The domain of Natural Language Processing (NLP) has experienced notable progress in the evolution of Bangla Question Answering (QA) systems. This paper presents a comprehensive review of seven research articles that contribute to the progress in this domain. These research studies explore different aspects of creating question-answering systems for the Bangla language. They cover areas like collecting data, preparing it for analysis, designing models, conducting experiments, and interpreting results. The papers introduce innovative methods like using LSTM-based models with attention mechanisms, context-based QA systems, and deep learning techniques based on prior knowledge. However, despite the progress made, several challenges remain, including the lack of well-annotated data, the absence of high-quality reading comprehension datasets, and difficulties in understanding the meaning of words in context. Bangla QA models' precision and applicability are constrained by these challenges. This review emphasizes the significance of these research contributions by highlighting the developments achieved in creating Bangla QA systems as well as the ongoing effort required to get past roadblocks and improve the performance of these systems for actual language comprehension tasks.

The optimization-based meta-learning approach is gaining increased traction because of its unique ability to quickly adapt to a new task using only small amounts of data. However, existing optimization-based meta-learning approaches, such as MAML, ANIL and their variants, generally employ backpropagation for upper-level gradient estimation, which requires using historical lower-level parameters/gradients and thus increases computational and memory overhead in each iteration. In this paper, we propose a meta-learning algorithm that can avoid using historical parameters/gradients and significantly reduce memory costs in each iteration compared to existing optimization-based meta-learning approaches. In addition to memory reduction, we prove that our proposed algorithm converges sublinearly with the iteration number of upper-level optimization, and the convergence error decays sublinearly with the batch size of sampled tasks. In the specific case in terms of deterministic meta-learning, we also prove that our proposed algorithm converges to an exact solution. Moreover, we quantify that the computational complexity of the algorithm is on the order of $\mathcal{O}(\epsilon^{-1})$, which matches existing convergence results on meta-learning even without using any historical parameters/gradients. Experimental results on meta-learning benchmarks confirm the efficacy of our proposed algorithm.

Large Language Models (LLMs) have shown remarkable performance across various tasks, yet significant disparities remain for non-English languages, and especially native African languages. This paper addresses these disparities by creating approximately 1 million human-translated words of new benchmark data in 8 low-resource African languages, covering a population of over 160 million speakers of: Amharic, Bambara, Igbo, Sepedi (Northern Sotho), Shona, Sesotho (Southern Sotho), Setswana, and Tsonga. Our benchmarks are translations of Winogrande and three sections of MMLU: college medicine, clinical knowledge, and virology. Using the translated benchmarks, we report previously unknown performance gaps between state-of-the-art (SOTA) LLMs in English and African languages. Finally, using results from over 400 fine-tuned models, we explore several methods to reduce the LLM performance gap, including high-quality dataset fine-tuning (using an LLM-as-an-Annotator), cross-lingual transfer, and cultural appropriateness adjustments. Key findings include average mono-lingual improvements of 5.6% with fine-tuning (with 5.4% average mono-lingual improvements when using high-quality data over low-quality data), 2.9% average gains from cross-lingual transfer, and a 3.0% out-of-the-box performance boost on culturally appropriate questions. The publicly available benchmarks, translations, and code from this study support further research and development aimed at creating more inclusive and effective language technologies.

In an educational setting, an estimate of the difficulty of multiple-choice questions (MCQs), a commonly used strategy to assess learning progress, constitutes very useful information for both teachers and students. Since human assessment is costly from multiple points of view, automatic approaches to MCQ item difficulty estimation are investigated, yielding however mixed success until now. Our approach to this problem takes a different angle from previous work: asking various Large Language Models to tackle the questions included in two different MCQ datasets, we leverage model uncertainty to estimate item difficulty. By using both model uncertainty features as well as textual features in a Random Forest regressor, we show that uncertainty features contribute substantially to difficulty prediction, where difficulty is inversely proportional to the number of students who can correctly answer a question. In addition to showing the value of our approach, we also observe that our model achieves state-of-the-art results on the BEA publicly available dataset.

Additionally, the investigation into the nested pointwise aggregation of In the realm of real-time online Gaussian Process (GP) experts has been undertaken [20], [21]. Nevertheless, the regression, continuously collecting the training data becomes application of pointwise aggregation across the entirety of impractical for dynamic systems due to the constraints in the training dataset proves unattainable within distributed physical storage space and the escalating computational burden systems. Instead of employing the entire dataset for prediction, poses substantial practical challenges, particularly in real-time several approximation techniques prove instrumental. Moreover, local approximation B. Agent-based Gaussian Process methods, such as the naive local experts, the mixture of Distributed learning finds prominent application in multiagent experts, and the product of experts, present viable alternatives. Consequently, joint predictions are aggregated [8]. Several efforts have been dedicated to implementing distributed Prominently, cooperative learning within distributed systems Gaussian Process (DGP) methodologies within MASs.

Automated task guidance has recently attracted attention from the AI research community. Procedural mistake detection (PMD) is a challenging sub-problem of classifying whether a human user (observed through egocentric video) has successfully executed the task at hand (specified by a procedural text). Despite significant efforts in building resources and models for PMD, machine performance remains nonviable, and the reasoning processes underlying this performance are opaque. As such, we recast PMD to an explanatory self-dialog of questions and answers, which serve as evidence for a decision. As this reformulation enables an unprecedented transparency, we leverage a fine-tuned natural language inference (NLI) model to formulate two automated coherence metrics for generated explanations. Our results show that while open-source VLMs struggle with this task off-the-shelf, their accuracy, coherence, and dialog efficiency can be vastly improved by incorporating these coherence metrics into common inference and fine-tuning methods. Furthermore, our multi-faceted metrics can visualize common outcomes at a glance, highlighting areas for improvement.

Contact-rich manipulation plays a vital role in daily human activities, yet uncertain physical parameters pose significant challenges for both model-based and model-free planning and control. A promising approach to address this challenge is to develop policies robust to a wide range of parameters. Domain adaptation and domain randomization are commonly used to achieve such policies but often compromise generalization to new instances or perform conservatively due to neglecting instance-specific information. \textit{Explicit motor adaptation} addresses these issues by estimating system parameters online and then retrieving the parameter-conditioned policy from a parameter-augmented base policy. However, it typically relies on precise system identification or additional high-quality policy retraining, presenting substantial challenges for contact-rich tasks with diverse physical parameters. In this work, we propose \textit{implicit motor adaptation}, which leverages tensor factorization as an implicit representation of the base policy. Given a roughly estimated parameter distribution, the parameter-conditioned policy can be efficiently derived by exploiting the separable structure of tensor cores from the base policy. This framework eliminates the need for precise system estimation and policy retraining while preserving optimal behavior and strong generalization. We provide a theoretical analysis validating this method, supported by numerical evaluations on three contact-rich manipulation primitives. Both simulation and real-world experiments demonstrate its ability to generate robust policies for diverse instances.

Forklifts are used extensively in various industrial settings and are in high demand for automation. In particular, counterbalance forklifts are highly versatile and employed in diverse scenarios. However, efforts to automate these processes are lacking, primarily owing to the absence of a safe and performance-verifiable development environment. This study proposes a learning system that combines a photorealistic digital learning environment with a 1/14-scale robotic forklift environment to address this challenge. Inspired by the training-based learning approach adopted by forklift operators, we employ an end-to-end vision-based deep reinforcement learning approach. The learning is conducted in a digitalized environment created from CAD data, making it safe and eliminating the need for real-world data. In addition, we safely validate the method in a physical setting utilizing a 1/14-scale robotic forklift with a configuration similar to that of a real forklift. We achieved a 60% success rate in pallet loading tasks in real experiments using a robotic forklift. Our approach demonstrates zero-shot sim2real with a simple method that does not require heuristic additions. This learning-based approach is considered a first step towards the automation of counterbalance forklifts.

Learning navigation capabilities in different environments has long been one of the major challenges in decision-making. In this work, we focus on zero-shot navigation ability using given abstract $2$-D top-down maps. Like human navigation by reading a paper map, the agent reads the map as an image when navigating in a novel layout, after learning to navigate on a set of training maps. We propose a model-based reinforcement learning approach for this multi-task learning problem, where it jointly learns a hypermodel that takes top-down maps as input and predicts the weights of the transition network. We use the DeepMind Lab environment and customize layouts using generated maps. Our method can adapt better to novel environments in zero-shot and is more robust to noise.

Large Language Models (LLMs) have shown strong performance in solving mathematical problems, with code-based solutions proving particularly effective. However, the best practice to leverage coding instruction data to enhance mathematical reasoning remains underexplored. This study investigates three key questions: (1) How do different coding styles of mathematical code-based rationales impact LLMs' learning performance? (2) Can general-domain coding instructions improve performance? (3) How does integrating textual rationales with code-based ones during training enhance mathematical reasoning abilities? Our findings reveal that code-based rationales with concise comments, descriptive naming, and hardcoded solutions are beneficial, while improvements from general-domain coding instructions and textual rationales are relatively minor. Based on these insights, we propose CoinMath, a learning strategy designed to enhance mathematical reasoning by diversifying the coding styles of code-based rationales. CoinMath generates a variety of code-based rationales incorporating concise comments, descriptive naming conventions, and hardcoded solutions. Experimental results demonstrate that CoinMath significantly outperforms its baseline model, MAmmoTH, one of the SOTA math LLMs.