In patent prosecution, timely and effective responses to Office Actions (OAs) are crucial for acquiring patents, yet past automation and AI research have scarcely addressed this aspect. To address this gap, our study introduces the Patent Office Action Response Intelligence System (PARIS) and its advanced version, the Large Language Model Enhanced PARIS (LE-PARIS). These systems are designed to expedite the efficiency of patent attorneys in collaboratively handling OA responses. The systems' key features include the construction of an OA Topics Database, development of Response Templates, and implementation of Recommender Systems and LLM-based Response Generation. Our validation involves a multi-paradigmatic analysis using the USPTO Office Action database and longitudinal data of attorney interactions with our systems over six years. Through five studies, we examine the constructiveness of OA topics (studies 1 and 2) using topic modeling and the proposed Delphi process, the efficacy of our proposed hybrid recommender system tailored for OA (both LLM-based and non-LLM-based) (study 3), the quality of response generation (study 4), and the practical value of the systems in real-world scenarios via user studies (study 5). Results demonstrate that both PARIS and LE-PARIS significantly meet key metrics and positively impact attorney performance.

Social media bot detection has always been an arms race between advancements in machine learning bot detectors and adversarial bot strategies to evade detection. In this work, we bring the arms race to the next level by investigating the opportunities and risks of state-of-the-art large language models (LLMs) in social bot detection. To investigate the opportunities, we design novel LLM-based bot detectors by proposing a mixture-of-heterogeneous-experts framework to divide and conquer diverse user information modalities. To illuminate the risks, we explore the possibility of LLM-guided manipulation of user textual and structured information to evade detection. Extensive experiments with three LLMs on two datasets demonstrate that instruction tuning on merely 1,000 annotated examples produces specialized LLMs that outperform state-of-the-art baselines by up to 9.1% on both datasets, while LLM-guided manipulation strategies could significantly bring down the performance of existing bot detectors by up to 29.6% and harm the calibration and reliability of bot detection systems.

Machine unlearning has emerged as a new paradigm to deliberately forget data samples from a given model in order to adhere to stringent regulations. However, existing machine unlearning methods have been primarily focused on classification models, leaving the landscape of unlearning for generative models relatively unexplored. This paper serves as a bridge, addressing the gap by providing a unifying framework of machine unlearning for image-to-image generative models. Within this framework, we propose a computationally-efficient algorithm, underpinned by rigorous theoretical analysis, that demonstrates negligible performance degradation on the retain samples, while effectively removing the information from the forget samples. Empirical studies on two large-scale datasets, ImageNet-1K and Places-365, further show that our algorithm does not rely on the availability of the retain samples, which further complies with data retention policy. To our best knowledge, this work is the first that represents systemic, theoretical, empirical explorations of machine unlearning specifically tailored for image-to-image generative models. Our code is available at https://github.com/jpmorganchase/l2l-generator-unlearning.

In the evolving landscape of online communication, moderating hate speech (HS) presents an intricate challenge, compounded by the multimodal nature of digital content. This comprehensive survey delves into the recent strides in HS moderation, spotlighting the burgeoning role of large language models (LLMs) and large multimodal models (LMMs). Our exploration begins with a thorough analysis of current literature, revealing the nuanced interplay between textual, visual, and auditory elements in propagating HS. We uncover a notable trend towards integrating these modalities, primarily due to the complexity and subtlety with which HS is disseminated. A significant emphasis is placed on the advances facilitated by LLMs and LMMs, which have begun to redefine the boundaries of detection and moderation capabilities. We identify existing gaps in research, particularly in the context of underrepresented languages and cultures, and the need for solutions to handle low-resource settings. The survey concludes with a forward-looking perspective, outlining potential avenues for future research, including the exploration of novel AI methodologies, the ethical governance of AI in moderation, and the development of more nuanced, context-aware systems. This comprehensive overview aims to catalyze further research and foster a collaborative effort towards more sophisticated, responsible, and human-centric approaches to HS moderation in the digital era. WARNING: This paper contains offensive examples.

Common crowdsourcing systems average estimates of a latent quantity of interest provided by many crowdworkers to produce a group estimate. We develop a new approach -- predict-each-worker -- that leverages self-supervised learning and a novel aggregation scheme. This approach adapts weights assigned to crowdworkers based on estimates they provided for previous quantities. When skills vary across crowdworkers or their estimates correlate, the weighted sum offers a more accurate group estimate than the average. Existing algorithms such as expectation maximization can, at least in principle, produce similarly accurate group estimates. However, their computational requirements become onerous when complex models, such as neural networks, are required to express relationships among crowdworkers. Predict-each-worker accommodates such complexity as well as many other practical challenges. We analyze the efficacy of predict-each-worker through theoretical and computational studies. Among other things, we establish asymptotic optimality as the number of engagements per crowdworker grows.

The rapid progress of Large Models (LMs) has recently revolutionized various fields of deep learning with remarkable grades, ranging from Natural Language Processing (NLP) to Computer Vision (CV). However, LMs are increasingly challenged and criticized by academia and industry due to their powerful performance but untrustworthy behavior, which urgently needs to be alleviated by reliable methods. Despite the abundance of literature on trustworthy LMs in NLP, a systematic survey specifically delving into the trustworthiness of LMs in CV remains absent. In order to mitigate this gap, we summarize four relevant concerns that obstruct the trustworthy usage in vision of LMs in this survey, including 1) human misuse, 2) vulnerability, 3) inherent issue and 4) interpretability. By highlighting corresponding challenge, countermeasures, and discussion in each topic, we hope this survey will facilitate readers' understanding of this field, promote alignment of LMs with human expectations and enable trustworthy LMs to serve as welfare rather than disaster for human society.

A day before the Senate Judiciary Committee grilled CEOs from tech companies about internet child safety, bipartisan lawmakers introduced a bill that would allow victims to sue people who create and distribute sexually-explicit deepfakes under certain circumstances. The Disrupt Explicit Forged Images and Non-Consensual Edits, or DEFIANCE Act, allows victims to sue if those who created the deepfakes knew, or "recklessly disregarded" that the victim did not consent to its making. The federal bill, introduced on Tuesday, came nearly a week after deepfake pornographic images of Taylor Swift flooded X. The social media platform temporarily removed the ability to search for Swift's name on X after the explicit content was viewed tens of millions of times. Only ten states currently have criminal laws against this form of manipulated media files.

The CEOs of five social media companies including Meta, TikTok and X (formerly Twitter) were grilled by Senators on Wednesday about how they are preventing online child sexual exploitation. The Senate Judiciary Committee called the meeting to hold the CEOs to account for what they said was a failure to prevent the abuse of minors, and ask whether they would support the laws that members of the Committee had proposed to address the problem. It is an issue that is getting worse, according to the National Center for Missing and Exploited Children, which says reports of child sexual abuse material (CSAM) reached a record high last year of more than 36 million, as reported by the Washington Post. The National Center for Missing and Exploited Children CyberTipline, a centralized system in the U.S. for reporting online CSAM, was alerted to more than 88 million files in 2022, with almost 90% of reports coming from outside the country. Mark Zuckerberg of Meta, Shou Chew of TikTok, and Linda Yaccarino of X appeared alongside Jason Spiegel of Snap and Jason Citron of Discord to answer questions from the Senate Judiciary Committee.

White House national security spokesman John Kirby reiterated Wednesday that the U.S. will respond after three American soldiers were killed in a drone attack by an Iran-backed proxy group. President Biden on Tuesday blamed Iran for providing weapons to the militant groups that perpetuated the attack and said he had decided how to respond but did not offer further details. But with no public action in the days since the attack, a reporter asked Kirby whether the White House had missed an opportunity to signal resolve. "I think we signal resolve pretty well. And as I said the other day, we'll respond on our own time, on our own schedule, and we'll do that," Kirby said at the daily White House press briefing.

Americans will soon be subjected to facial recognition screening in airports as a new program that is quietly rolling out the technology to 400 locations across the US. The Transportation Security Administration (TSA) is'in the beginning stages of integrating automated facial recognition capability' to current systems that scan flyers' credentials but won't be fully operational until 2030 or 2040. The upgrade, which claims to capture'minimum data' will match the traveler's face to their identification document, flight status and vetting status - and the facial recognition system is already used at 25 airports. While TSA touts the program as a way to'improve security effectiveness and efficiency,' US government officials have called it'a precursor to a full-blown national surveillance state.' Americans will soon be subjected to facial recognition screening in airports as a new program that is quietly rolling out the technology to 400 locations across the US.