Machine unlearning aims to efficiently eliminate the influence of specific training data, known as the forget set, from the model. However, existing unlearning methods for Large Language Models (LLMs) face a critical challenge: they rely solely on negative feedback to suppress responses related to the forget set, which often results in nonsensical or inconsistent outputs, diminishing model utility and posing potential privacy risks. To address this limitation, we propose a novel approach called Alternate Preference Optimization (AltPO), which combines negative feedback with in-domain positive feedback on the forget set. Additionally, we introduce new evaluation metrics to assess the quality of responses related to the forget set. Extensive experiments show that our approach not only enables effective unlearning but also avoids undesirable model behaviors while maintaining overall model performance.

Robust unlearning is crucial for safely deploying large language models (LLMs) in environments where data privacy, model safety, and regulatory compliance must be ensured. Yet the task is inherently challenging, partly due to difficulties in reliably measuring whether unlearning has truly occurred. Moreover, fragmentation in current methodologies and inconsistent evaluation metrics hinder comparative analysis and reproducibility. To unify and accelerate research efforts, we introduce OpenUnlearning, a standardized and extensible framework designed explicitly for benchmarking both LLM unlearning methods and metrics. OpenUnlearning integrates 13 state-of-the-art unlearning algorithms and 16 diverse evaluations across 3 leading benchmarks (TOFU, MUSE, and WMDP) and also enables analyses of forgetting behaviors across 450+ publicly released checkpoints. Leveraging OpenUnlearning, we propose a novel meta-evaluation benchmark focused specifically on assessing the faithfulness and robustness of evaluation metrics themselves. We also benchmark diverse unlearning methods and provide a comparative analysis against an extensive evaluation suite. Overall, we establish a clear, community-driven pathway toward rigorous development in LLM unlearning research.

Sketching is a visual thinking tool available to humans for several decades. With the advent of modern sketching technologies, artists use sketches to express and iterate their ideas. To accelerate sketch-based ideation and illustration workflows, we propose a novel framework, SketchBuddy, which retrieves diverse fine-grained object suggestions to enrich a sketch and coherently inserts it into the scene. Sketchbuddy detects objects in the input sketch to estimate the scene context which is then utilized for the recommendation and insertion. We propose a novel multi-modal transformer based framework for obtaining context-aware fine-grained object recommendations. We train a CNN-based bounding box classifier to extract information from the input scene and the recommended objects to infer plausible locations for insertion. While prior works focus on sketches at object-level only, SketchBuddy is the first work in the direction of scene-level sketching assistance. Our extensive evaluations comparing SketchBuddy against competing baselines across several metrics and agreements with human preferences demonstrate its value on several aspects.