LoRS: Efficient Low-Rank Adaptation for Sparse Large Language Model

Existing low-rank adaptation (LoRA) methods face challenges on sparse large language models (LLMs) due to the inability to maintain sparsity. Recent works introduced methods that maintain sparsity by augmenting LoRA techniques with additional masking mechanisms. Despite these successes, such approaches suffer from an increased memory and computation overhead, which affects efficiency of LoRA methods. In response to this limitation, we introduce LoRS, an innovative method designed to achieve both memory and computation efficiency when fine-tuning sparse LLMs. To mitigate the substantial memory and computation demands associated with preserving sparsity, our approach incorporates strategies of weight recompute and computational graph rearrangement. In addition, we also improve the effectiveness of LoRS through better adapter initialization. These innovations lead to a notable reduction in memory and computation consumption during the fine-tuning phase, all while achieving performance levels that outperform existing LoRA approaches.

Human Grasp Generation for Rigid and Deformable Objects with Decomposed VQ-VAE

Generating realistic human grasps is crucial yet challenging for object manipulation in computer graphics and robotics. Current methods often struggle to generate detailed and realistic grasps with full finger-object interaction, as they typically rely on encoding the entire hand and estimating both posture and position in a single step. Additionally, simulating object deformation during grasp generation is still difficult, as modeling such deformation requires capturing the comprehensive relationship among points of the object's surface. To address these limitations, we propose a novel improved Decomposed Vector-Quantized Variational Autoencoder (DVQ-VAE-2), which decomposes the hand into distinct parts and encodes them separately. This part-aware architecture allows for more precise management of hand-object interactions. Furthermore, we introduce a dual-stage decoding strategy that first predicts the grasp type under skeletal constraints and then identifies the optimal grasp position, enhancing both the realism and adaptability of the model to unseen interactions. Furthermore, we introduce a new Mesh UFormer as the backbone network to extract the hierarchical structural representations from the mesh and propose a new normal vector-guided position encoding to simulate the hand-object deformation. In experiments, our model achieves a relative improvement of approximately 14.1% in grasp quality compared to state-of-the-art methods across four widely used benchmarks. Our comparisons with other backbone networks show relative improvements of 2.23% in Hand-object Contact Distance and 5.86% in Quality Index on deformable and rigid object based datasets, respectively. Our source code and model are available at https://github.com/florasion/D-VQVAE.

Joint Knowledge Editing for Information Enrichment and Probability Promotion

Knowledge stored in large language models requires timely updates to reflect the dynamic nature of real-world information. To update the knowledge, most knowledge editing methods focus on the low layers, since recent probes into the knowledge recall process reveal that the answer information is enriched in low layers. However, these probes only and could only reveal critical recall stages for the original answers, while the goal of editing is to rectify model's prediction for the target answers. This inconsistency indicates that both the probe approaches and the associated editing methods are deficient. To mitigate the inconsistency and identify critical editing regions, we propose a contrast-based probe approach, and locate two crucial stages where the model behavior diverges between the original and target answers: Information Enrichment in low layers and Probability Promotion in high layers. Building upon the insights, we develop the Joint knowledge Editing for information Enrichment and probability Promotion (JEEP) method, which jointly edits both the low and high layers to modify the two critical recall stages. Considering the mutual interference and growing forgetting due to dual modifications, JEEP is designed to ensure that updates to distinct regions share the same objectives and are complementary. We rigorously evaluate JEEP by editing up to thousands of facts on various models, i.e., GPT-J (6B) and LLaMA (7B), and addressing diverse editing objectives, i.e., adding factual and counterfactual knowledge. In all tested scenarios, JEEP achieves best performances, validating the effectiveness of the revealings of our probe approach and the designs of our editing method. Our code and data are available at https://github.com/Eric8932/JEEP.

GraphLoRA: Empowering LLMs Fine-Tuning via Graph Collaboration of MoE

Low-Rank Adaptation (LoRA) is a parameter-efficient fine-tuning method that has been widely adopted in various downstream applications of LLMs. Together with the Mixture-of-Expert (MoE) technique, fine-tuning approaches have shown remarkable improvements in model capability. However, the coordination of multiple experts in existing studies solely relies on the weights assigned by the simple router function. Lack of communication and collaboration among experts exacerbate the instability of LLMs due to the imbalance load problem of MoE. To address this issue, we propose a novel MoE graph-based LLM fine-tuning framework GraphLoRA, in which a graph router function is designed to capture the collaboration signals among experts by graph neural networks (GNNs). GraphLoRA enables all experts to understand input knowledge and share information from neighbor experts by aggregating operations. Besides, to enhance each expert's capability and their collaborations, we design two novel coordination strategies: the Poisson distribution-based distinction strategy and the Normal distribution-based load balance strategy. Extensive experiments on four real-world datasets demonstrate the effectiveness of our GraphLoRA in parameter-efficient fine-tuning of LLMs, showing the benefits of facilitating collaborations of multiple experts in the graph router of GraphLoRA.

Training Verification-Friendly Neural Networks via Neuron Behavior Consistency

Formal verification provides critical security assurances for neural networks, yet its practical application suffers from the long verification time. This work introduces a novel method for training verification-friendly neural networks, which are robust, easy to verify, and relatively accurate. Our method integrates neuron behavior consistency into the training process, making neuron activation states consistent across different inputs in a local neighborhood, reducing the number of unstable neurons and tightening the bounds of neurons thereby enhancing neural network verifiability. We evaluated our method using the MNIST, Fashion-MNIST, and CIFAR-10 datasets across various network architectures. The results of the experiment demonstrate that networks trained using our method are verification-friendly across different radii and different model architectures, whereas other tools fail to maintain verifiability as the radius increases. We also show that our method can be combined with existing methods to further improve the verifiability of networks.

Analytic Continuation by Feature Learning

Analytic continuation aims to reconstruct real-time spectral functions from imaginary-time Green's functions; however, this process is notoriously ill-posed and challenging to solve. We propose a novel neural network architecture, named the Feature Learning Network (FL-net), to enhance the prediction accuracy of spectral functions, achieving an improvement of at least $20\%$ over traditional methods, such as the Maximum Entropy Method (MEM), and previous neural network approaches. Furthermore, we develop an analytical method to evaluate the robustness of the proposed network. Using this method, we demonstrate that increasing the hidden dimensionality of FL-net, while leading to lower loss, results in decreased robustness. Overall, our model provides valuable insights into effectively addressing the complex challenges associated with analytic continuation.

Global Challenge for Safe and Secure LLMs Track 1

This paper introduces the Global Challenge for Safe and Secure Large Language Models (LLMs), a pioneering initiative organized by AI Singapore (AISG) and the CyberSG R&D Programme Office (CRPO) to foster the development of advanced defense mechanisms against automated jailbreaking attacks. With the increasing integration of LLMs in critical sectors such as healthcare, finance, and public administration, ensuring these models are resilient to adversarial attacks is vital for preventing misuse and upholding ethical standards. This competition focused on two distinct tracks designed to evaluate and enhance the robustness of LLM security frameworks. Track 1 tasked participants with developing automated methods to probe LLM vulnerabilities by eliciting undesirable responses, effectively testing the limits of existing safety protocols within LLMs. Participants were challenged to devise techniques that could bypass content safeguards across a diverse array of scenarios, from offensive language to misinformation and illegal activities. Through this process, Track 1 aimed to deepen the understanding of LLM vulnerabilities and provide insights for creating more resilient models.

Efficient Multi-task Prompt Tuning for Recommendation

With the expansion of business scenarios, real recommender systems are facing challenges in dealing with the constantly emerging new tasks in multi-task learning frameworks. In this paper, we attempt to improve the generalization ability of multi-task recommendations when dealing with new tasks. We find that joint training will enhance the performance of the new task but always negatively impact existing tasks in most multi-task learning methods. Besides, such a re-training mechanism with new tasks increases the training costs, limiting the generalization ability of multi-task recommendation models. Based on this consideration, we aim to design a suitable sharing mechanism among different tasks while maintaining joint optimization efficiency in new task learning. A novel two-stage prompt-tuning MTL framework (MPT-Rec) is proposed to address task irrelevance and training efficiency problems in multi-task recommender systems. Specifically, we disentangle the task-specific and task-sharing information in the multi-task pre-training stage, then use task-aware prompts to transfer knowledge from other tasks to the new task effectively. By freezing parameters in the pre-training tasks, MPT-Rec solves the negative impacts that may be brought by the new task and greatly reduces the training costs. Extensive experiments on three real-world datasets show the effectiveness of our proposed multi-task learning framework. MPT-Rec achieves the best performance compared to the SOTA multi-task learning method. Besides, it maintains comparable model performance but vastly improves the training efficiency (i.e., with up to 10% parameters in the full training way) in the new task learning.

Bridging the Gap: Unpacking the Hidden Challenges in Knowledge Distillation for Online Ranking Systems

Knowledge Distillation (KD) is a powerful approach for compressing a large model into a smaller, more efficient model, particularly beneficial for latency-sensitive applications like recommender systems. However, current KD research predominantly focuses on Computer Vision (CV) and NLP tasks, overlooking unique data characteristics and challenges inherent to recommender systems. This paper addresses these overlooked challenges, specifically: (1) mitigating data distribution shifts between teacher and student models, (2) efficiently identifying optimal teacher configurations within time and budgetary constraints, and (3) enabling computationally efficient and rapid sharing of teacher labels to support multiple students. We present a robust KD system developed and rigorously evaluated on multiple large-scale personalized video recommendation systems within Google. Our live experiment results demonstrate significant improvements in student model performance while ensuring consistent and reliable generation of high quality teacher labels from a continuous data stream of data.

Learned Ranking Function: From Short-term Behavior Predictions to Long-term User Satisfaction

We present the Learned Ranking Function (LRF), a system that takes short-term user-item behavior predictions as input and outputs a slate of recommendations that directly optimizes for long-term user satisfaction. Most previous work is based on optimizing the hyperparameters of a heuristic function. We propose to model the problem directly as a slate optimization problem with the objective of maximizing long-term user satisfaction. We also develop a novel constraint optimization algorithm that stabilizes objective trade-offs for multi-objective optimization. We evaluate our approach with live experiments and describe its deployment on YouTube.