Domain generalization is a critical challenge for machine learning systems. Prior domain generalization methods focus on extracting domain-invariant features across several stationary domains to enable generalization to new domains. However, in non-stationary tasks where new domains evolve in an underlying continuous structure, such as time, merely extracting the invariant features is insufficient for generalization to the evolving new domains. Nevertheless, it is non-trivial to learn both evolving and invariant features within a single model due to their conflicts. To bridge this gap, we build causal models to characterize the distribution shifts concerning the two patterns, and propose to learn both dynamic and invariant features via a new framework called Mutual Information-Based Sequential Autoencoders (MISTS). MISTS adopts information theoretic constraints onto sequential autoencoders to disentangle the dynamic and invariant features, and leverage a domain adaptive classifier to make predictions based on both evolving and invariant information. Our experimental results on both synthetic and real-world datasets demonstrate that MISTS succeeds in capturing both evolving and invariant information, and present promising results in evolving domain generalization tasks.

Large Language Models (LLMs), with their flexible generation abilities, can be powerful data sources in domains with few or no available corpora. However, problems like hallucinations and biases limit such applications. In this case study, we pick nutrition counselling, a domain lacking any public resource, and show that high-quality datasets can be gathered by combining LLMs, crowd-workers and nutrition experts. We first crowd-source and cluster a novel dataset of diet-related issues, then work with experts to prompt ChatGPT into producing related supportive text. Finally, we let the experts evaluate the safety of the generated text. We release HAI-coaching, the first expert-annotated nutrition counselling dataset containing ~2.4K dietary struggles from crowd workers, and ~97K related supportive texts generated by ChatGPT. Extensive analysis shows that ChatGPT while producing highly fluent and human-like text, also manifests harmful behaviours, especially in sensitive topics like mental health, making it unsuitable for unsupervised use.

This paper provides an overview of the morphology and syntax of the Tamil language, focusing on its contemporary usage. The paper also highlights the complexity and richness of Tamil in terms of its morphological and syntactic features, which will be useful for linguists analysing the language and conducting comparative studies. In addition, the paper will be useful for those developing computational resources for the Tamil language. It is proven as a rule-based morphological analyser cum generator and a computational grammar for Tamil have already been developed based on this paper. To enhance accessibility for a broader audience, the analysis is conducted without relying on any specific grammatical formalism.

The continual learning (CL) ability is vital for deploying large language models (LLMs) in the dynamic world. Based on parameter-efficient tuning (PET), existing methods devise the learning module and the selection module to handle the challenges of catastrophic forgetting (CF) and knowledge transfer (KT) in CL. The learning module allocates separate PET blocks for each continually emerged task and the selection module function to choose the correct one for the input at testing time. However, there are limitations in their deigns of both modules and they ignore the potential of aligning the two module to address CF and KT simultaneously. To this end, we propose a novel Dual Attention Framework , to align the PET learning and selection via the Dual Attentive Learning\&Selection module. Extensive Experiments on two CL benchmarks demonstrate the superiority of DAPT to resist CF and facilitate KT at the same time. Moreover, DAPT exhibits the superiority when we scale it to different model sizes (from 770M to 11B) and unseen tasks.

This paper presents null-shot prompting. Null-shot prompting exploits hallucination in large language models (LLMs) by instructing LLMs to utilize information from the "Examples" section that never exists within the provided context to perform a task. While reducing hallucination is crucial and non-negligible for daily and critical uses of LLMs, we propose that in the current landscape in which these LLMs still hallucinate, it is possible, in fact, to exploit hallucination to increase performance in performing tasks compared to standard zero-shot prompting. Experiments with six LLMs show improvements in performance across the majority of eight datasets, including reading comprehension, arithmetic reasoning, and closed-book question answering. The observed inconsistency in increased relative performance across LLMs also potentially indicates a different degree of inherent hallucination in each model. These differences show that it is possible to utilize null-shot prompting as a way to detect degrees of hallucination in LLMs using existing benchmarking datasets. We also perform ablation studies, including experimenting with a modified version of null-shot prompting that incorporates ideas from zero-shot chain-of-thought prompting, which shows different trends of results.

Large language models (LLMs) have seen considerable advancements in natural language understanding tasks, yet there remains a gap to bridge before attaining true artificial general intelligence, especially concerning shortcomings in mathematical reasoning capabilities. We postulate that the inherent nature of LLM training, which focuses on predicting probabilities of next token, presents challenges in effectively modeling mathematical reasoning that demands exact calculations, both from data-driven and theoretical standpoints. In this paper, we address this challenge by enriching the data landscape and introducing a novel math dataset, enhanced with a capability to utilize a Python code interpreter. This dataset is derived from GSM8K and MATH and has been further refined through a combination of GPT-4 annotations, human review, and self-training processes, where the errors in the original GSM8K training set have been fixed. Additionally, we propose a tentative, easily replicable protocol for the fine-tuning of math-specific LLMs, which has led to a significant improvement in the performance of a 7B-parameter LLM on the GSM8K and MATH datasets. We are committed to advancing the field of mathematical reasoning in LLMs and, to that end, we have made the model checkpoints and will make the dataset publicly available. We hope this will facilitate further research and development within the community.

The second run of a five-week free course for anyone on AI starts on the 15th January 2023. We and AI were delighted to work with The Scottish AI Alliance designing content for their course designed to give the general public of Scotland (and beyond!) an introduction to the world of AI. "Living with AI" is a great and rare commitment to making general level AI literacy attainable to everyone to enable a greater understanding of the uses and questions posed by the range of tools and technologies, and to explore their future potential. Although creating materials was challenging due to the rapid changes and innovation, proliferating hype and platform restrictions, feedback from the first run of the course shows how worthwhile the effort was. Each week has a specific topic and the course is made up of written articles, videos from AI experts across Scotland, audio clips, reflective and activity-based exercises, quizzes and opportunities to collaborate with other learners. All content has been refreshed for 2024 and will be released at once, so learners can go at their own pace in their own time.

Artificial intelligence may soon affect more than half of all jobs in the UK, a new study from the IMF has warned. Across the world, 40 per cent of all occupations are likely to be affected by AI. But in advanced economies like the UK, the impact is expected to be even more pronounced, with 60 per cent of all workers feeling the impact. The IMF warns that, even where AI's effects are positive, computer automation is likely to drive wealth inequality. Highly paid professions will see AI boost their wages while lower paid roles are at a significant risk of pay cuts and lay-offs.

Saliency maps have proven to be a highly efficacious approach for explicating the decisions of Convolutional Neural Networks. However, extant methodologies predominantly rely on gradients, which constrain their ability to explicate complex models. Furthermore, such approaches are not fully adept at leveraging negative gradient information to improve interpretive veracity. In this study, we present a novel concept, termed positive and negative excitation, which enables the direct extraction of positive and negative excitation for each layer, thus enabling complete layer-by-layer information utilization sans gradients. To organize these excitations into final saliency maps, we introduce a double-chain backpropagation procedure. A comprehensive experimental evaluation, encompassing both binary classification and multi-classification tasks, was conducted to gauge the effectiveness of our proposed method. Encouragingly, the results evince that our approach offers a significant improvement over the state-of-the-art methods in terms of salient pixel removal, minor pixel removal, and inconspicuous adversarial perturbation generation guidance. Additionally, we verify the correlation between positive and negative excitations.

In the rapidly evolving field of artificial intelligence (AI), a paradigm shift is underway. We are witnessing the transition from specialized, fragmented deep learning models to versatile, one-size-fits-all foundation models. These advanced AI systems are capable of operating in an open-world context, interacting with open vocabularies and image pixels for unseen AI tasks, i.e., zero-shot abilities. They are exemplified by (1) Large Language Models (LLMs) such as GPTs [39] that can ingest almost every NLP task in the form as a prompt; (2) Vision Transformers Models (ViTs) such as Masked Autoencoder [133] that can handle various downstream vision tasks; (3) Latent Diffusion Models (LDMs) such as Stable Diffusion [310] that generate high-quality images with arbitrary text-based prompts; (4) Multimodal models such as CLIP [296] and ImageBind [116] that map different modal data into the same latent space and are widely used as backbone for cross-modality tasks like image retrieval/search and visual-question answering. Such flexibility and generality marks a significant departure from the earlier era of AI, setting a new standard for how AI interfaces with the world. The success of these foundation models is deeply rooted in their scalability: unlike their predecessors, these models' accuracy and generalization ability can continuously expand with more data or parameters, without altering the underlying simple algorithms and architectures.