← Back to all news
arXiv Machine Learning By Pol G. Recasens, Alberto Gutierrez, Jordi Torres, Josep. Ll Berral, Javier Carnerero-Cano, Anisa Halimi, Kieran Fraser

In-Context Bias Propagation in LLM-Based Tabular Data Generation

Read the original on arXiv Machine Learning →

The paper investigates how biases present in in-context examples used by Large Language Models (LLMs) for synthetic tabular data generation can propagate to the generated data, causing global statistical distortions even when the biases are mild. It also explores an adversarial scenario where a malicious actor can inject bias through a subset of examples, thereby compromising the fairness of downstream classifiers for protected subgroups. The authors evaluate prompt-side mitigation strategies, such as preprocessing examples and instruction-based defenses, noting that while these can reduce disparity, LLMs remain highly sensitive to adversarial prompts, revealing a significant vulnerability in data generation pipelines for sensitive domains.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv AI
Jun 26

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes

arXiv:2509. 09960v2 Announce Type: replace-cross Abstract: Synthetic tabular data generation is increasingly essential in machine learning, supporting downstream applications when real-world, high-quality tabular data is insufficient.

By Mingxuan Jiang, Keyang Chen, Yongxin Wang, Yongsheng Zhao, Ziyue Dai, Yicun Liu, Zeping Li, Qiuyang Zhang, Hongyi Nie, Hongbin Zhu, Sen Liu, Guangnan Ye, Hongfeng Chai
llmsdiffusionfine-tuningbenchmarkssafety
More like this →
arXiv AI
Sep 1

Hidden Threat in Synthetic Data: Covert Targeted Bias Injection through Benign Text

The paper examines how synthetic data can be used to covertly inject targeted social biases into aligned large language models. By having a misaligned teacher model generate filtered synthetic datasets in domains like creative writing and code generation, the authors fine‑tune aligned student models and demonstrate that the benign‑looking data can transmit biases while preserving general task performance. The study highlights a security risk in synthetic data‑driven LLM training pipelines and proposes log‑linearity‑based scoring as a potential safeguard.

By Minkyung Cho, Jihyo Kim, SeungWoo Song, Junghun Yuk, Minjoon Kee, Hoyun Song, KyungTae Lim
llmsfine-tuningsafety
More like this →
arXiv AI
Sep 1

Watch your steps: Dormant Adversarial Behaviors that Activate upon LLM Finetuning

The paper introduces FAB, an attack that uses meta‑learning to embed dormant adversarial behaviors into large language models (LLMs). These behaviors remain inactive until the model is finetuned by downstream users, at which point the model can exhibit unwanted actions such as unsolicited advertising, jailbreakability, or over‑refusal. FAB is shown to be effective across multiple LLMs and resilient to various finetuning settings.

By Thibaud Gloaguen, Mark Vero, Robin Staab, Martin Vechev
llmsreinforcement-learningfine-tuningefficiencysafety
More like this →
arXiv Machine Learning
Jul 16

When T2I Synthetic Data Backfires: Amplified Privacy Risks in Real-Synthetic Mix Training

arXiv:2607. 13541v1 Announce Type: cross Abstract: To overcome data scarcity and privacy constraints in data collection, it has become standard practice across academia and industry to augment real training data with text-to-image (T2I)-generated synthetic data, a paradigm we term Real-Synthetic Mix-Training (RSMT).

By Na Li, Boyu Kuang, Hongsheng Hu, Liquan Chen, Hyoungshick Kim, Yansong Gao, Anmin Fu
diffusionsafety
More like this →