With a name that sounds like something a parent would slowly mouth to their infant, NaNoWriMo is an annual "challenge" in which many thousands of seemingly well-adjusted people decide to write a novel in a month. "Do I need something special to write a novel?" the nonprofit that puts on this exquisite torture reasonably asks on its website. National Novel Writing Month began in 1999 with 21 participants, and now nearly half a million take part every November. The event is also the name of the organization that gamifies the exercise, hosting participants on its online platform. To "win" NaNoWriMo, you need to produce a minimum of 50,000 words in a month (about the length of The Great Gatsby)--or 1,667 words a day, a number, NaNoWriMo tells us, that "scientists have determined to be the perfect amount to boost your creativity."

In this paper, we investigate the presence of additive bias in Large Language Models (LLMs), drawing a parallel to the cognitive bias observed in humans where individuals tend to favor additive over subtractive changes. Using a series of controlled experiments, we tested various LLMs, including GPT-3.5 Turbo, Claude 3.5 Sonnet, Mistral, Math$\Sigma$tral, and Llama 3.1, on tasks designed to measure their propensity for additive versus subtractive modifications. Our findings demonstrate a significant preference for additive changes across all tested models. For example, in a palindrome creation task, Llama 3.1 favored adding letters 97.85% of the time over removing them. Similarly, in a Lego tower balancing task, GPT-3.5 Turbo chose to add a brick 76.38% of the time rather than remove one. In a text summarization task, Mistral 7B produced longer summaries in 59.40% to 75.10% of cases when asked to improve its own or others' writing. These results indicate that, similar to humans, LLMs exhibit a marked additive bias, which might have implications when LLMs are used on a large scale. Addittive bias might increase resource use and environmental impact, leading to higher economic costs due to overconsumption and waste. This bias should be considered in the development and application of LLMs to ensure balanced and efficient problem-solving approaches.

Two primary ways to change LLM behavior are prompting and weight updates (e.g., fine-tuning). Prompting LLMs is simple and effective, specifying the desired changes explicitly in natural language, whereas weight updates provide more expressive and permanent behavior changes, specified implicitly via training on large datasets. We present a technique for "baking" prompts into the weights of an LLM. Prompt Baking converts a prompt $u$ and initial weights $\theta$ to a new set of weights $\theta_u$ such that new "baked" LLM behaves like the original prompted LLM. Mathematically, we minimize the KL divergence between $P_\theta(\cdot | u)$ and $P_{\theta_u}(\cdot)$, where $P$ is the LLM's probability distribution over token sequences. Across all our experiments, we find prompts can be readily baked into weight updates. Baking chain-of-thought prompts improves zero-shot performance on GSM8K, ASDiv, MBPP, ARC-Easy, ARC-Challenge, and CommonsenseQA benchmarks. Baking news headlines directly updates an LLM's knowledge. And baking instructions & personas alleviates "prompt forgetting" over long sequences. Furthermore, stopping baking early creates "half-baked" models, continuously scaling prompt strength. Baked models retain their sensitivity to further prompting and baking, including re-prompting with the baked-in prompt. Surprisingly, the re-prompted models yield further performance gains in instruction following, as well as math reasoning and coding benchmarks. Taking re-prompting and re-baking to the limit yields a form of iterative self-improvement we call Prompt Pursuit, and preliminary results on instruction following exhibit dramatic performance gains. Finally, we discuss implications for AI safety, continuous model updating, enhancing real-time learning capabilities in LLM-based agents, and generating more stable AI personas.

Indonesian and Western gamelan ensembles are investigated with respect to performance differences. Thereby, the often exotistic history of this music in the West might be reflected in contemporary tonal system, articulation, or large-scale form differences. Analyzing recordings of four Western and five Indonesian orchestras with respect to tonal systems and timbre features and using self-organizing Kohonen map (SOM) as a machine learning algorithm, a clear clustering between Indonesian and Western ensembles appears using certain psychoacoustic features. These point to a reduced articulation and large-scale form variability of Western ensembles compared to Indonesian ones. The SOM also clusters the ensembles with respect to their tonal systems, but no clusters between Indonesian and Western ensembles can be found in this respect. Therefore, a clear analogy between lower articulatory variability and large-scale form variation and a more exostistic, mediative and calm performance expectation and reception of gamelan in the West therefore appears.

Vision-language models (VLMs) excel at tasks requiring joint understanding of visual and linguistic information. A particularly promising yet under-explored application for these models lies in answering questions based on various kinds of maps. This study investigates the efficacy of VLMs in answering questions based on choropleth maps, which are widely used for data analysis and representation. To facilitate and encourage research in this area, we introduce a novel map-based question-answering benchmark, consisting of maps from three geographical regions (United States, India, China), each containing 1000 questions. Our benchmark incorporates 43 diverse question templates, requiring nuanced understanding of relative spatial relationships, intricate map features, and complex reasoning. It also includes maps with discrete and continuous values, encompassing variations in color-mapping, category ordering, and stylistic patterns, enabling comprehensive analysis. We evaluate the performance of multiple VLMs on this benchmark, highlighting gaps in their abilities and providing insights for improving such models.

In this paper, we investigate the use of probabilistic graphical models, specifically stochastic blockmodels, for the purpose of hierarchical entity clustering on knowledge graphs. These models, seldom used in the Semantic Web community, decompose a graph into a set of probability distributions. The parameters of these distributions are then inferred allowing for their subsequent sampling to generate a random graph. In a non-parametric setting, this allows for the induction of hierarchical clusterings without prior constraints on the hierarchy's structure. Specifically, this is achieved by the integration of the Nested Chinese Restaurant Process and the Stick Breaking Process into the generative model. In this regard, we propose a model leveraging such integration and derive a collapsed Gibbs sampling scheme for its inference. To aid in understanding, we describe the steps in this derivation and provide an implementation for the sampler. We evaluate our model on synthetic and real-world datasets and quantitatively compare against benchmark models. We further evaluate our results qualitatively and find that our model is capable of inducing coherent cluster hierarchies in small scale settings. The work presented in this paper provides the first step for the further application of stochastic blockmodels for knowledge graphs on a larger scale. We conclude the paper with potential avenues for future work on more scalable inference schemes.

Effective water quality monitoring in coastal regions is crucial due to the progressive deterioration caused by pollution and human activities. To address this, this study develops time-series models to predict chlorophyll-a (Chl-a), suspended solids (SS), and turbidity using Sentinel-2 satellite data and Google Earth Engine (GEE) in the coastal regions of Hong Kong. Leveraging Long Short-Term Memory (LSTM) Recurrent Neural Networks, the study incorporates extensive temporal datasets to enhance prediction accuracy. The models utilize spectral data from Sentinel-2, focusing on optically active components, and demonstrate that selected variables closely align with the spectral characteristics of Chl-a and SS. The results indicate improved predictive performance over previous methods, highlighting the potential for remote sensing technology in continuous and comprehensive water quality assessment.

Interpretability research aims to provide a human-understandable explanation for model outputs and behaviors based on the input and model's internal structure [Doshi-Velez and Kim, 2017]. The field's goal is to generate mechanistic explanations of how neural networks perform computations and produce behaviors [Nanda et al., 2023, Olsson et al., 2022], which could help predict the behavior of such networks across a wide range of scenarios and possibly solve notable problems of AI systems, such as hallucination and toxic output [Ji et al., 2023]. Being able to interpret AI systems is therefore a key capability to be able to understand whether models are appropriately fair, reliable, robust, and worthy of user trust [Doshi-Velez and Kim, 2017]. However, understanding the computations of frontier AI systems with hundreds of billions of parameters presents many technical challenges, from the curse of dimensionality [Zhao et al., 2024, Altman and Krzywinski, 2018] to finding a suitable unit of analysis [Olah et al., 2020, Zou et al., 2023]. These challenges are par for the course when studying complex systems. In particular, many challenges around artificial neural networks (ANN) interpretability are intimately familiar to another group of researchers: neuroscientists. Neuroscience (often in partnership with cognitive science and psychology) investigates how neurons, their connections, and their activity patterns give rise to cognition and behavior. Similar to how deep learning researchers have recognized, neuroscientists have realized that simply examining activity profiles of individual neurons in response to a particular input is often insufficient for understanding how the system performs computation. Instead, complex neural systems are best understood across multiple levels of analysis - considering behavior alongside the brain's connectome, population codes, and codes of single neurons to gain a holistic understanding of the inner workings of the brain

Digital Twin technology creates virtual replicas of physical objects, processes, or systems by replicating their properties, data, and behaviors. This advanced technology offers a range of intelligent functionalities, such as modeling, simulation, and data-driven decision-making, that facilitate design optimization, performance estimation, and monitoring operations. Forecasting plays a pivotal role in Digital Twin technology, as it enables the prediction of future outcomes, supports informed decision-making, minimizes risks, driving improvements in efficiency, productivity, and cost reduction. Recently, Digital Twin technology has leveraged Graph forecasting techniques in large-scale complex sensor networks to enable accurate forecasting and simulation of diverse scenarios, fostering proactive and data-driven decision making. However, existing Graph forecasting techniques lack scalability for many real-world applications. They have limited ability to adapt to non-stationary environments, retain past knowledge, lack a mechanism to capture the higher order spatio-temporal dynamics, and estimate uncertainty in model predictions. To surmount the challenges, we introduce a hybrid architecture that enhances the hypergraph representation learning backbone by incorporating fast adaptation to new patterns and memory-based retrieval of past knowledge. This balance aims to improve the slowly-learned backbone and achieve better performance in adapting to recent changes. In addition, it models the time-varying uncertainty of multi-horizon forecasts, providing estimates of prediction uncertainty. Our forecasting architecture has been validated through ablation studies and has demonstrated promising results across multiple benchmark datasets, surpassing state-ofthe-art forecasting methods by a significant margin.

Recently many deep learning models have been proposed for Long-term Time Series Forecasting (LTSF). Based on previous literature, we identify three critical patterns that can improve forecasting accuracy: the order and semantic dependencies in time dimension as well as cross-variate dependency. However, little effort has been made to simultaneously consider order and semantic dependencies when developing forecasting models. Moreover, existing approaches utilize cross-variate dependency by mixing information from different timestamps and variates, which may introduce irrelevant or harmful cross-variate information to the time dimension and largely hinder forecasting performance. To overcome these limitations, we investigate the potential of Mamba for LTSF and discover two key advantages benefiting forecasting: (i) the selection mechanism makes Mamba focus on or ignore specific inputs and learn semantic dependency easily, and (ii) Mamba preserves order dependency by processing sequences recursively. After that, we empirically find that the non-linear activation used in Mamba is unnecessary for semantically sparse time series data. Therefore, we further propose SAMBA, a Simplified Mamba with disentangled dependency encoding. Specifically, we first remove the non-linearities of Mamba to make it more suitable for LTSF. Furthermore, we propose a disentangled dependency encoding strategy to endow Mamba with cross-variate dependency modeling capabilities while reducing the interference between time and variate dimensions. Extensive experimental results on seven real-world datasets demonstrate the effectiveness of SAMBA over state-of-the-art forecasting models.