetuning and distillation require large amounts of training data to achieve comparable performance to LLMs. We introduce Distilling step-by-step, a new mechanism that (a) trains smaller models that outperform LLMs, and (b) achieves so by leveraging less training data needed by finetuning or distillation. Our method extracts LLM rationales as additional supervision for small models within a multi-task training framework. We present three findings across 4 NLP benchmarks: First, compared to both finetuning and distillation, our mechanism achieves better performance with much fewer labeled/unlabeled training examples. Second, compared to LLMs, we achieve better performance using substantially smaller model sizes. Third, we reduce both the model size and the amount of data required to outperform LLMs; our 770M T5 model outperforms the 540B PaLM model using only 80% of available data on a benchmark task.
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We study anomaly clustering, grouping data into coherent clusters of anomaly types. This is different from anomaly detection that aims to divide anomalies from normal data.Unlike object-centered image clustering, anomaly clustering is particularly challenging as anomalous patterns are subtle and local. We present a simple yet effective clustering framework using a patch-based pretrained deep embeddings and off-the-shelf clustering methods. We define a distance function between images, each of which is represented as a bag of embeddings, by the Euclidean distance between weighted averaged embeddings. The weight defines the importance of instances (i.e., patch embeddings) in the bag, which may highlight defective regions. We compute weights in an unsupervised way or in a semi-supervised way when labeled normal data is available. Extensive experimental studies show the effectiveness of the proposed clustering framework along with a novel distance function upon existing multiple instance or deep clustering frameworks. Overall, our framework achieves 0.451 and 0.674 normalized mutual information scores on MVTec object and texture categories and further improve with a few labeled normal data(0.577, 0.669), far exceeding the baselines (0.244, 0.273)or state-of-the-art deep clustering methods (0.176, 0.277).
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While remarkable progress has been made in imbalanced supervised learning, less attention has been given to the setting of imbalanced semi-supervised learning (SSL) where not only are few labeled data provided, but the underlying data distribution can be severely imbalanced. Recent work requires both complicated sampling strategies of pseudo-labeled unlabeled data and distribution alignment of the pseudo-label distribution to accommodate this imbalance. We present a novel approach that relies only on a form of a distribution alignment but no sampling strategy where rather than aligning the pseudo-labels during inference, we move the distribution alignment component into the respective cross entropy loss computations for both the supervised and unsupervised losses. This alignment compensates for both imbalance in the data and the eventual distributional shift present during evaluation. Altogether, this provides a unified strategy that offers both significantly reduced training requirements and improved performance across both low and richly labeled regimes and over varying degrees of imbalance. In experiments, we validate the efficacy of our method on SSL variants of CIFAR10-LT, CIFAR100-LT, and ImageNet-127. On ImageNet-127, our method shows 1.6% accuracy improvement over CReST with an 80% training time reduction and is competitive with other SOTA methods.
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We propose a canonical approach for feature selection, sparse learnable masks (SLM). SLM integrates learnable sparse masks into end-to-end training. For the fundamental non-differentiability challenge of selecting a desired number of features, we propose duo mechanisms for automatic mask scaling to achieve the desired feature sparsity, and gradually tempering this sparsity for effective learning.
In addition, SLM employs a novel objective that maximizes the mutual information (MI) between the selected features and the labels, in an efficient and scalable way. Empirically, SLM achieves state-of-the-art results on several benchmark datasets, often by a significant margin, especially on real-world challenging datasets.
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FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction
Chun-Liang Li
Nan Hua
Joshua Ainslie
Association for Computational Linguistics (ACL) (2022)
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Sequence modeling has demonstrated state-of-the-art performance on natural language and document understanding tasks. However, it is challenging to correctly serialize tokens in form-like documents in practice due to their variety of layout patterns. We propose FormNet, a structure-aware sequence model to mitigate the suboptimal serialization of forms. First, we design Rich Attention that leverages the spatial relationship between tokens in a form for more precise attention score calculation. Second, we construct Super-Tokens for each word by embedding representations from their neighboring tokens through graph convolutions. FormNet therefore explicitly recovers local syntactic information that may have been lost during serialization. In experiments, FormNet outperforms existing methods with a more compact model size and less pre-training data, establishing new state-of-the-art performance on CORD, FUNSD and Payment benchmarks.
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Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual Understanding
Han Zhang
Ting Chen
AAAI Conference on Artificial Intelligence (AAAI), 2022
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Hierarchical structures are popular in recent vision transformers, however, they require sophisticated designs and massive datasets to work well.
In this paper, we explore the idea of nesting basic local transformers on non-overlapping image blocks and aggregating them in a hierarchical way.
We find that the block aggregation function plays a critical role in enabling cross-block non-local information communication.
This observation leads us to design a simplified architecture that requires minor code changes upon the original vision transformer.
The benefits of the proposed judiciously-selected design are threefold:
(1) NesT converges faster and requires much less training data to achieve good generalization on both ImageNet and small datasets like CIFAR;
(2) when extending our key ideas to image generation, NesT leads to a strong decoder that is 8$\times$ faster than previous transformer-based generators; and
(3) we show that decoupling the feature learning and abstraction processes via this nested hierarchy in our design enables constructing a novel method (named GradCAT) for visually interpreting the learned model. Source code is available https://github.com/google-research/nested-transformer.
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Learning to prompt for continual learning
Han Zhang
Xiaoqi Ren
Jennifer Dy
CVPR2022
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The mainstream paradigm behind continual learning has been to adapt the model parameters to non-stationary data distributions, where catastrophic forgetting is the central challenge. Typical methods rely on a rehearsal buffer or known task identity at test time to retrieve learned knowledge and address forgetting, while this work presents a new paradigm for continual learning that aims to train a more succinct memory system without accessing task identity at test time. Our method learns to dynamically prompt (L2P) a pre-trained model to learn tasks sequentially under different task transitions. In our proposed framework, prompts are small learnable parameters, which are maintained in a memory space. The objective is to optimize prompts to instruct the model prediction and explicitly manage task-invariant and task-specific knowledge while maintaining model plasticity. We conduct comprehensive experiments under popular image classification benchmarks with different challenging continual learning settings, where L2P consistently outperforms prior state-ofthe-art methods. Surprisingly, L2P achieves competitive results against rehearsal-based methods even without a rehearsal buffer and is directly applicable to challenging taskagnostic continual learning. Source code is available at https://github.com/google-research/l2p.
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