Multilingual LLM outputs can vary across sociocultural contexts. However, evidence of cultural grounding can be misleading: identity labels may be inferred from explicit or indirect textual cues, while names and wording can reveal the source language. Treating all these signals as evidence of cultural grounding may obscure potential biases. We present a human-validated, multi-agent audit that separates three questions: whether outputs reproduce social biases, whether identity groups are represented differently, and whether outputs reflect cross-cultural patterns. The study analyzes 89,253 outputs from 12 LLMs in English, French, and Chinese, spanning 18 occupations and three task conditions. We find that bias representation varies systematically across languages and tasks. Removing direct identity cues sharply reduces identity-label prediction in English and Chinese, but has a much smaller effect in French. Across all language-genre settings, the cultural context associated with the source language receives the highest average relevance score, with moderate agreement between automated and human ratings. However, the ability to identify the source language drops substantially after translation and again after masking names. Without these controls, multilingual audits may mistake surface cues for cultural understanding, leading to misleading conclusions about cross-cultural variation and bias. Our audit offers a practical framework for separating such shortcuts from more meaningful cross-cultural patterns.
Yuanjun Feng, Tanzhou Liu, S. Feuerriegel et al.· 0 citations
This framework is the first to address catastrophic forgetting by leveraging models in CL as their own memory buffers by exploiting an implicit bias of gradient-based neural networks due to which these converge to margin maximization points.
Pascal Janetzky, T. Schlagenhauf, S. Feuerriegel· Proceedings of the 32nd ACM...· 0 citations
Continual learning (CL) aims to incrementally update machine learning models from a stream of data without forgetting previously acquired knowledge. CL is highly relevant in many real-world applications such as manufacturing, where storing historical data for retraining is often infeasible due to volume, governance, or system constraints. Yet, a common challenge in continual learning (CL) is catastrophic forgetting, where the performance on old tasks drops after new, additional tasks are learned. In this paper, we propose a novel framework called ReCL to slow down forgetting in CL. Our framework exploits an implicit bias of gradient-based neural networks due to which these converge to margin maximization points. Such convergence points allow us to reconstruct old data from previous tasks, which we then combine with the current training data. Our framework is flexible and can be applied on top of existing, state-of-the-art CL methods. We first demonstrate the performance gain from our framework across a large series of experiments on three established public CL datasets (MNIST, CIFAR10, TinyImageNet), a public industrial dataset (SECOM), and across two different scenarios (class incremental and domain incremental learning). Then, we evaluate the performance of ReCL for predictive maintenance in a manufacturing environment at Bosch, a global engineering company, using an internal real-world time-series feature dataset captured from a high-volume precision-machining process. Lastly, we apply our framework to streaming machine data from real-world industrial data at Bosch. Across all our experiments, we find large performance gains through ReCL. To the best of our knowledge, our framework is the first to address catastrophic forgetting by leveraging models in CL as their own memory buffers.
Pascal Janetzky, T. Schlagenhauf, Michael Klar et al.· Proceedings of the 32nd ACM...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
