UnKE: Unstructured Knowledge Editing in Large Language Models

Recent knowledge editing methods have primarily focused on modifying structured knowledge in large language models, heavily relying on the assumption that structured knowledge is stored as key-value pairs locally in MLP layers or specific neurons. However, this task setting overlooks the fact that a significant portion of real-world knowledge is stored in an unstructured format, characterized by long-form content, noise, and a complex yet comprehensive nature. The "knowledge locating" and "term-driven optimization" techniques conducted from the assumption used in previous methods (e.g., MEMIT) are ill-suited for unstructured knowledge. To address these challenges, we propose a novel unstructured knowledge editing method, namely UnKE, which extends previous assumptions in the layer dimension and token dimension. Firstly, in the layer dimension, we discard the "knowledge locating" step and treat first few layers as the key, which expand knowledge storage through layers to break the "knowledge stored locally" assumption. Next, we replace "term-driven optimization" with "cause-driven optimization" across all inputted tokens in the token dimension, directly optimizing the last layer of the key generator to perform editing to generate the required key vectors. By utilizing key-value pairs at the layer level, UnKE effectively represents and edits complex and comprehensive unstructured knowledge, leveraging the potential of both the MLP and attention layers. Results on newly proposed unstructure knowledge editing dataset (UnKEBench) and traditional structured datasets demonstrate that UnKE achieves remarkable performance, surpassing strong baselines.

Efficient Vision-Language Pre-training by Cluster Masking

We propose a simple strategy for masking image patches during visual-language contrastive learning that improves the quality of the learned representations and the training speed. During each iteration of training, we randomly mask clusters of visually similar image patches, as measured by their raw pixel intensities. This provides an extra learning signal, beyond the contrastive training itself, since it forces a model to predict words for masked visual structures solely from context. It also speeds up training by reducing the amount of data used in each image. We evaluate the effectiveness of our model by pre-training on a number of benchmarks, finding that it outperforms other masking strategies, such as FLIP, on the quality of the learned representation.

MLaKE: Multilingual Knowledge Editing Benchmark for Large Language Models

The extensive utilization of large language models (LLMs) underscores the crucial necessity for precise and contemporary knowledge embedded within their intrinsic parameters. Existing research on knowledge editing primarily concentrates on monolingual scenarios, neglecting the complexities presented by multilingual contexts and multi-hop reasoning. To address these challenges, our study introduces MLaKE (Multilingual Language Knowledge Editing), a novel benchmark comprising 4072 multi-hop and 5360 single-hop questions designed to evaluate the adaptability of knowledge editing methods across five languages: English, Chinese, Japanese, French, and German. MLaKE aggregates fact chains from Wikipedia across languages and utilizes LLMs to generate questions in both free-form and multiple-choice. We evaluate the multilingual knowledge editing generalization capabilities of existing methods on MLaKE. Existing knowledge editing methods demonstrate higher success rates in English samples compared to other languages. However, their generalization capabilities are limited in multi-language experiments. Notably, existing knowledge editing methods often show relatively high generalization for languages within the same language family compared to languages from different language families. These results underscore the imperative need for advancements in multilingual knowledge editing and we hope MLaKE can serve as a valuable resource for benchmarking and solution development.

MicroDiffusion: Implicit Representation-Guided Diffusion for 3D Reconstruction from Limited 2D Microscopy Projections

Volumetric optical microscopy using non-diffracting beams enables rapid imaging of 3D volumes by projecting them axially to 2D images but lacks crucial depth information. Addressing this, we introduce MicroDiffusion, a pioneering tool facilitating high-quality, depth-resolved 3D volume reconstruction from limited 2D projections. While existing Implicit Neural Representation (INR) models often yield incomplete outputs and Denoising Diffusion Probabilistic Models (DDPM) excel at capturing details, our method integrates INR's structural coherence with DDPM's fine-detail enhancement capabilities. We pretrain an INR model to transform 2D axially-projected images into a preliminary 3D volume. This pretrained INR acts as a global prior guiding DDPM's generative process through a linear interpolation between INR outputs and noise inputs. This strategy enriches the diffusion process with structured 3D information, enhancing detail and reducing noise in localized 2D images. By conditioning the diffusion model on the closest 2D projection, MicroDiffusion substantially enhances fidelity in resulting 3D reconstructions, surpassing INR and standard DDPM outputs with unparalleled image quality and structural fidelity. Our code and dataset are available at https://github.com/UCSC-VLAA/MicroDiffusion.

Stable Knowledge Editing in Large Language Models

Efficient knowledge editing of large language models is crucial for replacing obsolete information or incorporating specialized knowledge on a large scale. However, previous methods implicitly assume that knowledge is localized and isolated within the model, an assumption that oversimplifies the interconnected nature of model knowledge. The premise of localization results in an incomplete knowledge editing, whereas an isolated assumption may impair both other knowledge and general abilities. It introduces instability to the performance of the knowledge editing method. To transcend these assumptions, we introduce StableKE, a method adopts a novel perspective based on knowledge augmentation rather than knowledge localization. To overcome the expense of human labeling, StableKE integrates two automated knowledge augmentation strategies: Semantic Paraphrase Enhancement strategy, which diversifies knowledge descriptions to facilitate the teaching of new information to the model, and Contextual Description Enrichment strategy, expanding the surrounding knowledge to prevent the forgetting of related information. StableKE surpasses other knowledge editing methods, demonstrating stability both edited knowledge and multi-hop knowledge, while also preserving unrelated knowledge and general abilities. Moreover, StableKE can edit knowledge on ChatGPT.

Retrieve Only When It Needs: Adaptive Retrieval Augmentation for Hallucination Mitigation in Large Language Models

Hallucinations pose a significant challenge for the practical implementation of large language models (LLMs). The utilization of parametric knowledge in generating factual content is constrained by the limited knowledge of LLMs, potentially resulting in internal hallucinations. While incorporating external information can help fill knowledge gaps, it also introduces the risk of irrelevant information, thereby increasing the likelihood of external hallucinations. A careful and balanced integration of the parametric knowledge within LLMs with external information is crucial to alleviate hallucinations. In this study, we present Rowen, a novel approach that enhances LLMs with a selective retrieval augmentation process tailored to address hallucinated outputs. This process is governed by a multilingual semantic-aware detection module, which evaluates the consistency of the perturbed responses across various languages for the same queries. Upon detecting inconsistencies indicative of hallucinations, Rowen activates the retrieval of external information to rectify the model outputs. Rowen adeptly harmonizes the intrinsic parameters in LLMs with external knowledge sources, effectively mitigating hallucinations by ensuring a balanced integration of internal reasoning and external evidence. Through a comprehensive empirical analysis, we demonstrate that Rowen surpasses the current state-of-the-art in both detecting and mitigating hallucinated content within the outputs of LLMs.

SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image Segmentation

Recent advancements in large-scale Vision Transformers have made significant strides in improving pre-trained models for medical image segmentation. However, these methods face a notable challenge in acquiring a substantial amount of pre-training data, particularly within the medical field. To address this limitation, we present Masked Multi-view with Swin Transformers (SwinMM), a novel multi-view pipeline for enabling accurate and data-efficient self-supervised medical image analysis. Our strategy harnesses the potential of multi-view information by incorporating two principal components. In the pre-training phase, we deploy a masked multi-view encoder devised to concurrently train masked multi-view observations through a range of diverse proxy tasks. These tasks span image reconstruction, rotation, contrastive learning, and a novel task that employs a mutual learning paradigm. This new task capitalizes on the consistency between predictions from various perspectives, enabling the extraction of hidden multi-view information from 3D medical data. In the fine-tuning stage, a cross-view decoder is developed to aggregate the multi-view information through a cross-attention block. Compared with the previous state-of-the-art self-supervised learning method Swin UNETR, SwinMM demonstrates a notable advantage on several medical image segmentation tasks. It allows for a smooth integration of multi-view information, significantly boosting both the accuracy and data-efficiency of the model. Code and models are available at https://github.com/UCSC-VLAA/SwinMM/.

MacLaSa: Multi-Aspect Controllable Text Generation via Efficient Sampling from Compact Latent Space

Multi-aspect controllable text generation aims to generate fluent sentences that possess multiple desired attributes simultaneously. Traditional methods either combine many operators in the decoding stage, often with costly iteration or search in the discrete text space, or train separate controllers for each aspect, resulting in a degeneration of text quality due to the discrepancy between different aspects. To address these limitations, we introduce a novel approach for multi-aspect control, namely MacLaSa, that estimates compact latent space for multiple aspects and performs efficient sampling with a robust sampler based on ordinary differential equations (ODEs). To eliminate the domain gaps between different aspects, we utilize a Variational Autoencoder (VAE) network to map text sequences from varying data sources into close latent representations. The estimated latent space enables the formulation of joint energy-based models (EBMs) and the plugging in of arbitrary attribute discriminators to achieve multi-aspect control. Afterwards, we draw latent vector samples with an ODE-based sampler and feed sampled examples to the VAE decoder to produce target text sequences. Experimental results demonstrate that MacLaSa outperforms several strong baselines on attribute relevance and textual quality while maintaining a high inference speed.

TRBoost: A Generic Gradient Boosting Machine based on Trust-region Method

Gradient Boosting Machines (GBMs) are derived from Taylor expansion in functional space and have achieved state-of-the-art results on a variety of problems. However, there is a dilemma for GBMs to maintain a balance between performance and generality. Specifically, gradient descent-based GBMs employ the first-order Taylor expansion to make them appropriate for all loss functions. And Newton's method-based GBMs use the positive hessian information to achieve better performance at the expense of generality. In this paper, a generic Gradient Boosting Machine called Trust-region Boosting (TRBoost) is presented to maintain this balance. In each iteration, we apply a constrained quadratic model to approximate the objective and solve it by the Trust-region algorithm to obtain a new learner. TRBoost offers the benefit that we do not need the hessian to be positive definite, which generalizes GBMs to suit arbitrary loss functions while keeping up the good performance as the second-order algorithm. Several numerical experiments are conducted to confirm that TRBoost is not only as general as the first-order GBMs but also able to get competitive results with the second-order GBMs.

Discovering Intrinsic Reward with Contrastive Random Walk

The aim of this paper is to demonstrate the efficacy of using Contrastive Random Walk as a curiosity method to achieve faster convergence to the optimal policy.Contrastive Random Walk defines the transition matrix of a random walk with the help of neural networks. It learns a meaningful state representation with a closed loop. The loss of Contrastive Random Walk serves as an intrinsic reward and is added to the environment reward. Our method works well in non-tabular sparse reward scenarios, in the sense that our method receives the highest reward within the same iterations compared to other methods. Meanwhile, Contrastive Random Walk is more robust. The performance doesn't change much with different random initialization of environments. We also find that adaptive restart and appropriate temperature are crucial to the performance of Contrastive Random Walk.