Recent works show that these biases may stem from uneven cultural representation in pretraining corpora. This work investigates how pretraining leads to biased culture-conditioned generations by analyzing how models associate entities with cultures based on pretraining data patterns. Additionally, the model favors generating entities with extraordinarily high frequency regardless of the conditioned culture, reflecting biases toward frequent pretraining terms irrespective of relevance. Our findings reflect trends observed specifically within OLMo-7B's pretraining data and are limited to this dataset. We make no claims about whether these results reflect real-world conditions.] In open-ended generative tasks like narrative writing or dialogue, language models often show bias against marginalized social groups based on gender, race, or culture (Gallegos et al., 2024; Manvi et al., 2024; Li et al., 2024b). Cultural bias is particularly notable due to the vast number of cultures to account for. Cultures are often unevenly represented in the pretraining corpora, with some mentioned more frequently than others, irrespective of their real-world prevalence (Li et al., 2024a). Recent studies reveal that models favor entities (Naous et al., 2023) and opinions (Ryan et al., 2024) from frequently represented cultures in pretraining while showing inadequate knowledge and templated answers for less frequent ones (Li et al., 2024b). Such biases in culture-conditioned generations can be linked to studies showing that LLMs' memorization and generalization are constrained by pretraining data imbalances. Zhang et al. (2024) find that these imbalances cause models to overgeneralize to high-frequency knowledge, overshadowing lower-frequency knowledge.

I arrive in Hermanus, a picturesque South African coastal village an hour-and-a-half from Cape Town, at about 11am on a sunny October morning. Ignoring the restaurants and art galleries on the main drag and the throngs of tourists watching southern right whales from the cliff path, I drive straight to the harbour to meet Els Vermeulen, the Belgium-born scientist who heads up the whale unit for the University of Pretoria's Mammal Research Institute. She is waiting for her colleagues to return from the last whale-tagging sortie of the 2024 season. "I would normally be out on the boat with the team," says Vermeulen, who is dressed in a bold geometric print dress and a denim jacket. "But I had to drop my kids at school and couldn't get down here early enough." The water next to the concrete pier is so clear that I can see a giant orange starfish inching its way along the rocky seabed.

In this paper, we explore the paradox of trust and vulnerability in human-machine interactions, inspired by Alexander Reben's BlabDroid project. This project used small, unassuming robots that actively engaged with people, successfully eliciting personal thoughts or secrets from individuals, often more effectively than human counterparts. This phenomenon raises intriguing questions about how trust and self-disclosure operate in interactions with machines, even in their simplest forms. We study the change of trust in technology through analyzing the psychological processes behind such encounters. The analysis applies theories like Social Penetration Theory and Communication Privacy Management Theory to understand the balance between perceived security and the risk of exposure when personal information and secrets are shared with machines or AI. Additionally, we draw on philosophical perspectives, such as posthumanism and phenomenology, to engage with broader questions about trust, privacy, and vulnerability in the digital age. Rapid incorporation of AI into our most private areas challenges us to rethink and redefine our ethical responsibilities.

We propose a decentralized, learning-based framework for dynamic coalition formation in Multi-Robot Task Allocation (MRTA). Our approach extends Multi-Agent Proximal Policy Optimization (MAPPO) by incorporating spatial action maps, robot motion control, task allocation revision, and intention sharing to enable effective coalition formation. Extensive simulations demonstrate that our model significantly outperforms existing methods, including a market-based baseline. Furthermore, we assess the scalability and generalizability of the proposed framework, highlighting its ability to handle large robot populations and adapt to diverse task allocation environments.

Recent work has demonstrated that semantics specified by pretraining data influence how representations of different concepts are organized in a large language model (LLM). However, given the open-ended nature of LLMs, e.g., their ability to in-context learn, we can ask whether models alter these pretraining semantics to adopt alternative, context-specified ones. Specifically, if we provide in-context exemplars wherein a concept plays a different role than what the pretraining data suggests, do models reorganize their representations in accordance with these novel semantics? To answer this question, we take inspiration from the theory of conceptual role semantics and define a toy "graph tracing" task wherein the nodes of the graph are referenced via concepts seen during training (e.g., apple, bird, etc.) and the connectivity of the graph is defined via some predefined structure (e.g., a square grid). Given exemplars that indicate traces of random walks on the graph, we analyze intermediate representations of the model and find that as the amount of context is scaled, there is a sudden re-organization from pretrained semantic representations to in-context representations aligned with the graph structure. Further, we find that when reference concepts have correlations in their semantics (e.g., Monday, Tuesday, etc.), the context-specified graph structure is still present in the representations, but is unable to dominate the pretrained structure. To explain these results, we analogize our task to energy minimization for a predefined graph topology, providing evidence towards an implicit optimization process to infer context-specified semantics. Overall, our findings indicate scaling context-size can flexibly re-organize model representations, possibly unlocking novel capabilities.

Building on the foundations of language modeling in natural language processing, Next Token Prediction (NTP) has evolved into a versatile training objective for machine learning tasks across various modalities, achieving considerable success. As Large Language Models (LLMs) have advanced to unify understanding and generation tasks within the textual modality, recent research has shown that tasks from different modalities can also be effectively encapsulated within the NTP framework, transforming the multimodal information into tokens and predict the next one given the context. This survey introduces a comprehensive taxonomy that unifies both understanding and generation within multimodal learning through the lens of NTP. The proposed taxonomy covers five key aspects: Multimodal tokenization, MMNTP model architectures, unified task representation, datasets \& evaluation, and open challenges. This new taxonomy aims to aid researchers in their exploration of multimodal intelligence. An associated GitHub repository collecting the latest papers and repos is available at https://github.com/LMM101/Awesome-Multimodal-Next-Token-Prediction

This paper explores advanced topics in complex multi-agent systems building upon our previous work. We examine four fundamental challenges in Multi-Agent Reinforcement Learning (MARL): non-stationarity, partial observability, scalability with large agent populations, and decentralized learning. The paper provides mathematical formulations and analysis of recent algorithmic advancements designed to address these challenges, with a particular focus on their integration with game-theoretic concepts. We investigate how Nash equilibria, evolutionary game theory, correlated equilibrium, and adversarial dynamics can be effectively incorporated into MARL algorithms to improve learning outcomes. Through this comprehensive analysis, we demonstrate how the synthesis of game theory and MARL can enhance the robustness and effectiveness of multi-agent systems in complex, dynamic environments.

We present a survey paper on methods and applications of digital twins (DT) for urban traffic management. While the majority of studies on the DT focus on its "eyes," which is the emerging sensing and perception like object detection and tracking, what really distinguishes the DT from a traditional simulator lies in its ``brain," the prediction and decision making capabilities of extracting patterns and making informed decisions from what has been seen and perceived. In order to add values to urban transportation management, DTs need to be powered by artificial intelligence and complement with low-latency high-bandwidth sensing and networking technologies. We will first review the DT pipeline leveraging cyberphysical systems and propose our DT architecture deployed on a real-world testbed in New York City. This survey paper can be a pointer to help researchers and practitioners identify challenges and opportunities for the development of DTs; a bridge to initiate conversations across disciplines; and a road map to exploiting potentials of DTs for diverse urban transportation applications.

We present a simple way to merge masked language modeling with causal language modeling. This hybrid training objective results in a model that combines the strengths of both modeling paradigms within a single transformer stack: GPT-BERT can be transparently used like any standard causal or masked language model. We test the pretraining process that enables this flexible behavior on the BabyLM Challenge 2024. The results show that the hybrid pretraining outperforms masked-only or causal-only models. We openly release the models, training corpora and code.

Moving your body is for everyone, regardless of experience level, skill, or location. This year's Best of What's New innovations make getting outside and active easier in many ways. A tightly woven shirt stops itchy mosquito bites sans chemicals. An electric fishing reel cuts the cord and ditches heavy batteries once and for all. An app combines avalanche education with hard-to-find reports for safer snowshoeing and skiing.