Large Language Models (LLMs) have been found to memorize and recite some of the textual sequences from their training set verbatim, raising broad concerns about privacy and copyright issues. This Textual Sequence Memorization (TSM) phenomenon leads to a high demand to regulate LLM output to prevent generating certain memorized text that a user wants to be forgotten. However, our empirical study reveals that existing methods for TSM erasure fail to unlearn large numbers of memorized samples without substantially jeopardizing the model utility. To achieve a better trade-off between the effectiveness of TSM erasure and model utility in LLMs, our paper proposes a new method, named Entropy Maximization with Selective Optimization (EMSO), where the model parameters are updated sparsely based on novel optimization and selection criteria, in a manner that does not require additional models or data other than that in the forget set. More specifically, we propose an entropy-based loss that is shown to lead to more stable optimization and better preserves model utility than existing methods. In addition, we propose a contrastive gradient metric that takes both the gradient magnitude and direction into consideration, so as to localize model parameters to update in a sparse model updating scehme. Extensive experiments across three model scales demonstrate that our method excels in handling large-scale forgetting requests while preserving model ability in language generation and understanding.

In the domain of text-to-image generative models, biases inherent in training datasets often propagate into generated content, posing significant ethical challenges, particularly in socially sensitive contexts. We introduce FairCoT, a novel framework that enhances fairness in text-to-image models through Chain-of-Thought (CoT) reasoning within multimodal generative large language models. FairCoT employs iterative CoT refinement to systematically mitigate biases, and dynamically adjusts textual prompts in real time, ensuring diverse and equitable representation in generated images. By integrating iterative reasoning processes, FairCoT addresses the limitations of zero-shot CoT in sensitive scenarios, balancing creativity with ethical responsibility. Experimental evaluations across popular text-to-image systems—including DALL-E and various Stable Diffusion variants—demonstrate that FairCoT significantly enhances fairness and diversity without sacrificing image quality or semantic fidelity. By combining robust reasoning, lightweight deployment, and extensibility to multiple models, FairCoT represents a promising step toward more socially responsible and transparent AI-driven content generation.

Multilingual vision–language models (VLMs) promise universal image–text retrieval, yet their social biases remain under‐explored.We perform the first systematic audit of four public multilingual CLIP variants—M‐CLIP, NLLB‐CLIP, CAPIVARA‐CLIP, and the debiased SigLIP‐2—covering ten languages that differ in resource availability and morphological gender marking.Using balanced subsets of FairFace and the PATA stereotype suite in a zero‐shot setting, we quantify race and gender bias and measure stereotype amplification.Contrary to the intuition that multilinguality mitigates bias, every model exhibits stronger gender skew than its English‐only baseline.CAPIVARA‐CLIP shows its largest biases precisely in the low‐resource languages it targets, while the shared encoder of NLLB‐CLIP and SigLIP‐2 transfers English gender stereotypes into gender‐neutral languages; loosely coupled encoders largely avoid this leakage.Although SigLIP‐2 reduces agency and communion skews, it inherits—and in caption‐sparse contexts (e.g., Xhosa) amplifies—the English anchor’s crime associations.Highly gendered languages consistently magnify all bias types, yet gender‐neutral languages remain vulnerable whenever cross‐lingual weight sharing imports foreign stereotypes.Aggregated metrics thus mask language‐specific “hot spots,” underscoring the need for fine‐grained, language‐aware bias evaluation in future multilingual VLM research.

This paper is on the problem of Knowledge-Based Visual Question Answering (KB-VQA). Recent works have emphasized the significance of incorporating both explicit (through external databases) and implicit (through LLMs) knowledge to answer questions requiring external knowledge effectively. A common limitation of such approaches is that they consist of relatively complicated pipelines and often heavily rely on accessing GPT-3 API. Our main contribution in this paper is to propose a much simpler and readily reproducible pipeline which, in a nutshell, is based on efficient in-context learning by prompting LLaMA (1 and 2) using question-informative captions as contextual information. Contrary to recent approaches, our method is training-free, does not require access to external databases or APIs, and yet achieves state-of-the-art accuracy on the OK-VQA and A-OK-VQA datasets. Finally, we perform several ablation studies to understand important aspects of our method. Our code is publicly available at https://github.com/alexandrosXe/ASimple-Baseline-For-Knowledge-Based-VQA