As Large Language Models (LLMs) continue to evolve, they are increasingly being employed in numerous studies to simulate societies and execute diverse social tasks. However, LLMs are susceptible to societal biases due to their exposure to human-generated data. Given that LLMs are being used to gain insights into various societal aspects, it is essential to mitigate these biases. To that end, our study investigates the presence of implicit gender biases in multi-agent LLM interactions and proposes two strategies to mitigate these biases. We begin by creating a dataset of scenarios where implicit gender biases might arise, and subsequently develop a metric to assess the presence of biases. Our empirical analysis reveals that LLMs generate outputs characterized by strong implicit bias associations (>= 50\% of the time). Furthermore, these biases tend to escalate following multi-agent interactions. To mitigate them, we propose two strategies: self-reflection with in-context examples (ICE); and supervised fine-tuning. Our research demonstrates that both methods effectively mitigate implicit biases, with the ensemble of fine-tuning and self-reflection proving to be the most successful.

Data augmentation, a cornerstone technique in deep learning, is crucial in enhancing model performance, especially with scarce labeled data. While traditional techniques are effective, their reliance on hand-crafted methods limits their applicability across diverse data types and tasks. Although modern learnable augmentation methods offer increased adaptability, they are computationally expensive and challenging to incorporate within prevalent augmentation workflows. In this work, we present a novel, efficient method for data augmentation, effectively bridging the gap between existing augmentation strategies and emerging datasets and learning tasks. We introduce SAFLEX (Self-Adaptive Augmentation via Feature Label EXtrapolation), which learns the sample weights and soft labels of augmented samples provided by any given upstream augmentation pipeline, using a specifically designed efficient bilevel optimization algorithm. Remarkably, SAFLEX effectively reduces the noise and label errors of the upstream augmentation pipeline with a marginal computational cost. As a versatile module, SAFLEX excels across diverse datasets, including natural and medical images and tabular data, showcasing its prowess in few-shot learning and out-of-distribution generalization. SAFLEX seamlessly integrates with common augmentation strategies like RandAug, CutMix, and those from large pre-trained generative models like stable diffusion and is also compatible with frameworks such as CLIP's fine-tuning. Our findings highlight the potential to adapt existing augmentation pipelines for new data types and tasks, signaling a move towards more adaptable and resilient training frameworks.

Large language models (LLMs) offer personalized responses based on user interactions, but this use case raises serious privacy concerns. Homomorphic encryption (HE) is a cryptographic protocol supporting arithmetic computations in encrypted states and provides a potential solution for privacy-preserving machine learning (PPML). However, the computational intensity of transformers poses challenges for applying HE to LLMs. In this work, we propose a modified HEfriendly transformer architecture with an emphasis on inference following personalized (private) fine-tuning. Utilizing LoRA fine-tuning and Gaussian kernels, we achieve significant computational speedups--6.94 Our findings provide a viable proof of concept for offering privacy-preserving LLM services in areas where data protection is crucial. One of the many capabilities of LLMs that has received much attention is their ability to provide personalized responses based on user interactions, especially with the use of fine-tuning. However, this use case raises serious concerns about user privacy. In response, regulations such as the GDPR (European Union, 2016) and CCPA (State of California, 2018) have been amended. In Italy, ChatGPT was even temporarily banned (McCallum, 2023), and several major corporations, including Apple and Samsung, have restricted its use within their companies (Mok, 2023). Privacy-preserving machine learning (PPML) refers to methods that use machine learning while protecting data privacy. Techniques for PPML include secure multi-party computation (MPC) (Yao, 1982), differential privacy (Dwork, 2006), and homomorphic encryption (HE) (Rivest et al., 1978; Gentry, 2009). Among these, only MPC and HE offer provable security based on cryptographic assumptions. MPC utilizes communications between parties, but these communications can make it challenging to accelerate and parallelize the heavy computation of neural networks. In contrast, HE supports arithmetic computations in encrypted states without requiring communications.

Generating a long story of several thousand words with narrative coherence using Large Language Models (LLMs) has been a challenging task. Previous research has addressed this challenge by proposing different frameworks that create a story plan and generate a long story based on that plan. However, these frameworks have been mainly focusing on maintaining narrative coherence in stories, often overlooking creativity in story planning and the expressiveness of the stories generated from those plans, which are desirable properties to captivate readers' interest. In this paper, we propose Collective Critics for Creative Story Generation framework (CritiCS), which is composed of plan refining stage (CrPlan) and story generation stage (CrText), to integrate a collective revision mechanism that promotes those properties into long-form story generation process. Specifically, in each stage, a group of LLM critics and one leader collaborate to incrementally refine drafts of plan and story throughout multiple rounds. Extensive human evaluation shows that the CritiCS can significantly enhance story creativity and reader engagement, while also maintaining narrative coherence. Furthermore, the design of the framework allows active participation from human writers in any role within the critique process, enabling interactive human-machine collaboration in story writing.

The Coastal underwater evidence search system with surface-underwater collaboration is designed to revolutionize the search for artificial objects in coastal underwater environments, overcoming limitations associated with traditional methods such as divers and tethered remotely operated vehicles. Our innovative multi-robot collaborative system consists of three parts, an autonomous surface vehicle as a mission control center, a towed underwater vehicle for wide-area search, and a biomimetic underwater robot inspired by marine organisms for detailed inspections of identified areas. We conduct extensive simulations and real-world experiments in pond environments and coastal fields to demonstrate the system potential to surpass the limitations of conventional underwater search methods, offering a robust and efficient solution for law enforcement and recovery operations in marine settings.

Tree-based methods are powerful nonparametric techniques in statistics and machine learning. However, their effectiveness, particularly in finite-sample settings, is not fully understood. Recent applications have revealed their surprising ability to distinguish transformations (which we call symbolic feature selection) that remain obscure under current theoretical understanding. This work provides a finite-sample analysis of tree-based methods from a ranking perspective. We link oracle partitions in tree methods to response rankings at local splits, offering new insights into their finite-sample behavior in regression and feature selection tasks. Building on this local ranking perspective, we extend our analysis in two ways: (i) We examine the global ranking performance of individual trees and ensembles, including Classification and Regression Trees (CART) and Bayesian Additive Regression Trees (BART), providing finite-sample oracle bounds, ranking consistency, and posterior contraction results. (ii) Inspired by the ranking perspective, we propose concordant divergence statistics $\mathcal{T}_0$ to evaluate symbolic feature mappings and establish their properties. Numerical experiments demonstrate the competitive performance of these statistics in symbolic feature selection tasks compared to existing methods.

Although Large Language Models (LLMs) have achieved remarkable performance in numerous downstream tasks, their ubiquity has raised two significant concerns. One is that LLMs can hallucinate by generating content that contradicts relevant contextual information; the other is that LLMs can inadvertently leak private information due to input regurgitation. Many prior works have extensively studied each concern independently, but none have investigated them simultaneously. Furthermore, auditing the influence of provided context during open-ended generation with a privacy emphasis is understudied. To this end, we comprehensively characterize the influence and hallucination of contextual information during summarization. We introduce a definition for context influence and Context-Influence Decoding (CID), and then we show that amplifying the context (by factoring out prior knowledge) and the context being out of distribution with respect to prior knowledge increases the context's influence on an LLM. Moreover, we show that context influence gives a lower bound of the private information leakage of CID. We corroborate our analytical findings with experimental evaluations that show improving the F1 ROGUE-L score on CNN-DM for LLaMA 3 by $\textbf{10}$% over regular decoding also leads to $\textbf{1.5x}$ more influence by the context. Moreover, we empirically evaluate how context influence and hallucination are affected by (1) model capacity, (2) context size, (3) the length of the current response, and (4) different token $n$-grams of the context. Our code can be accessed here: https://github.com/james-flemings/context_influence.

There are two major types of uncertainty one can model.

Efficiently aggregating data from different sources is a challenging problem, particularly when samples from each source are distributed differently. These differences can be inherent to the inference task or present for other reasons: sensors in a sensor network may be placed far apart, affecting their individual measurements. Conversely, it is computationally advantageous to split Bayesian inference tasks across subsets of data, but data need not be identically distributed across subsets. One principled way to fuse probability distributions is via the lens of optimal transport: the Wasserstein barycenter is a single distribution that summarizes a collection of input measures while respecting their geometry. However, computing the barycenter scales poorly and requires discretization of all input distributions and the barycenter itself.

Artificial intelligence chatbots struggle to consistently provide accurate answers about Russia's invasion of Ukraine and sometimes regurgitate Kremlin propaganda – an especially noticeable problem for Google's Gemini chatbot. "Increasingly, chatbot users tend to trust the output of these new digital tools," says Elizaveta Kuznetsova at the Weizenbaum Institute in Germany. "Therefore, the way in which chatbots frame information about current events can have a substantial effect on political attitudes about crucial events, like the ongoing war in Ukraine."