Active Learning (AL) allows models to learn interactively from user feedback. This paper introduces a counterfactual data augmentation approach to AL, particularly addressing the selection of datapoints for user querying, a pivotal concern in enhancing data efficiency. Our approach is inspired by Variation Theory, a theory of human concept learning that emphasizes the essential features of a concept by focusing on what stays the same and what changes. Instead of just querying with existing datapoints, our approach synthesizes artificial datapoints that highlight potential key similarities and differences among labels using a neuro-symbolic pipeline combining large language models (LLMs) and rule-based models. Through an experiment in the example domain of text classification, we show that our approach achieves significantly higher performance when there are fewer annotated data. As the annotated training data gets larger the impact of the generated data starts to diminish showing its capability to address the cold start problem in AL. This research sheds light on integrating theories of human learning into the optimization of AL.

Instruction tuning plays a critical role in aligning large language models (LLMs) with human preference. Despite the vast amount of open instruction datasets, naively training a LLM on all existing instructions may not be optimal and practical. To pinpoint the most beneficial datapoints, data assessment and selection methods have been proposed in the fields of natural language processing (NLP) and deep learning. However, under the context of instruction tuning, there still exists a gap in knowledge on what kind of data evaluation metrics can be employed and how they can be integrated into the selection mechanism. To bridge this gap, we present a comprehensive review on existing literature of data assessment and selection especially for instruction tuning of LLMs. We systematically categorize all applicable methods into quality-based, diversity-based, and importance-based ones where a unified, fine-grained taxonomy is structured. For each category, representative methods are elaborated to describe the landscape of relevant research. In addition, comparison between latest methods is conducted on their officially reported results to provide in-depth discussions on their limitations. Finally, we summarize the open challenges and propose the promosing avenues for future studies. All related contents are available at https://github.com/yuleiqin/fantastic-data-engineering.

Spectral techniques are popular and robust approaches to data analysis. A prominent example is the use of eigenvectors of a Laplacian, constructed from data affinities, to identify natural data groupings or clusters, or to produce a simplified representation of data lying on a manifold. This tutorial concerns the dynamic Laplacian, which is a natural generalisation of the Laplacian to handle data that has a time component and lies on a time-evolving manifold. In this dynamic setting, clusters correspond to long-lived ``coherent'' collections. We begin with a gentle recap of spectral geometry before describing the dynamic generalisations. We also discuss computational methods and the automatic separation of many distinct features through the SEBA algorithm. The purpose of this tutorial is to bring together many results from the dynamic Laplacian literature into a single short document, written in an accessible style.

Automated Short Answer Scoring (ASAS) is a critical component in educational assessment. While traditional ASAS systems relied on rule-based algorithms or complex deep learning methods, recent advancements in Generative Language Models (GLMs) offer new opportunities for improvement. This study explores the application of GLMs to ASAS, leveraging their off-the-shelf capabilities and performance in various domains. We propose a novel pipeline that combines vector databases, transformer-based encoders, and GLMs to enhance short answer scoring accuracy. Our approach stores training responses in a vector database, retrieves semantically similar responses during inference, and employs a GLM to analyze these responses and determine appropriate scores. We further optimize the system through fine-tuned retrieval processes and prompt engineering. Evaluation on the SemEval 2013 dataset demonstrates a significant improvement on the SCIENTSBANK 3-way and 2-way tasks compared to existing methods, highlighting the potential of GLMs in advancing ASAS technology.

This paper presents a 1/10th scale mini-city platform used as a testing bed for evaluating autonomous and connected vehicles. Using the mini-city platform, we can evaluate different driving scenarios including human-driven and autonomous driving. We provide a unique, visual feature-rich environment for evaluating computer vision methods. The conducted experiments utilize onboard sensors mounted on a robotic platform we built, allowing them to navigate in a controlled real-world urban environment. The designed city is occupied by cars, stop signs, a variety of residential and business buildings, and complex intersections mimicking an urban area. Furthermore, We have designed an intelligent infrastructure at one of the intersections in the city which helps safer and more efficient navigation in the presence of multiple cars and pedestrians. We have used the mini-city platform for the analysis of three different applications: city mapping, depth estimation in challenging occluded environments, and smart infrastructure for connected vehicles. Our smart infrastructure is among the first to develop and evaluate Vehicle-to-Infrastructure (V2I) communication at intersections. The intersection-related result shows how inaccuracy in perception, including mapping and localization, can affect safety. The proposed mini-city platform can be considered as a baseline environment for developing research and education in intelligent transportation systems.

Reinforcement Learning (RL) has shown remarkable progress in simulation environments, yet its application to real-world robotic tasks remains limited due to challenges in exploration and generalisation. To address these issues, we introduce NAVINACT, a framework that chooses when the robot should use classical motion planning-based navigation and when it should learn a policy. To further improve the efficiency in exploration, we use imitation data to bootstrap the exploration. NAVINACT dynamically switches between two modes of operation: navigating to a waypoint using classical techniques when away from the objects and reinforcement learning for fine-grained manipulation control when about to interact with objects. NAVINACT consists of a multi-head architecture composed of ModeNet for mode classification, NavNet for waypoint prediction, and InteractNet for precise manipulation. By combining the strengths of RL and Imitation Learning (IL), NAVINACT improves sample efficiency and mitigates distribution shift, ensuring robust task execution. We evaluate our approach across multiple challenging simulation environments and real-world tasks, demonstrating superior performance in terms of adaptability, efficiency, and generalization compared to existing methods. In both simulated and real-world settings, NAVINACT demonstrates robust performance. In simulations, NAVINACT surpasses baseline methods by 10-15\% in training success rates at 30k samples and by 30-40\% during evaluation phases. In real-world scenarios, it demonstrates a 30-40\% higher success rate on simpler tasks compared to baselines and uniquely succeeds in complex, two-stage manipulation tasks. Datasets and supplementary materials can be found on our website: {https://raaslab.org/projects/NAVINACT/}.

In this system report, we describe the models and methods we used for our participation in the PLABA2023 task on biomedical abstract simplification, part of the TAC 2023 tracks. The system outputs we submitted come from the following three categories: 1) domain fine-tuned T5-like models including Biomedical-T5 and Lay-SciFive; 2) fine-tuned BARTLarge model with controllable attributes (via tokens) BART-w-CTs; 3) ChatGPTprompting. We also present the work we carried out for this task on BioGPT finetuning. In the official automatic evaluation using SARI scores, BeeManc ranks 2nd among all teams and our model LaySciFive ranks 3rd among all 13 evaluated systems. In the official human evaluation, our model BART-w-CTs ranks 2nd on Sentence-Simplicity (score 92.84), 3rd on Term-Simplicity (score 82.33) among all 7 evaluated systems; It also produced a high score 91.57 on Fluency in comparison to the highest score 93.53. In the second round of submissions, our team using ChatGPT-prompting ranks the 2nd in several categories including simplified term accuracy score 92.26 and completeness score 96.58, and a very similar score on faithfulness score 95.3 to re-evaluated PLABA-base-1 (95.73) via human evaluations. Our codes, fine-tuned models, prompts, and data splits from the system development stage will be available at https://github.com/ HECTA-UoM/PLABA-MU

Making science more accessible remains a challenge even with much effort devoted on the producer and publisher side. As content producers, researchers are encouraged to engage directly with the public, either through social media (Davies, 2008; Hara et al., 2019; Knox and Hara, 2021) or by crafting more digestible manuscripts in research (Maurer et al., 2021) and practice (Grene et al., 2017). Funding agencies and renowned journals also encourage the communication of scientific findings in accessible language. For instance, the National Institutes of Health (NIH) advocate "clear and simple" principles when communicating with audiences with limited health literacy, and the Proceedings of the National Academy of Sciences of the United States of America (PNAS) requires authors to submit a significance statement accessible to non-experts (Berenbaum, 2021; Pool et al., 2021). As scientific research progresses with increased specialization and interdisciplinarity, it is acknowledged that the use of jargon effectively reduces communication costs among domain experts, particularly those responsible for reviewing submissions. This specialized language, however, can become incomprehensible to those without a similar research background. While efforts to share scientific findings in more accessible language from the producer side are gaining traction, widespread adoption is unlikely in the near future due to the inherent conflicts between the specialized nature of scholarly communication and the public-oriented dissemination of scientific findings. Within this effort to create understandable research findings and open science to broader communities, libraries--and our digital libraries in particular--have a role to play. Driven by this idea, we propose to start by improving the readability of abstracts from scholarly works through automated rewriting.

Ordinal regression is a fundamental problem within the field of computer vision, with customised well-trained models on specific tasks. While pre-trained vision-language models (VLMs) have exhibited impressive performance on various vision tasks, their potential for ordinal regression has received less exploration. In this study, we first investigate CLIP's potential for ordinal regression, from which we expect the model could generalise to different ordinal regression tasks and scenarios. Unfortunately, vanilla CLIP fails on this task, since current VLMs have a well-documented limitation of encapsulating compositional concepts such as number sense. We propose a simple yet effective method called NumCLIP to improve the quantitative understanding of VLMs. We disassemble the exact image to number-specific text matching problem into coarse classification and fine prediction stages. We discretize and phrase each numerical bin with common language concept to better leverage the available pre-trained alignment in CLIP. To consider the inherent continuous property of ordinal regression, we propose a novel fine-grained cross-modal ranking-based regularisation loss specifically designed to keep both semantic and ordinal alignment in CLIP's feature space. Experimental results on three general ordinal regression tasks demonstrate the effectiveness of NumCLIP, with 10% and 3.83% accuracy improvement on historical image dating and image aesthetics assessment task, respectively. Code is publicly available at https://github.com/xmed-lab/NumCLIP.

Teaching Computer Science (CS) by having students write programs by hand on paper has key pedagogical advantages: It allows focused learning and requires careful thinking compared to the use of Integrated Development Environments (IDEs) with intelligent support tools or "just trying things out". The familiar environment of pens and paper also lessens the cognitive load of students with no prior experience with computers, for whom the mere basic usage of computers can be intimidating. Finally, this teaching approach opens learning opportunities to students with limited access to computers. However, a key obstacle is the current lack of teaching methods and support software for working with and running handwritten programs. Optical character recognition (OCR) of handwritten code is challenging: Minor OCR errors, perhaps due to varied handwriting styles, easily make code not run, and recognizing indentation is crucial for languages like Python but is difficult to do due to inconsistent horizontal spacing in handwriting. Our approach integrates two innovative methods. The first combines OCR with an indentation recognition module and a language model designed for post-OCR error correction without introducing hallucinations. This method, to our knowledge, surpasses all existing systems in handwritten code recognition. It reduces error from 30\% in the state of the art to 5\% with minimal hallucination of logical fixes to student programs. The second method leverages a multimodal language model to recognize handwritten programs in an end-to-end fashion. We hope this contribution can stimulate further pedagogical research and contribute to the goal of making CS education universally accessible. We release a dataset of handwritten programs and code to support future research at https://github.com/mdoumbouya/codeocr