Reviewerly/exHarmony huggingface dataset:
Factual or Contextual? Disentangling Error Types in Entity Description Generation

In the task of entity description generation, given a context and a specified entity, a model must describe that entity correctly and in a contextually-relevant way.In this task, as well as broader language generation tasks, the generation of a nonfactual description (factual error) versus an incongruous description (contextual error) is fundamentally different, yet often conflated.We develop an evaluation paradigm that enables us to disentangle these two types of errors in naturally occurring textual contexts.We find that factuality and congruity are often at odds, and that models specifically struggle with accurate descriptions of entities that are less familiar to people.This shortcoming of language models raises concerns around the trustworthiness of such models, since factual errors on less well-known entities are exactly those that a human reader will not recognize.
DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecasting

Stock trend forecasting is a fundamental task of quantitative investment where precise predictions of price trends are indispensable. As an online service, stock data continuously arrive over time. It is practical and efficient to incrementally update the forecast model with the latest data which may reveal some new patterns recurring in the future stock market. However, incremental learning for stock trend forecasting still remains under-explored due to the challenge of distribution shifts (a.k.a. concept drifts). With the stock market dynamically evolving, the distribution of future data can slightly or significantly differ from incremental data, hindering the effectiveness of incremental updates. To address this challenge, we propose DoubleAdapt, an end-to-end framework with two adapters, which can effectively adapt the data and the model to mitigate the effects of distribution shifts. Our key insight is to automatically learn how to adapt stock data into a locally stationary distribution in favor of profitable updates. Complemented by data adaptation, we can confidently adapt the model parameters under mitigated distribution shifts. We cast each incremental learning task as a meta-learning task and automatically optimize the adapters for desirable data adaptation and parameter initialization. Experiments on real-world stock datasets demonstrate that DoubleAdapt achieves state-of-the-art predictive performance and shows considerable efficiency.
Self-Adaptive Perturbation Radii for Adversarial Training

Adversarial training has been shown to be the most popular and effective technique to protect models from imperceptible adversarial samples. Despite its success, it also accompanies the significant performance degeneration to clean data. To achieve a good performance on both clean and adversarial samples, the main effort is searching for an adaptive perturbation radius for each training sample. However, this method suffers from a conflict between exact searching and computational overhead. To address this conflict, in this paper, firstly we show the superiority of adaptive perturbation radii on the accuracy and robustness respectively. Then we propose our novel self-adaptive adjustment framework for perturbation radii without tedious searching. We also discuss this framework on both deep neural networks (DNNs) and kernel support vector machines (SVMs). Finally, extensive experimental results show that our framework can improve adversarial robustness without compromising the natural generalization. It is also competitive with existing searching strategies in terms of running time.
How to DP-fy ML: A Practical Tutorial to Machine Learning with Differential Privacy

Machine Learning (ML) models are ubiquitous in real world applications and are a constant focus of research. At the same time, the community has started to realize the importance of protecting the privacy of models' training data.
The Second Workshop on Knowledge-Augmented Methods for Natural Language Processing

Language models are being developed and deployed in many applications, "small"-scale and large-scale, generic and specialized, text-only and multimodal, etc. Meanwhile, the missingness of important knowledge causes limitations and safety challenges. The knowledge includes commonsense, world facts, domain expertise, personalization, and especially the unique patterns that need to be discovered from big data applications. Training and inference processes of the language models can be and should be augmented with the knowledge. The first KnowledgeNLP at AAAI 2023 attracted scientists on knowledge augmentation methods towards higher language intelligence. This workshop offers a broad platform to share ideas and discuss various topics, such as (1) synergy between knowledge and language model, (2) scalable architectures that integrate NLP, knowledge graph, and graph learning technologies, (3) KnowledgeNLP for e-commerce, education, and healthcare, (4) human factors and social good in KnowledgeNLP.
Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning

Human activity recognition (HAR) is a time series classification task that focuses on identifying the motion patterns from human sensor readings. Adequate data is essential but a major bottleneck for training a generalizable HAR model, which assists customization and optimization of online web applications. However, it is costly in time and economy to collect large-scale labeled data in reality, i.e., the low-resource challenge. Meanwhile, data collected from different persons have distribution shifts due to different living habits, body shapes, age groups, etc. The low-resource and distribution shift challenges are detrimental to HAR when applying the trained model to new unseen subjects. In this paper, we propose a novel approach called Diverse and Discriminative representation Learning (DDLearn) for generalizable low-resource HAR. DDLearn simultaneously considers diversity and discrimination learning. With the constructed self-supervised learning task, DDLearn enlarges the data diversity and explores the latent activity properties. Then, we propose a diversity preservation module to preserve the diversity of learned features by enlarging the distribution divergence between the original and augmented domains. Meanwhile, DDLearn also enhances semantic discrimination by learning discriminative representations with supervised contrastive learning. Extensive experiments on three public HAR datasets demonstrate that our method significantly outperforms state-of-art methods by an average accuracy improvement of 9.5% under the low-resource distribution shift scenarios, while being a generic, explainable, and flexible framework. Code is available at: https://github.com/microsoft/robustlearn.
ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation

Paraphrase generation is a long-standing task in natural language processing (NLP).Supervised paraphrase generation models, which rely on human-annotated paraphrase pairs, are costinefficient and hard to scale up.On the other hand, automatically annotated paraphrase pairs (e.g., by machine back-translation), usually suffer from the lack of syntactic diversity -the generated paraphrase sentences are very similar to the source sentences in terms of syntax.In this work, we present PARAAMR, a large-scale syntactically diverse paraphrase dataset created by abstract meaning representation backtranslation.Our quantitative analysis, qualitative examples, and human evaluation demonstrate that the paraphrases of PARAAMR are syntactically more diverse compared to existing large-scale paraphrase datasets while preserving good semantic similarity.In addition, we show that PARAAMR can be used to improve on three NLP tasks: learning sentence embeddings, syntactically controlled paraphrase generation, and data augmentation for few-shot learning.Our results thus showcase the potential of PARAAMR for improving various NLP applications.
DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models

Gigantic pre-trained models have become central to natural language processing (NLP), serving as the starting point for fine-tuning towards a range of downstream tasks.However, two pain points persist for this paradigm: (a) as the pre-trained models grow bigger (e.g., 175B parameters for GPT-3), even the fine-tuning process can be time-consuming and computationally expensive; (b) the finetuned model has the same size as its starting point by default, which is neither sensible due to its more specialized functionality, nor practical since many fine-tuned models will be deployed in resource-constrained environments.To address these pain points, we propose a framework for resource-and parameterefficient fine-tuning by leveraging the sparsity prior in both weight updates and the final model weights.Our proposed framework, dubbed Dually Sparsity-Embedded Efficient Tuning (DSEE), aims to achieve two key objectives: (i) parameter efficient fine-tuning -by enforcing sparsity-aware low-rank updates on top of the pre-trained weights; and (ii) resourceefficient inference -by encouraging a sparse weight structure towards the final fine-tuned model.We leverage sparsity in these two directions by exploiting both unstructured and structured sparse patterns in pre-trained language models via a unified approach.Extensive experiments and in-depth investigations, with diverse network backbones (i.e., BERT, RoBERTa, and GPT-2) on dozens of datasets, consistently demonstrate impressive parameter-/inference-efficiency, while maintaining competitive downstream performance.For instance, DSEE saves about 25% inference FLOPs while achieving comparable performance, with 0.5% trainable parameters on BERT.Codes are available at https://github.com/VITA-Group/DSEE.
Sketch-Based Anomaly Detection in Streaming Graphs

Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges and subgraphs in an online manner, for the purpose of detecting unusual behavior, using constant time and memory? For example, in intrusion detection, existing work seeks to detect either anomalous edges or anomalous subgraphs, but not both. In this paper, we first extend the count-min sketch data structure to a higher-order sketch. This higher-order sketch has the useful property of preserving the dense subgraph structure (dense subgraphs in the input turn into dense submatrices in the data structure). We then propose 4 online algorithms that utilize this enhanced data structure, which (a) detect both edge and graph anomalies; (b) process each edge and graph in constant memory and constant update time per newly arriving edge, and; (c) outperform state-of-the-art baselines on 4 real-world datasets. Our method is the first streaming approach that incorporates dense subgraph search to detect graph anomalies in constant memory and time.
Fragility Index: A New Approach for Binary Classification

In binary classification problems, many performance metrics evaluate the probability that some error exceeds a threshold. Nevertheless, they focus more on the probability and fail to capture the magnitude of the error, which evaluates how large this error exceeds the threshold. Capturing the magnitude of error is desired in many applications. For example, in detecting disease and predicting credit default, the magnitude of error illustrates the confidence in making the wrong prediction. We propose a novel metric, the Fragility Index (FI), to evaluate the performance of binary classifiers by capturing the magnitude of the error. FI alleviates the risk of misclassification by penalizing the large error greatly, which is seldom considered by standard metrics. Moreover, to strengthen the generalization ability and handle unseen samples, we adopt the framework of distributionally robust optimization and robust satisficing, which allows us to derive and control the maximum degree of fragility of the classifier when the distribution of samples shifts. We show that FI can be easily calculated and optimized for common probabilistic distance measures. Experiments with real datasets demonstrate the new insights brought by FI and the advantages of classifiers selected under FI, which always improve the robustness and reduce the risk of large errors as compared to classifiers selected by alternative metrics.
Shilling Black-box Review-based Recommender Systems through Fake Review Generation

Review-Based Recommender Systems (RBRS) have attracted increasing research interest due to their ability to alleviate well-known cold-start problems. RBRS utilizes reviews to construct the user and items representations. However, in this paper, we argue that such a reliance on reviews may instead expose systems to the risk of being shilled. To explore this possibility, in this paper, we propose the first generation-based model for shilling attacks against RBRSs. Specifically, we learn a fake review generator through reinforcement learning, which maliciously promotes items by forcing prediction shifts after adding generated reviews to the system. By introducing the auxiliary rewards to increase text fluency and diversity with the aid of pre-trained language models and aspect predictors, the generated reviews can be effective for shilling with high fidelity. Experimental results demonstrate that the proposed framework can successfully attack three different kinds of RBRSs on the Amazon corpus with three domains and Yelp corpus. Furthermore, human studies also show that the generated reviews are fluent and informative. Finally, equipped with Attack Review Generators (ARGs), RBRSs with adversarial training are much more robust to malicious reviews.
OpenTIPE: An Open-source Translation Framework for Interactive Post-Editing Research

Despite the latest improvements on machine translation, professional translators still must review and post-edit the automatic output to ensure high-quality translations.The research on automating this process lacks an interactive post-editing environment implemented for this purpose; therefore, current approaches do not consider the human interactions that occur in real post-editing scenarios.To address this issue, we present OpenTIPE, a flexible and extensible framework that aims at supporting research on interactive post-editing.Specifically, the interactive environment of OpenTIPE allows researchers to explore human-centered approaches for the post-editing task.We release the OpenTIPE source code 1 and showcase its main functionalities with a demonstration video 2 and an online live demo. 3
Macular: A Multi-Task Adversarial Framework for Cross-Lingual Natural Language Understanding

Cross-lingual natural language understanding~(NLU) aims to train NLU models on a source language and apply the models to NLU tasks in target languages, and is a fundamental task for many cross-language applications. Most of the existing cross-lingual NLU models assume the existence of parallel corpora so that words and sentences in source and target languages could be aligned. However, the construction of such parallel corpora is expensive and sometimes infeasible. Motivated by this challenge, recent works propose data augmentation or adversarial training methods to reduce the reliance on external parallel corpora. In this paper, we propose an orthogonal and novel perspective to tackle this challenging cross-lingual NLU task (i.e., when parallel corpora are unavailable). We propose to conduct multi-task learning across different tasks for mutual performance improvement on both source and target languages. The proposed multi-task learning framework is complementary to existing studies and could be integrated with existing methods to further improve their performance on challenging cross-lingual NLU tasks.
PERSONALIZATION: Why the relational modes between Generative AI chatbots and human users are critical factors for product design and safety

Our experience of Generative AI platforms is through applications such as ChatGPT, a general purpose chatbot that can capably respond to a broad diversity of natural language prompts. Generative AI chatbots are designed to be conversational applications. As such, their ability to keep us engaged depends in part on how suitably personalized their outputs are to both our prompts and our underlying motivations. Correspondingly, reported instances of adverse user experiences with chatbots have involved outputs that are personalized in coercive or disturbing ways. As a design focus for Generative AI chatbots, personalization also brings with it psychological safety risks. Research has long shown that humans are highly susceptible to anthropomorphizing computation systems with conversational capabilities, an important facet of understanding why chatbots can both satisfy and threaten a user’s sense of well-being. Recent studies reveal that Generative AI Chatbots can be highly proficient at understanding and appealing to distinct human personality traits. Nonetheless, such chatbots are trained on corpora that include toxic, abusive, and manipulative language, and this is the primary source of psychological safety risk for users predisposed to anthropomorphizing conversational computation systems. Given the current AI industry trajectory to deepen user engagement with chatbots, it is important to consider both the promises and perils of personalization. In this regard, I present a categorization of Generative AI chatbots based on the extent to which their design and use is premised on personalized user experiences, with examples of in-market chatbots that fall into each category. I then present a rubric for thinking about the psychological safety risks that may be posed by a Generative AI chatbot, considering the extent to which the chatbot is designed to provide, or its users intend to experience, personalized interactions. I conclude by examining the possibilities for deepening personalization proficiency of chatbots with different types of user data and the issues these raise for further research, legal compliance, industry best practices development, and public policy development.
GAT-MF: Graph Attention Mean Field for Very Large Scale Multi-Agent Reinforcement Learning

Recent advancements in reinforcement learning have witnessed remarkable achievements by intelligent agents ranging from game-playing to industrial applications. Of particular interest is the area of multi-agent reinforcement learning (MARL), which holds significant potential for real-world scenarios. However, typical MARL methods are limited in their ability to handle tens of agents, leaving scenarios with up to hundreds or even thousands of agents almost unexplored. The scaling up of the number of agents presents two primary challenges: (1) agent-agent interactions are crucial in multi-agent systems while the number of interactions grows quadratically with the number of agents, resulting in substantial computational complexity and difficulty in strategies-learning; (2) the strengths of interactions among agents exhibit variations both across agents and over time, making it difficult to precisely model such interactions. In this paper, we propose a novel approach named Graph Attention Mean Field (GAT-MF). By converting agent-agent interactions into interactions between each agent and a weighted mean field, we achieve a substantial reduction in computational complexity. The proposed method offers a precise modeling of interaction dynamics with mathematical proofs of its correctness. Additionally, we design a graph attention mechanism to automatically capture the diverse and time-varying strengths of interactions, ensuring an accurate representation of agent interactions. Through extensive experimentation conducted in both manual and real-world scenarios involving over 3000 agents, we validate the efficacy of our method. The results demonstrate that our method outperforms the best baseline method with a remarkable improvement of 42.7%. Furthermore, our method saves 86.4% training time and 19.2% GPU memory compared to the best baseline method. For reproducibility, our source codes and data are available at https://github.com/tsinghua-fib-lab/Large-Scale-MARL-GATMF.
Robustness Certification for Structured Prediction with General Inputs via Safe Region Modeling in the Semimetric Output Space

Many real-world machine learning problems involve structured prediction beyond categorical labels. However, most existing robustness certification works are devoted to the classification case. It remains open for robustness certification for more general outputs. In this paper, we propose a novel framework of robustness certification for structured prediction problems, where the output space is modeled as a semimetric space with a distance function that satisfies non-negativity and symmetry but not necessarily the triangle inequality. We further develop our tailored certification methods for binary, numerical, and hybrid inputs in structured prediction. Experiment results show that our method achieves tighter robustness guarantees than the SOTA structured certification baseline for numerical inputs (for which it only supports) with ℓ2 norm perturbation when outputs are measured by intersection over union (IoU) similarity, total variation distance, and perceptual distance. Moreover, we achieve good robustness certification for binary inputs with ℓ0 norm perturbation and hybrid inputs with corresponding perturbation when outputs are measured by Manhattan distance.
Accelerating Personalized PageRank Vector Computation

Personalized PageRank Vectors are widely used as fundamental graph-learning tools for detecting anomalous spammers, learning graph embeddings, and training graph neural networks. The well-known local FwdPush algorithm[5] approximates PPVs and has a sublinear rate of O(1 over αε). A recent study [51] found that when high precision is required, FwdPush is similar to the power iteration method, and its run time is pessimistically bounded by O(m over α log 1 over ε). This paper looks closely at calculating PPVs for both directed and undirected graphs. By leveraging the linear invariant property, we show that FwdPush is a variant of Gauss-Seidel and propose a Successive Over-Relaxation based method, FwdPushSOR to speed it up by slightly modifying FwdPush. Additionally, we prove FwdPush has local linear convergence rate O(vol (S) over α log 1 over ε) enjoying advantages of two existing bounds. We also design a new local heuristic push method that reduces the number of operations by 10-50 percent compared to FwdPush. For undirected graphs, we propose two momentum-based acceleration methods that can be expressed as one-line updates and speed up non-acceleration methods by O (1 / √ α). Our experiments on six real-world graph datasets confirm the efficiency of FwdPushSOR and the acceleration methods for directed and undirected graphs, respectively.
Uncertainties in Clear-Sky Solar Irradiance Modeling Induced by the Limited Availability of the Atmospheric Parameters

Abstract In the recent years, great progress has been made in developing terrestrial and satellite-based networks for the measurement of atmospheric parameters, which serve as inputs in clear sky parametric models. However, some parameters may still be missing from the model input due to small temporal sampling or lack of in situ data. This paper presents a case study on the accuracy of the clear-sky solar irradiance estimation by a parametric model, when the availability of inputs is limited. The study was conducted with high-quality radiometric and atmospheric data recorded by the INOE-Magurele station, Romania. Seven different scenarios were studied. In each scenario the clear-sky solar irradiance model was run assuming that certain parameters are missing from the input. The results quantitatively confirm the dominant role of aerosols in establishing the accuracy of solar irradiance estimation under clear sky conditions. The unavailability of the measured Ångström turbidity coefficient may lead to a twofold/threefold increase in the uncertainty of direct-normal/diffuse solar irradiance estimation.