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Found 14 papers, 9 papers with code
Introducing v0.5 of the AI Safety Benchmark from MLCommons
We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0. 5 benchmark.
Finetuning Text-to-Image Diffusion Models for Fairness
The rapid adoption of text-to-image diffusion models in society underscores an urgent need to address their biases.
Multimodal Prompt Transformer with Hybrid Contrastive Learning for Emotion Recognition in Conversation
MPT embeds multimodal fusion information into each attention layer of the Transformer, allowing prompt information to participate in encoding textual features and being fused with multi-level textual information to obtain better multimodal fusion features.
Ranked #2 on
Emotion Recognition in Conversation
on IEMOCAP
AI and the EU Digital Markets Act: Addressing the Risks of Bigness in Generative AI
As AI technology advances rapidly, concerns over the risks of bigness in digital markets are also growing.
Towards Regulatable AI Systems: Technical Gaps and Policy Opportunities
There is increasing attention being given to how to regulate AI systems.
Inverse Scaling: When Bigger Isn't Better
Here, we present evidence for the claim that LMs may show inverse scaling, or worse task performance with increased scale, e. g., due to flaws in the training objective and data.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering
While conventional CF models are known for facing the challenges of the popularity bias that favors popular items, one may wonder "Whether the existing graph-based CF models alleviate or exacerbate popularity bias of recommender systems?"
Unsupervised Representation Learning of Player Behavioral Data with Confidence Guided Masking
Players of online games generate rich behavioral data during gaming.
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions -- training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones.
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.
RL4RS: A Real-World Dataset for Reinforcement Learning based Recommender System
In summary, the RL4RS (Reinforcement Learning for Recommender Systems), a new resource with special concerns on the reality gaps, contains two real-world datasets, data understanding tools, tuned simulation environments, related advanced RL baselines, batch RL baselines, and counterfactual policy evaluation algorithms.
Unsupervised Motion Representation Learning with Capsule Autoencoders
We propose the Motion Capsule Autoencoder (MCAE), which addresses a key challenge in the unsupervised learning of motion representations: transformation invariance.
Fair Representation: Guaranteeing Approximate Multiple Group Fairness for Unknown Tasks
Motivated by scenarios where data is used for diverse prediction tasks, we study whether fair representation can be used to guarantee fairness for unknown tasks and for multiple fairness notions simultaneously.
