Canada

Time series forecasting is an essential area of machine learning with a wide range of real-world applications. Most of the previous forecasting models aim to capture dynamic characteristics from uni-modal numerical historical data. Although extra knowledge can boost the time series forecasting performance, it is hard to collect such information. In addition, how to fuse the multi-modality information is non-trivial. In this paper, we first propose a general principle of collecting the corresponding textual information from different data sources with the help of modern large language models (LLM). Then, we propose a prompt-based LLM framework to utilize both the numerical data and the textual information simultaneously, named GPT4MTS. In practice, we propose a GDELT-based multimodal time series dataset for news number forecasting, which provides a concise and well-structured version of time series dataset with textual information for further research in communication. Through extensive experiments, we demonstrate the effectiveness of our proposed method on forecasting tasks with extra-textual information. In addition, we apply a case study on different news events of two countries for a better understanding of our proposed dataset.

AAAI 2024

GPT4MTS: Prompt-based Large Language Model for Multimodal Time-series Forecasting

dmkm: mining of visual

temporal or spatio-temporal data

multimedia & multimodal data

snlp: language models

ml: multimodal learning

dmkm: mining of spatial

### Welcome to IJCNLP-AACL 2025! 
 It is a great honor to host this joint conference in Mumbai, India, from December 20 to 24, 2025. The joint conferences of IJCNLP and AACL are organized with alternating leadership in the Asia-Pacific region. The event is run by the Asian Federation of Natural Language Processing (AFNLP) in odd years, and by AACL in even years, while it is organized solely by ACL when the annual ACL meeting is held in the region. This year, the conference is primarily organized by AFNLP. 
*Kentaro Inui
MBZUAI, UAE
General Chair, IJCNLP-AACL 2025* 
Read full message and download the Conference Handbook [**here**](https://drive.google.com/file/d/1UTwxkAqSqI-GAoJC3wE1zZt5VP1Y8GX0/view?usp=sharing).

IJCNLP-AACL 2025

The 14th IJCNLP & 4th AACL will be held in Mumbai, India from December 20th to December 24th, 2025.

technical paper

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-24 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/registration/)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

The rapid progress of Transformers in artificial intelligence has come at the cost of increased resource consumption and greenhouse gas emissions due to growing model sizes. Prior work suggests using pretrained small models to improve training efficiency, but this approach may not be suitable for new model structures. On the other hand, training from scratch can be slow, and progressively stacking layers often fails to achieve significant acceleration. To address these challenges, we propose a novel method called Apollo, which prepares lessons for expanding operations by learning high-layer functionality during training of low layers. Our approach involves low-value-prioritized sampling (LVPS) to train different depths and weight sharing to facilitate efficient expansion. We also introduce an interpolation method for stable model depth extension. Experiments demonstrate that Apollo achieves state-of-the-art acceleration ratios, even rivaling methods using pretrained models, making it a universal and efficient solution for training deep models while reducing time, financial, and environmental costs.

Preparing Lessons for Progressive Training on Language Models

Conflict-driven clause learning (CDCL) is the dominating algorithmic paradigm for SAT solving and hugely successful in practice. In its lifted version QCDCL, it is  one of the main approaches for solving quantified Boolean formulas (QBF).
	
In both SAT and QBF, proofs can be efficiently extracted from runs of (Q)CDCL solvers. While for CDCL, it is known that the proof size in the underlying proof system propositional resolution matches the CDCL runtime up to a polynomial factor, we show that in QBF there is an exponential gap between QCDCL runtime and the size of the extracted proofs in QBF resolution systems. We demonstrate that this is not just a gap between QCDCL runtime and the size of any QBF resolution proof, but even the extracted proofs are exponentially smaller for some instances. Hence searching for a small proof via QCDCL (even with non-deterministic decision policies) will provably incur an exponential overhead for some instances.

Runtime vs. Extracted Proof Size: An Exponential Gap for CDCL on QBFs | VIDEO

The recent surge in research interest in applying large language models (LLMs) to decision-making tasks has flourished by leveraging the extensive world knowledge embedded in LLMs. While there is a growing demand to tailor LLMs for custom decision-making tasks, finetuning them for specific tasks is resource-intensive and may diminish the model's generalization capabilities. Moreover, state-of-the-art language models like GPT-4 and Claude are primarily accessible through API calls, with their parametric weights remaining proprietary and unavailable to the public. This scenario emphasizes the growing need for new methodologies that allow learning from agent experiences without requiring parametric updates. To address these problems, we introduce the Experiential Learning (ExpeL) agent. Our agent autonomously gathers experiences and extracts knowledge using natural language from a collection of training tasks. At inference, the agent recalls its extracted insights and past experiences to make informed decisions. Our empirical results highlight the robust learning efficacy of the ExpeL agent, indicating a consistent enhancement in its performance as it accumulates experiences. We further explore the emerging capabilities and transfer learning potential of the ExpeL agent through qualitative observations and additional experiments.

ExpeL: LLM Agents Are Experiential Learners

We present three novel graph representations of planning tasks suitable for learning domain-independent heuristics using Graph Neural Networks (GNNs) to guide search. In particular, to mitigate the issues caused by large grounded GNNs we present the first method for learning domain-independent heuristics with only the lifted representation of a planning task. We also provide a theoretical analysis of the expressiveness of our models, showing that some are more powerful than STRIPS-HGN, the only other existing model for learning domain-independent heuristics. Our experiments show that our heuristics generalise to much larger problems than those in the training set, vastly surpassing STRIPS-HGN heuristics.

Learning Domain-Independent Heuristics for Grounded and Lifted Planning

Spiking Graph Neural Networks are emerging tools for analyzing graph data along with low energy consumption and certain biological fidelity. Existing methods directly integrate same-reactive spiking neurons into graph neural networks for processing propagated graphs. However, such same-reactive neurons are not biological-functionality enough compared to the brain's dynamic-reactive ones, limiting the model's expression. Meanwhile, insufficient long-range neighbor information can be excavated with the few-step propagated graph, restricting discrimination of graph spiking embeddings. Inspired by the dynamic cognition in the brain, we propose a Dynamic Reactive Spiking Graph Neural Network that can enhance model's expressive ability in higher biological fidelity. Specifically, we design dynamic reactive spiking neurons to process spiking graph inputs, which have unique optimizable thresholds to spontaneously explore dynamic reactive states between neurons. Moreover, discriminative graph positional spikes are learned and integrated adaptively into spiking outputs through our neurons, thereby exploring long-range neighbors more thoroughly. Finally, with the dynamic reactive mechanism and learnable positional integration, we can obtain a powerful and highly bio-fidelity model with low energy consumption. Experiments on various domain-related datasets can demonstrate the effectiveness of our model. Our code is available at https://github.com/hzhao98/DRSGNN.

Dynamic Reactive Spiking Graph Neural Network

Recently, regression-based methods, which predict parameterized text shapes for text localization, have gained popularity in scene text detection. However, the existing parameterized text shape methods still have limitations in modeling arbitrary-shaped texts due to ignoring the utilization of text-specific shape information. Moreover, the time consumption of the entire pipeline has been largely overlooked, leading to a suboptimal overall inference speed. To address these issues, we first propose a novel parameterized text shape method based on low-rank approximation. Unlike other shape representation methods that employ data-irrelevant parameterization, our approach utilizes singular value decomposition and reconstructs the text shape using a few eigenvectors learned from labeled text contours. By exploring the shape correlation among different text contours, our method achieves consistency, compactness, simplicity, and robustness in shape representation. Next, we propose a dual assignment scheme for speed acceleration. It adopts a sparse assignment branch to accelerate the inference speed, and meanwhile, provides ample supervised signals for training through a dense assignment branch. Building upon these designs, we implement an accurate and efficient arbitrary-shaped text detector named LRANet. Extensive experiments are conducted on several challenging benchmarks, demonstrating the superior accuracy and efficiency of LRANet compared to state-of-the-art methods.

LRANet: Towards Accurate and Efficient Scene Text Detection with Low-Rank Approximation Network

Abstract Visual Reasoning (AVR) comprises a wide selection of various problems similar to those used in human IQ tests. Recent years have brought dynamic progress in solving particular AVR tasks, however, in the contemporary literature AVR problems are largely dealt with in isolation, leading to highly specialized task-specific methods. With the aim of developing universal learning systems in the AVR domain, we propose the unified model for solving Single-Choice Abstract visual Reasoning tasks (SCAR), capable of solving various single-choice AVR tasks, without making any a priori assumptions about the task structure, in particular the number and location of panels. The proposed model relies on a novel Structure-Aware dynamic Layer (SAL), which adapts its weights to the structure of the considered AVR problem. Experiments conducted on Raven's Progressive Matrices, Visual Analogy Problems, and Odd One Out problems show that SCAR (SAL-based models, in general) effectively solves diverse AVR tasks, and its performance is on par with the state-of-the-art task-specific baselines. What is more, SCAR demonstrates effective knowledge reuse in multi-task and transfer learning settings. To our knowledge, this work is the first successful attempt to construct a general single-choice AVR solver relying on self-configurable architecture and unified solving method. With this work we aim to stimulate and foster progress on task-independent research paths in the AVR domain, with the long-term goal of development of a general AVR solver.

One Self-Configurable Model to Solve Many Abstract Visual Reasoning Problems

Generative artificial intelligence (AI) is swiftly cementing its role as an indispensable tool for students transitioning from K-12 to higher education and professional spheres. Yet, harnessing its full potential requires more than mere familiarity. Students must be equipped with the skills to engage with AI both productively and ethically. Left unchecked, AI usage can pose risks, especially if students lack proper guidance or understanding of their actions. Moreover, effective interaction with AI necessitates skills in prompt engineering to yield desired outcomes. Sidekick Academy is a digital online platform where students can safely experiment with and learn about AI. This article delves into the genesis of Sidekick Academy, offering a glimpse into its lessons on how to use AI and complex debate on ethical use. It also sheds light on the academy's "sandbox" - a secure space for students to explore AI without jeopardizing their safety or privacy.

AI, Ethics, and Education: The Pioneering Path of Sidekick Academy

In an age where Large Language Models (LLMs) expedite the generation of text, the skills for critically evaluating and creating meaningful text using these models are often lacking. To help classroom teachers address this, we introduce Prompty, a specialized teaching tool co-designed to facilitate both critical and effective use of LLMs. Prompty serves multiple learning goals: it allows students to critically evaluate text generated by LLMs, aids in their writing practice, and provides a deeper understanding of how LLMs function—all within a student-friendly environment secured by essential guardrails. Prompty was co-designed in collaboration with high school teachers as part of CRAFT, an initiative by Stanford University to promote AI literacy. It was pilot-tested in a high school English class to serve as an AI writing assistant, focusing on the critical evaluation of machine-generated text. This trial yielded preliminary evidence that attests to the tool's effectiveness in fulfilling its educational goals. The findings from the pilot study indicate that easy-to-use tools like Prompty have great potential. These tools can be adapted to fit the goals of individual teachers. They can help in achieving subject-specific learning goals while serving as an effective way to teach AI concepts in high school.

From Consumers to Critical Users: Prompty, an AI Literacy Tool for High School Students

Many learning tasks in Artificial Intelligence (AI) require dealing with graph data, ranging from biology and chemistry to finance and education. As powerful learning tools for graphs,  graph neural networks (GNNs) have demonstrated remarkable performance in various graph-related applications.  Despite the significant accomplishments of GNNs, recent studies have highlighted that their efficiency and effectiveness face significant challenges such as adversarial robustness and scalability, which are fundamentally linked to data. While major attention has been devoted to improving GNNs from the model perspective, the potential of directly enhancing data has often been overlooked.  It underscores a critical gap in GNN research---while model improvements are undoubtedly important, we also need to recognize and address the data-related factors contributing to the challenges. Hence, \textbf{my research is to investigate solutions for these challenges from the data perspective, employing strategies such as data characterization, reduction, augmentation, transformation, and detection.

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Preparing Lessons for Progressive Training on Language Models

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