Instruction subsets are heuristics that can reduce the size of the inductive programming search space by tens of orders of magnitude. Comprising many overlapping subsets of different sizes, they serve as predictions of the instructions required to code a solution for any problem. Currently, this approach employs a single, large family of subsets meaning that some problems can search thousands of subsets before a solution is found. In this paper we introduce the use of test case type signatures as hyper-heuristics to select one of many, smaller families of instruction subsets. The type signature for any set of test cases maps directly to a single family and smaller families mean that fewer subsets need to be considered for most problems. Having many families also permits subsets to be reordered to better reflect their relative occurrence in human code - again reducing the search space size for many problems. Overall the new approach can further reduce the size of the inductive programming search space by between 1 and 3 orders of magnitude, depending on the type signature. Larger and more consistent reductions are possible through the use of more sophisticated type systems. The potential use of additional test case features as hyper-heuristics and some other possible future work is also briefly discussed.

The breakthroughs and benefits attributed to big data and, consequently, to machine learning (ML) - or AIsystems [1, 2], have also resulted in making prevalent how these systems are capable of producing unexpected, biased, and in some cases, undesirable output [3, 4, 5]. Seminal work on bias (i.e., prejudice for, or against one person, or group, especially in a way considered to be unfair) in the context of ML systems demonstrates how facial recognition tools and popular search engines can exacerbate demographic disparities, worsening the marginalization of minorities at the individual and group level [6, 7]. Further, biases in news recommenders and social media feeds actively play a role in conditioning and manipulating people's behavior and amplifying individual and public opinion polarization [8, 9]. In this context, the last few years have seen the consolidation of the Trustworthy AI framework, led in large part by regulatory bodies [10], with the objective of guiding commercial AI development to proactively account for ethical, legal, and technical dimensions [11]. Furthermore, this framework is also accompanied by the call to establish standards across the field in order to ensure AI systems are safe, secure and fair upon deployment [11]. In terms of AI bias, many efforts have been concentrated in devising methods that can improve its identification, understanding, measurement, and mitigation [12]. For example, the special publication prepared by the National Institute of Standards and Technology (NIST) proposes a thorough, however not exhaustive, categorization of different types of bias in AI beyond common computational definitions (see Figure 1 for core hierarchy) [13]. In this same direction, some scholars advocate for practices that account for the characteristics of ML pipelines (i.e., datasets, ML algorithms, and user interaction loop) [14] to enable actors concerned with its research, development, regulation, and use, to inspect all the actions performed across the engineering process, with the objective to increase trust placed not only on the development processes, but on the systems themselves [15, 16, 17, 18].

We present an architectural alternative to global digital platforms termed grassroots, designed to serve the social, economic, civic, and political needs of local digital communities, as well as their federation. Grassroots platforms may offer local communities an alternative to global digital platforms while operating solely on the smartphones of their members, forsaking any global resources other than the network itself. Such communities may form digital economies without initial capital or external credit, exercise sovereign democratic governance, and federate, ultimately resulting in the grassroots formation of a global digital democracy.

The recent success of large language models (LLMs) trained on static, pre-collected, general datasets has sparked numerous research directions and applications. One such direction addresses the non-trivial challenge of integrating pre-trained LLMs into dynamic data distributions, task structures, and user preferences. Pre-trained LLMs, when tailored for specific needs, often experience significant performance degradation in previous knowledge domains -- a phenomenon known as "catastrophic forgetting". While extensively studied in the continual learning (CL) community, it presents new manifestations in the realm of LLMs. In this survey, we provide a comprehensive overview of the current research progress on LLMs within the context of CL. This survey is structured into four main sections: we first describe an overview of continually learning LLMs, consisting of two directions of continuity: vertical continuity (or vertical continual learning), i.e., continual adaptation from general to specific capabilities, and horizontal continuity (or horizontal continual learning), i.e., continual adaptation across time and domains (Section 3). We then summarize three stages of learning LLMs in the context of modern CL: Continual Pre-Training (CPT), Domain-Adaptive Pre-training (DAP), and Continual Fine-Tuning (CFT) (Section 4). Then we provide an overview of evaluation protocols for continual learning with LLMs, along with the current available data sources (Section 5). Finally, we discuss intriguing questions pertaining to continual learning for LLMs (Section 6). The full list of papers examined in this survey is available at https://github.com/Wang-ML-Lab/llm-continual-learning-survey.

Data Shapley provides a principled framework for attributing data's contribution within machine learning contexts. However, existing approaches require re-training models on different data subsets, which is computationally intensive, foreclosing their application to large-scale models. Furthermore, they produce the same attribution score for any models produced by running the learning algorithm, meaning they cannot perform targeted attribution towards a specific model obtained from a single run of the algorithm. This paper introduces In-Run Data Shapley, which addresses these limitations by offering scalable data attribution for a target model of interest. In its most efficient implementation, our technique incurs negligible additional runtime compared to standard model training. This dramatic efficiency improvement makes it possible to perform data attribution for the foundation model pretraining stage for the first time. We present several case studies that offer fresh insights into pretraining data's contribution and discuss their implications for copyright in generative AI and pretraining data curation.

Devindra: because like you could just always just take a photo of something and be like, yeah, that's how we wonder if it's going to Cherlynn: be a ram hog.

The Center for Investigative Reporting (CIR), which publishes Mother Jones and Reveal, said on Thursday that it had filed the lawsuit accusing the tech firms of using its content without permission in a "rebuke to artificial intelligence and its exploitative practices". "OpenAI and Microsoft started vacuuming up our stories to make their product more powerful, but they never asked for permission or offered compensation, unlike other organisations that license our material," Monika Bauerlein, CEO of the Center for Investigative Reporting, said in a statement. The work of journalists, at CIR and everywhere, is valuable, and OpenAI and Microsoft know it." OpenAI and Microsoft did not immediately respond to requests for comment. OpenAI's ChatGPT chatbot relies on vast quantities of information scraped from the internet, including news sites, to respond to users' queries.

As the development of large language models (LLMs) rapidly advances, securing these models effectively without compromising their utility has become a pivotal area of research. However, current defense strategies against jailbreak attacks (i.e., efforts to bypass security protocols) often suffer from limited adaptability, restricted general capability, and high cost. To address these challenges, we introduce SafeAligner, a methodology implemented at the decoding stage to fortify defenses against jailbreak attacks. We begin by developing two specialized models: the Sentinel Model, which is trained to foster safety, and the Intruder Model, designed to generate riskier responses. SafeAligner leverages the disparity in security levels between the responses from these models to differentiate between harmful and beneficial tokens, effectively guiding the safety alignment by altering the output token distribution of the target model. Extensive experiments show that SafeAligner can increase the likelihood of beneficial tokens, while reducing the occurrence of harmful ones, thereby ensuring secure alignment with minimal loss to generality.

Traditional keyphrase prediction methods predict a single set of keyphrases per document, failing to cater to the diverse needs of users and downstream applications. To bridge the gap, we introduce on-demand keyphrase generation, a novel paradigm that requires keyphrases that conform to specific high-level goals or intents. For this task, we present MetaKP, a large-scale benchmark comprising four datasets, 7500 documents, and 3760 goals across news and biomedical domains with human-annotated keyphrases. Leveraging MetaKP, we design both supervised and unsupervised methods, including a multi-task fine-tuning approach and a self-consistency prompting method with large language models. The results highlight the challenges of supervised fine-tuning, whose performance is not robust to distribution shifts. By contrast, the proposed self-consistency prompting approach greatly improves the performance of large language models, enabling GPT-4o to achieve 0.548 SemF1, surpassing the performance of a fully fine-tuned BART-base model. Finally, we demonstrate the potential of our method to serve as a general NLP infrastructure, exemplified by its application in epidemic event detection from social media.

Our society collects data on people for a wide range of applications, from building a census for policy evaluation to running meaningful clinical trials. To collect data, we typically sample individuals with the goal of accurately representing a population of interest. However, current sampling processes often collect data opportunistically from data sources, which can lead to datasets that are biased and not representative, i.e., the collected dataset does not accurately reflect the distribution of demographics of the true population. This is a concern because subgroups within the population can be under- or over-represented in a dataset, which may harm generalizability and lead to an unequal distribution of benefits and harms from downstream tasks that use such datasets (e.g., algorithmic bias in medical decision-making algorithms). In this paper, we assess the relationship between dataset representativeness and group-fairness of classifiers trained on that dataset. We demonstrate that there is a natural tension between dataset representativeness and classifier fairness; empirically we observe that training datasets with better representativeness can frequently result in classifiers with higher rates of unfairness. We provide some intuition as to why this occurs via a set of theoretical results in the case of univariate classifiers. We also find that over-sampling underrepresented groups can result in classifiers which exhibit greater bias to those groups. Lastly, we observe that fairness-aware sampling strategies (i.e., those which are specifically designed to select data with high downstream fairness) will often over-sample members of majority groups. These results demonstrate that the relationship between dataset representativeness and downstream classifier fairness is complex; balancing these two quantities requires special care from both model- and dataset-designers.