A Robot Walks into a Bar: Can Language Models Serve as Creativity SupportTools for Comedy? An Evaluation of LLMs’ Humour Alignment with Comedians
Piotr Mirowski
Juliette Love
Kory Wallace Mathewson
Shakir Mohamed
We interviewed twenty professional comedians who perform live shows in front of audiences and who use artificial intelligence in their artis… (see more)tic process as part of 3-hour workshops on “AI x Comedy” conducted at the Edinburgh Festival Fringe in August 2023 and online. The workshop consisted of a comedy writing session with large language models (LLMs), a human-computer interaction questionnaire to assess the Creativity Support Index of AI as a writing tool, and a focus group interrogating the comedians’ motivations for and processes of using AI, as well as their ethical concerns about bias, censorship and copyright. Participants noted that existing moderation strategies used in safety filtering and instruction-tuned LLMs reinforced hegemonic viewpoints by erasing minority groups and their perspectives, and qualified this as a form of censorship. At the same time, most participants felt the LLMs did not succeed as a creativity support tool, by producing bland and biased comedy tropes, akin to “cruise ship comedy material from the 1950s, but a bit less racist”. Our work extends scholarship about the subtle difference between, one the one hand, harmful speech, and on the other hand, “offensive” language as a practice of resistance, satire and “punching up”. We also interrogate the global value alignment behind such language models, and discuss the importance of community-based value alignment and data ownership to build AI tools that better suit artists’ needs. Warning: this study may contain offensive language and discusses self-harm.
2024-05-31
ArXiv (preprint)
doi.org
arxiv.org
On shallow planning under partial observability
Randy Lefebvre
Audrey Durand
2024-05-31
rl-conference.cc/RLC/2024/Workshop/Deployable_RL (published)
openreview.net
openreview.net
On the Costs and Benefits of Adopting Lifelong Learning for Software Analytics -- Empirical Study on Brown Build and Risk Prediction
Doriane Olewicki
Sarra Habchi
Mathieu Nayrolles
Mojtaba Faramarzi
Sarath Chandar
Bram Adams
Nowadays, software analytics tools using machine learning (ML) models to, for example, predict the risk of a code change are well establishe… (see more)d. However, as the goals of a project shift over time, and developers and their habits change, the performance of said models tends to degrade (drift) over time. Current retraining practices typically require retraining a new model from scratch on a large updated dataset when performance decay is observed, thus incurring a computational cost; also there is no continuity between the models as the past model is discarded and ignored during the new model training. Even though the literature has taken interest in online learning approaches, those have rarely been integrated and evaluated in industrial environments. This paper evaluates the use of lifelong learning (LL) for industrial use cases at Ubisoft, evaluating both the performance and the required computational effort in comparison to the retraining-from-scratch approaches commonly used by the industry. LL is used to continuously build and maintain ML-based software analytics tools using an incremental learner that progressively updates the old model using new data. To avoid so-called"catastrophic forgetting"of important older data points, we adopt a replay buffer of older data, which still allows us to drastically reduce the size of the overall training dataset, and hence model training time.
2024-05-31
Proceedings of the 46th International Conference on Software Engineering: Software Engineering in Practice (published)
doi.org
arxiv.org
Towards a General GNN Framework for Combinatorial Optimization
Frederik Wenkel
Semih Cantürk
Michael Perlmutter
Guy Wolf
2024-05-31
ArXiv (preprint)
doi.org
arxiv.org
GraphAny: A Foundation Model for Node Classification on Any Graph
Jianan Zhao
Hesham Mostafa
Mikhail Galkin
Michael M. Bronstein
Zhaocheng Zhu
Jian Tang
2024-05-30
ArXiv (preprint)
doi.org
arxiv.org
GraphAny: A Foundation Model for Node Classification on Any Graph
Jianan Zhao
Hesham Mostafa
Mikhail Galkin
Michael M. Bronstein
Zhaocheng Zhu
Jian Tang
Foundation models that can perform inference on any new task without requiring specific training have revolutionized machine learning in vis… (see more)ion and language applications. However, applications involving graph-structured data remain a tough nut for foundation models, due to challenges in the unique feature- and label spaces associated with each graph. Traditional graph ML models such as graph neural networks (GNNs) trained on graphs cannot perform inference on a new graph with feature and label spaces different from the training ones. Furthermore, existing models learn functions specific to the training graph and cannot generalize to new graphs. In this work, we tackle these two challenges with a new foundational architecture for inductive node classification named GraphAny. GraphAny models inference on a new graph as an analytical solution to a LinearGNN, thereby solving the first challenge. To solve the second challenge, we learn attention scores for each node to fuse the predictions of multiple LinearGNNs. Specifically, the attention module is carefully parameterized as a function of the entropy-normalized distance-features between multiple LinearGNNs predictions to ensure generalization to new graphs. Empirically, GraphAny trained on the Wisconsin dataset with only 120 labeled nodes can effectively generalize to 30 new graphs with an average accuracy of 67.26\% in an inductive manner, surpassing GCN and GAT trained in the supervised regime, as well as other inductive baselines.
2024-05-30
ArXiv (preprint)
doi.org
arxiv.org
Sparsity regularization via tree-structured environments for disentangled representations
Elliot Layne
Dhanya Sridhar
Jason Hartford
Mathieu Blanchette
2024-05-30
ArXiv (preprint)
arxiv.org
arxiv.org
Sparsity regularization via tree-structured environments for disentangled representations
Elliot Layne
Dhanya Sridhar
Jason Hartford
Mathieu Blanchette
Many causal systems such as biological processes in cells can only be observed indirectly via measurements, such as gene expression. Causal … (see more)representation learning -- the task of correctly mapping low-level observations to latent causal variables -- could advance scientific understanding by enabling inference of latent variables such as pathway activation. In this paper, we develop methods for inferring latent variables from multiple related datasets (environments) and tasks. As a running example, we consider the task of predicting a phenotype from gene expression, where we often collect data from multiple cell types or organisms that are related in known ways. The key insight is that the mapping from latent variables driven by gene expression to the phenotype of interest changes sparsely across closely related environments. To model sparse changes, we introduce Tree-Based Regularization (TBR), an objective that minimizes both prediction error and regularizes closely related environments to learn similar predictors. We prove that under assumptions about the degree of sparse changes, TBR identifies the true latent variables up to some simple transformations. We evaluate the theory empirically with both simulations and ground-truth gene expression data. We find that TBR recovers the latent causal variables better than related methods across these settings, even under settings that violate some assumptions of the theory.
2024-05-30
ArXiv (preprint)
arxiv.org
arxiv.org
Deep Grokking: Would Deep Neural Networks Generalize Better?
Simin Fan
Razvan Pascanu
Martin Jaggi
Recent research on the grokking phenomenon has illuminated the intricacies of neural networks' training dynamics and their generalization be… (see more)haviors. Grokking refers to a sharp rise of the network's generalization accuracy on the test set, which occurs long after an extended overfitting phase, during which the network perfectly fits the training set. While the existing research primarily focus on shallow networks such as 2-layer MLP and 1-layer Transformer, we explore grokking on deep networks (e.g. 12-layer MLP). We empirically replicate the phenomenon and find that deep neural networks can be more susceptible to grokking than its shallower counterparts. Meanwhile, we observe an intriguing multi-stage generalization phenomenon when increase the depth of the MLP model where the test accuracy exhibits a secondary surge, which is scarcely seen on shallow models. We further uncover compelling correspondences between the decreasing of feature ranks and the phase transition from overfitting to the generalization stage during grokking. Additionally, we find that the multi-stage generalization phenomenon often aligns with a double-descent pattern in feature ranks. These observations suggest that internal feature rank could serve as a more promising indicator of the model's generalization behavior compared to the weight-norm. We believe our work is the first one to dive into grokking in deep neural networks, and investigate the relationship of feature rank and generalization performance.
2024-05-29
ArXiv (preprint)
doi.org
arxiv.org
Differentially Private Clustered Federated Learning
Saber Malekmohammadi
Afaf Taïk
Golnoosh Farnadi
Federated learning (FL), which is a decentralized machine learning (ML) approach, often incorporates differential privacy (DP) to provide ri… (see more)gorous data privacy guarantees. Previous works attempted to address high structured data heterogeneity in vanilla FL settings through clustering clients (a.k.a clustered FL), but these methods remain sensitive and prone to errors, further exacerbated by the DP noise. This vulnerability makes the previous methods inappropriate for differentially private FL (DPFL) settings with structured data heterogeneity. To address this gap, we propose an algorithm for differentially private clustered FL, which is robust to the DP noise in the system and identifies the underlying clients' clusters correctly. To this end, we propose to cluster clients based on both their model updates and training loss values. Furthermore, for clustering clients' model updates at the end of the first round, our proposed approach addresses the server's uncertainties by employing large batch sizes as well as Gaussian Mixture Models (GMM) to reduce the impact of DP and stochastic noise and avoid potential clustering errors. This idea is efficient especially in privacy-sensitive scenarios with more DP noise. We provide theoretical analysis to justify our approach and evaluate it across diverse data distributions and privacy budgets. Our experimental results show its effectiveness in addressing large structured data heterogeneity in DPFL.
2024-05-29
ArXiv (preprint)
arxiv.org
arxiv.org
Differentially Private Clustered Federated Learning
Saber Malekmohammadi
Afaf Taïk
Golnoosh Farnadi
2024-05-29
ArXiv (preprint)
arxiv.org
arxiv.org
Does learning the right latent variables necessarily improve in-context learning?
Sarthak Mittal
Eric Elmoznino
Leo Gagnon
Sangnie Bhardwaj
Dhanya Sridhar
Guillaume Lajoie
Large autoregressive models like Transformers can solve tasks through in-context learning (ICL) without learning new weights, suggesting ave… (see more)nues for efficiently solving new tasks. For many tasks, e.g., linear regression, the data factorizes: examples are independent given a task latent that generates the data, e.g., linear coefficients. While an optimal predictor leverages this factorization by inferring task latents, it is unclear if Transformers implicitly do so or if they instead exploit heuristics and statistical shortcuts enabled by attention layers. Both scenarios have inspired active ongoing work. In this paper, we systematically investigate the effect of explicitly inferring task latents. We minimally modify the Transformer architecture with a bottleneck designed to prevent shortcuts in favor of more structured solutions, and then compare performance against standard Transformers across various ICL tasks. Contrary to intuition and some recent works, we find little discernible difference between the two; biasing towards task-relevant latent variables does not lead to better out-of-distribution performance, in general. Curiously, we find that while the bottleneck effectively learns to extract latent task variables from context, downstream processing struggles to utilize them for robust prediction. Our study highlights the intrinsic limitations of Transformers in achieving structured ICL solutions that generalize, and shows that while inferring the right latents aids interpretability, it is not sufficient to alleviate this problem.
2024-05-29
ArXiv (preprint)
doi.org
openreview.net