This paper studies the problem of parallelising large transformer-based language models. It goes beyond data parallelism in that it focuses on splitting the model when it does not fit in the memory of a single GPU. The idea is to segment the model into groups such that GPUs do not sit around waiting on others to pass gradients ( this is the case for layer-wise parallel solutions where each layer is on its own GPU). The model then allows backpropagation to use stale gradients between groups. An L-layer network is split into K modules so that the weights of the network are divided into K groups and each group is placed on a GPU.

Overview: The authors propose an original approach to zero-shot-learning by combining VAEs with EM for inferring the optimal unseen examples. The key idea is simultaneously generating examples of unseen classes and learning from them. The authors run a number of experiments which demonstrate that the proposed method shows competitive performance in a number of ZSL tasks. Quality: The work is generally of high quality. The experiments are clearly described, and the model specifications are detailed.

The addresses zero-shot learning by an EM process of iteratively generating examples from unseen classes, and learning with them, this leads to generating samples that are good to learn from. Reviewers found the idea novel for this context, the writing clear and the experiments (mostly) convincing. They asked to see additional ablation studies in the final version.

OpenAI CEO Sam Altman, Oracle founder Larry Ellison and SoftBank CEO Masayoshi Son comment on President Trump's Stargate AI investment project on'Special Report.' The three tech CEOs who have joined forces in President Donald Trump's multi-billion dollar artificial intelligence infrastructure project defended the venture as an investment that "impacts all of humanity." "This is a very large investment that affects all of humanity," Oracle founder Larry Ellison told Fox News chief political anchor Bret Baier on "Special Report" on Tuesday. President Trump unveiled a massive AI infrastructure project from the private sector on the first full day of his second term in office on Tuesday. During a speech at the White House, Trump announced that Softbank, OpenAI and Oracle have joined forces for a project called Stargate to build data centers in the U.S. for powering AI.

OpenAI did not respond to questions about its lobbying efforts. But perhaps more important, the disclosure is a clear signal of the company's arrival as a political player, as its first year of serious lobbying ends and Republican control of Washington begins. While OpenAI's lobbying spending is still dwarfed by its peers'--Meta tops the list of Big Tech spenders, with more than 24 million in 2024--the uptick comes as it and other AI companies have helped redraw the shape of AI policy. For the past few years, AI policy has been something like a whack-a-mole response to the risks posed by deepfakes and misinformation. But over the last year, AI companies have started to position the success of the technology as pivotal to national security and American competitiveness, arguing that the government must therefore support the industry's growth.

LLMs' sources of knowledge are data snapshots containing factual information about entities collected at different timestamps and from different media types (e.g. wikis, social media, etc.). Such unstructured knowledge is subject to change due to updates through time from past to present. Equally important are the inconsistencies and inaccuracies occurring in different information sources. Consequently, the model's knowledge about an entity may be perturbed while training over the sequence of snapshots or at inference time, resulting in inconsistent and inaccurate model performance. In this work, we study the appropriateness of Large Language Models (LLMs) as repositories of factual knowledge. We consider twenty-four state-of-the-art LLMs that are either closed-, partially (weights), or fully (weight and training data) open-source. We evaluate their reliability in responding to time-sensitive factual questions in terms of accuracy and consistency when prompts are perturbed. We further evaluate the effectiveness of state-of-the-art methods to improve LLMs' accuracy and consistency. We then propose "ENtity-Aware Fine-tuning" (ENAF), a soft neurosymbolic approach aimed at providing a structured representation of entities during fine-tuning to improve the model's performance.

Objective This study is part of a series of initiatives at a UK university designed to cultivate a deep understanding of students' perspectives on analytics that resonate with their unique learning needs. It explores collaborative data processing undertaken by postgraduate students who examined an Open University Learning Analytics Dataset (OULAD). Methods A qualitative approach was adopted, integrating a Retrieval-Augmented Generation (RAG) and a Large Language Model (LLM) technique with human-led content analysis to gather information about students' perspectives based on their submitted work. The study involved 72 postgraduate students in 12 groups. Findings The analysis of group work revealed diverse insights into essential learning analytics from the students' perspectives. All groups adopted a structured data science methodology. The questions formulated by the groups were categorised into seven themes, reflecting their specific areas of interest. While there was variation in the selected variables to interpret correlations, a consensus was found regarding the general results. Conclusion A significant outcome of this study is that students specialising in data science exhibited a deeper understanding of learning analytics, effectively articulating their interests through inferences drawn from their analyses. While human-led content analysis provided a general understanding of students' perspectives, the LLM offered nuanced insights.

Generative AI technologies, particularly Large Language Models (LLMs), have transformed information management systems but introduced substantial biases that can compromise their effectiveness in informing business decision-making. This challenge presents information management scholars with a unique opportunity to advance the field by identifying and addressing these biases across extensive applications of LLMs. Building on the discussion on bias sources and current methods for detecting and mitigating bias, this paper seeks to identify gaps and opportunities for future research. By incorporating ethical considerations, policy implications, and sociotechnical perspectives, we focus on developing a framework that covers major stakeholders of Generative AI systems, proposing key research questions, and inspiring discussion. Our goal is to provide actionable pathways for researchers to address bias in LLM applications, thereby advancing research in information management that ultimately informs business practices. Our forward-looking framework and research agenda advocate interdisciplinary approaches, innovative methods, dynamic perspectives, and rigorous evaluation to ensure fairness and transparency in Generative AI-driven information systems. We expect this study to serve as a call to action for information management scholars to tackle this critical issue, guiding the improvement of fairness and effectiveness in LLM-based systems for business practice.

Different from traditional Business-to-Consumer e-commerce platforms~(e.g., Amazon), online fleamarket platforms~(e.g., Craigslist) mainly focus on individual sellers who are lack of time investment and business proficiency. Individual sellers often struggle with the bargaining process and thus the deal is unaccomplished. Recent advancements in Large Language Models(LLMs) demonstrate huge potential in various dialogue tasks, but those tasks are mainly in the form of passively following user's instruction. Bargaining, as a form of proactive dialogue task, represents a distinct art of dialogue considering the dynamism of environment and uncertainty of adversary strategies. In this paper, we propose an LLM-empowered bargaining agent designed for online fleamarket platform sellers, named as FishBargain. Specifically, FishBargain understands the chat context and product information, chooses both action and language skill considering possible adversary actions and generates utterances. FishBargain has been tested by thousands of individual sellers on one of the largest online fleamarket platforms~(Xianyu) in China. Both qualitative and quantitative experiments demonstrate that FishBargain can effectively help sellers make more deals.

In this paper, we compare data generated with mono- and multilingual LLMs spanning a range of model sizes with data provided by human participants in an experimental setting investigating well-established discourse biases. Beyond the comparison as such, we aim to develop a benchmark to assess the capabilities of LLMs with discourse biases as a robust proxy for more general discourse understanding capabilities. More specifically, we investigated Implicit Causality verbs, for which psycholinguistic research has found participants to display biases with regard to three phenomena:\ the establishment of (i) coreference relations (Experiment 1), (ii) coherence relations (Experiment 2), and (iii) the use of particular referring expressions (Experiments 3 and 4). With regard to coreference biases we found only the largest monolingual LLM (German Bloom 6.4B) to display more human-like biases. For coherence relation, no LLM displayed the explanation bias usually found for humans. For referring expressions, all LLMs displayed a preference for referring to subject arguments with simpler forms than to objects. However, no bias effect on referring expression was found, as opposed to recent studies investigating human biases.