OpenRaiser/Intern-Atlas huggingface dataset:
DynSUP: Dynamic Gaussian Splatting from An Unposed Image Pair
Venue: arXiv cs.CV 2024
Year: 2024

Recent advances in 3D Gaussian Splatting have shown promising results. Existing methods typically assume static scenes and/or multiple images with prior poses. Dynamics, sparse views, and unknown poses significantly increase the problem complexity due to insufficient geometric constraints. To overcome this challenge, we propose a method that can use only two images without prior poses to fit Gaussians in dynamic environments. To achieve this, we introduce two technical contributions. First, we propose an object-level two-view bundle adjustment. This strategy decomposes dynamic scenes into piece-wise rigid components, and jointly estimates the camera pose and motions of dynamic objects. Second, we design an SE(3) field-driven Gaussian training method. It enables fine-grained motion modeling through learnable per-Gaussian transformations. Our method leads to high-fidelity novel view synthesis of dynamic scenes while accurately preserving temporal consistency and object motion. Experiments on both synthetic and real-world datasets demonstrate that our method significantly outperforms state-of-the-art approaches designed for the cases of static environments, multiple images, and/or known poses. Our project page is available at https://colin-de.github.io/DynSUP/.
QABISAR: Query-Article Bipartite Interactions for Statutory Article Retrieval
Venue: arXiv cs.IR 2024
Year: 2024

In this paper, we introduce QABISAR, a novel framework for statutory article retrieval, to overcome the semantic mismatch problem when modeling each query-article pair in isolation, making it hard to learn representation that can effectively capture multi-faceted information. QABISAR leverages bipartite interactions between queries and articles to capture diverse aspects inherent in them. Further, we employ knowledge distillation to transfer enriched query representations from the graph network into the query bi-encoder, to capture the rich semantics present in the graph representations, despite absence of graph-based supervision for unseen queries during inference. Our experiments on a real-world expert-annotated dataset demonstrate its effectiveness.
BIGCity: A Universal Spatiotemporal Model for Unified Trajectory and Traffic State Data Analysis
Venue: arXiv cs.AI 2024
Year: 2024

Typical dynamic ST data includes trajectory data (representing individual-level mobility) and traffic state data (representing population-level mobility). Traditional studies often treat trajectory and traffic state data as distinct, independent modalities, each tailored to specific tasks within a single modality. However, real-world applications, such as navigation apps, require joint analysis of trajectory and traffic state data. Treating these data types as two separate domains can lead to suboptimal model performance. Although recent advances in ST data pre-training and ST foundation models aim to develop universal models for ST data analysis, most existing models are "multi-task, solo-data modality" (MTSM), meaning they can handle multiple tasks within either trajectory data or traffic state data, but not both simultaneously. To address this gap, this paper introduces BIGCity, the first multi-task, multi-data modality (MTMD) model for ST data analysis. The model targets two key challenges in designing an MTMD ST model: (1) unifying the representations of different ST data modalities, and (2) unifying heterogeneous ST analysis tasks. To overcome the first challenge, BIGCity introduces a novel ST-unit that represents both trajectories and traffic states in a unified format. Additionally, for the second challenge, BIGCity adopts a tunable large model with ST task-oriented prompt, enabling it to perform a range of heterogeneous tasks without the need for fine-tuning. Extensive experiments on real-world datasets demonstrate that BIGCity achieves state-of-the-art performance across 8 tasks, outperforming 18 baselines. To the best of our knowledge, BIGCity is the first model capable of handling both trajectories and traffic states for diverse heterogeneous tasks. Our code are available at https://github.com/bigscity/BIGCity
WAFFLE: Multimodal Floorplan Understanding in the Wild
Venue: arXiv cs.CV 2024
Year: 2024

Buildings are a central feature of human culture and are increasingly being analyzed with computational methods. However, recent works on computational building understanding have largely focused on natural imagery of buildings, neglecting the fundamental element defining a building's structure -- its floorplan. Conversely, existing works on floorplan understanding are extremely limited in scope, often focusing on floorplans of a single semantic category and region (e.g. floorplans of apartments from a single country). In this work, we introduce WAFFLE, a novel multimodal floorplan understanding dataset of nearly 20K floorplan images and metadata curated from Internet data spanning diverse building types, locations, and data formats. By using a large language model and multimodal foundation models, we curate and extract semantic information from these images and their accompanying noisy metadata. We show that WAFFLE enables progress on new building understanding tasks, both discriminative and generative, which were not feasible using prior datasets. We will publicly release WAFFLE along with our code and trained models, providing the research community with a new foundation for learning the semantics of buildings.
Linear Probe Penalties Reduce LLM Sycophancy
Venue: arXiv cs.AI 2024
Year: 2024

Large language models (LLMs) are often sycophantic, prioritizing agreement with their users over accurate or objective statements. This problematic behavior becomes more pronounced during reinforcement learning from human feedback (RLHF), an LLM fine-tuning stage intended to align model outputs with human values. Instead of increasing accuracy and reliability, the reward model learned from RLHF often rewards sycophancy. We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. Our experiments show that constructing and optimizing against this surrogate reward function reduces sycophantic behavior in multiple open-source LLMs. Our results suggest a generalizable methodology for reducing unwanted LLM behaviors that are not sufficiently disincentivized by RLHF fine-tuning.
Patent-publication pairs for the detection of knowledge transfer from research to industry: reducing ambiguities with word embeddings and references
Venue: arXiv cs.IR 2024
Year: 2024

The performance of medical research can be viewed and evaluated not only from the perspective of publication output, but also from the perspective of economic exploitability. Patents can represent the exploitation of research results and thus the transfer of knowledge from research to industry. In this study, we set out to identify publication-patent pairs in order to use patents as a proxy for the economic impact of research. To identify these pairs, we matched scholarly publications and patents by comparing the names of authors and investors. To resolve the ambiguities that arise in this name-matching process, we expanded our approach with two additional filter features, one used to assess the similarity of text content, the other to identify common references in the two document types. To evaluate text similarity, we extracted and transformed technical terms from a medical ontology (MeSH) into numerical vectors using word embeddings. We then calculated the results of the two supporting features over an example five-year period. Furthermore, we developed a statistical procedure which can be used to determine valid patent classes for the domain of medicine. Our complete data processing pipeline is freely available, from the raw data of the two document types right through to the validated publication-patent pairs.
Identifying Root Causes of Null Pointer Exceptions with Logical Inferences
Venue: arXiv cs.SE 2024
Year: 2024

Recently, Large Language Model (LLM)-based Fault Localization (FL) techniques have been proposed, and showed improved performance with explanations on FL results. However, a major issue with LLM-based FL techniques is their heavy reliance on LLMs, which are often unreliable, expensive, and difficult to analyze or improve. When results are unsatisfactory, it is challenging both to determine a cause and to refine a technique for better outcomes. To address this issue, we propose LogicFL, a novel logical fault localization technique for Null Pointer Exceptions (NPEs). With logic programming, LogicFL imitates human developers' deduction process of fault localization, and identifies causes of NPEs after logical inferences on collected facts about faulty code and test execution. In an empirical evaluation of 76 NPE bugs from Apache Commons projects and the Defects4J benchmark, LogicFL accurately identified the fault locations and pinpointed the exact code fragments causing the NPEs for 67 bugs (88.16%), which were 19.64% and 4.69% more bugs than two compared LLM-based FL techniques respectively. In addition, LogicFL can be executed on a low-performance machine similar to a typical laptop, with an average runtime of 21.63 seconds and a worst-case time of under two minutes, including test execution and output file generation. Moreover, when compared to the two LLM-based FL techniques using the GPT-4o model, LogicFL was significantly more cost-efficient, as those techniques required 343.94 and 3,736.19 times the cost of LogicFL, respectively. Last but not least, the deduction process in LogicFL for providing FL results is fully traceable, enabling us to understand the reasoning behind the technique's outcomes and to further enhance the technique.
e-Fold Cross-Validation for Recommender-System Evaluation
Venue: arXiv cs.LG 2024
Year: 2024

To combat the rising energy consumption of recommender systems we implement a novel alternative for k-fold cross validation. This alternative, named e-fold cross validation, aims to minimize the number of folds to achieve a reduction in power usage while keeping the reliability and robustness of the test results high. We tested our method on 5 recommender system algorithms across 6 datasets and compared it with 10-fold cross validation. On average e-fold cross validation only needed 41.5% of the energy that 10-fold cross validation would need, while it's results only differed by 1.81%. We conclude that e-fold cross validation is a promising approach that has the potential to be an energy efficient but still reliable alternative to k-fold cross validation.
Detecting Memorization in Large Language Models
Venue: arXiv cs.LG 2024
Year: 2024

Large language models (LLMs) have achieved impressive results in natural language processing but are prone to memorizing portions of their training data, which can compromise evaluation metrics, raise privacy concerns, and limit generalization. Traditional methods for detecting memorization rely on output probabilities or loss functions, often lacking precision due to confounding factors like common language patterns. In this paper, we introduce an analytical method that precisely detects memorization by examining neuron activations within the LLM. By identifying specific activation patterns that differentiate between memorized and not memorized tokens, we train classification probes that achieve near-perfect accuracy. The approach can also be applied to other mechanisms, such as repetition, as demonstrated in this study, highlighting its versatility. Intervening on these activations allows us to suppress memorization without degrading overall performance, enhancing evaluation integrity by ensuring metrics reflect genuine generalization. Additionally, our method supports large-scale labeling of tokens and sequences, crucial for next-generation AI models, improving training efficiency and results. Our findings contribute to model interpretability and offer practical tools for analyzing and controlling internal mechanisms in LLMs.
AI Benchmarks and Datasets for LLM Evaluation
Venue: arXiv cs.DC 2024
Year: 2024

LLMs demand significant computational resources for both pre-training and fine-tuning, requiring distributed computing capabilities due to their large model sizes \cite{sastry2024computing}. Their complex architecture poses challenges throughout the entire AI lifecycle, from data collection to deployment and monitoring \cite{OECD_AIlifecycle}. Addressing critical AI system challenges, such as explainability, corrigibility, interpretability, and hallucination, necessitates a systematic methodology and rigorous benchmarking \cite{guldimann2024complai}. To effectively improve AI systems, we must precisely identify systemic vulnerabilities through quantitative evaluation, bolstering system trustworthiness. The enactment of the EU AI Act \cite{EUAIAct} by the European Parliament on March 13, 2024, establishing the first comprehensive EU-wide requirements for the development, deployment, and use of AI systems, further underscores the importance of tools and methodologies such as Z-Inspection. It highlights the need to enrich this methodology with practical benchmarks to effectively address the technical challenges posed by AI systems. To this end, we have launched a project that is part of the AI Safety Bulgaria initiatives \cite{AI_Safety_Bulgaria}, aimed at collecting and categorizing AI benchmarks. This will enable practitioners to identify and utilize these benchmarks throughout the AI system lifecycle.
Unleashing In-context Learning of Autoregressive Models for Few-shot Image Manipulation
Venue: arXiv cs.CV 2024
Year: 2024

Text-guided image manipulation has experienced notable advancement in recent years. In order to mitigate linguistic ambiguity, few-shot learning with visual examples has been applied for instructions that are underrepresented in the training set, or difficult to describe purely in language. However, learning from visual prompts requires strong reasoning capability, which diffusion models are struggling with. To address this issue, we introduce a novel multi-modal autoregressive model, dubbed $\textbf{InstaManip}$, that can $\textbf{insta}$ntly learn a new image $\textbf{manip}$ulation operation from textual and visual guidance via in-context learning, and apply it to new query images. Specifically, we propose an innovative group self-attention mechanism to break down the in-context learning process into two separate stages -- learning and applying, which simplifies the complex problem into two easier tasks. We also introduce a relation regularization method to further disentangle image transformation features from irrelevant contents in exemplar images. Extensive experiments suggest that our method surpasses previous few-shot image manipulation models by a notable margin ($\geq$19% in human evaluation). We also find our model can be further boosted by increasing the number or diversity of exemplar images.
TruncFormer: Private LLM Inference Using Only Truncations
Venue: arXiv cs.CR 2024
Year: 2024

Private inference (PI) serves an important role in guaranteeing the privacy of user data when interfacing with proprietary machine learning models such as LLMs. However, PI remains practically intractable due to the massive latency costs associated with nonlinear functions present in LLMs. Existing works have focused on improving latency of specific LLM nonlinearities (such as the Softmax, or the GeLU) via approximations. However, new types of nonlinearities are regularly introduced with new LLM architectures, and this has led to a constant game of catch-up where PI researchers attempt to optimize the newest nonlinear function. We introduce TruncFormer, a framework for taking any LLM and transforming it into a plaintext emulation of PI. Our framework leverages the fact that nonlinearities in LLMs are differentiable and can be accurately approximated with a sequence of additions, multiplications, and truncations. Further, we decouple the add/multiply and truncation operations, and statically determine where truncations should be inserted based on a given field size and input representation size. This leads to latency improvements over existing cryptographic protocols that enforce truncation after every multiplication operation. We open source our code for community use.
CRISP: Object Pose and Shape Estimation with Test-Time Adaptation
Venue: arXiv cs.CV 2024
Year: 2024

We consider the problem of estimating object pose and shape from an RGB-D image. Our first contribution is to introduce CRISP, a category-agnostic object pose and shape estimation pipeline. The pipeline implements an encoder-decoder model for shape estimation. It uses FiLM-conditioning for implicit shape reconstruction and a DPT-based network for estimating pose-normalized points for pose estimation. As a second contribution, we propose an optimization-based pose and shape corrector that can correct estimation errors caused by a domain gap. Observing that the shape decoder is well behaved in the convex hull of known shapes, we approximate the shape decoder with an active shape model, and show that this reduces the shape correction problem to a constrained linear least squares problem, which can be solved efficiently by an interior point algorithm. Third, we introduce a self-training pipeline to perform self-supervised domain adaptation of CRISP. The self-training is based on a correct-and-certify approach, which leverages the corrector to generate pseudo-labels at test time, and uses them to self-train CRISP. We demonstrate CRISP (and the self-training) on YCBV, SPE3R, and NOCS datasets. CRISP shows high performance on all the datasets. Moreover, our self-training is capable of bridging a large domain gap. Finally, CRISP also shows an ability to generalize to unseen objects. Code and pre-trained models will be available on https://web.mit.edu/sparklab/research/crisp_object_pose_shape/.
Blindfold: Confidential Memory Management by Untrusted Operating System
Venue: arXiv cs.CR 2024
Year: 2024

Confidential Computing (CC) has received increasing attention in recent years as a mechanism to protect user data from untrusted operating systems (OSes). Existing CC solutions hide confidential memory from the OS and/or encrypt it to achieve confidentiality. In doing so, they render OS memory optimization unusable or complicate the trusted computing base (TCB) required for optimization. This paper presents our results toward overcoming these limitations, synthesized in a CC design named Blindfold. Like many other CC solutions, Blindfold relies on a small trusted software component running at a higher privilege level than the kernel, called Guardian. It features three techniques that can enhance existing CC solutions. First, instead of nesting page tables, Guardian mediates how the OS accesses memory and handles exceptions by switching page and interrupt tables. Second, Blindfold employs a lightweight capability system to regulate the kernel semantic access to user memory, unifying case-by-case approaches in previous work. Finally, Blindfold provides carefully designed secure ABI for confidential memory management without encryption. We report an implementation of Blindfold that works on ARMv8-A/Linux. Using Blindfold prototype, we are able to evaluate the cost of enabling confidential memory management by the untrusted Linux kernel. We show Blindfold has a smaller runtime TCB than related systems and enjoys competitive performance. More importantly, we show that the Linux kernel, including all of its memory optimizations except memory compression, can function properly for confidential memory. This requires only about 400 lines of kernel modifications.
Lookahead Counterfactual Fairness
Venue: arXiv cs.LG 2024
Year: 2024

As machine learning (ML) algorithms are used in applications that involve humans, concerns have arisen that these algorithms may be biased against certain social groups. \textit{Counterfactual fairness} (CF) is a fairness notion proposed in Kusner et al. (2017) that measures the unfairness of ML predictions; it requires that the prediction perceived by an individual in the real world has the same marginal distribution as it would be in a counterfactual world, in which the individual belongs to a different group. Although CF ensures fair ML predictions, it fails to consider the downstream effects of ML predictions on individuals. Since humans are strategic and often adapt their behaviors in response to the ML system, predictions that satisfy CF may not lead to a fair future outcome for the individuals. In this paper, we introduce \textit{lookahead counterfactual fairness} (LCF), a fairness notion accounting for the downstream effects of ML models which requires the individual \textit{future status} to be counterfactually fair. We theoretically identify conditions under which LCF can be satisfied and propose an algorithm based on the theorems. We also extend the concept to path-dependent fairness. Experiments on both synthetic and real data validate the proposed method.
Advancing Speech Language Models by Scaling Supervised Fine-Tuning with Over 60,000 Hours of Synthetic Speech Dialogue Data
Venue: arXiv cs.CL 2024
Year: 2024

The GPT-4o represents a significant milestone in enabling real-time interaction with large language models (LLMs) through speech, its remarkable low latency and high fluency not only capture attention but also stimulate research interest in the field. This real-time speech interaction is particularly valuable in scenarios requiring rapid feedback and immediate responses, dramatically enhancing user experience. However, there is a notable lack of research focused on real-time large speech language models, particularly for Chinese. In this work, we present KE-Omni, a seamless large speech language model built upon Ke-SpeechChat, a large-scale high-quality synthetic speech interaction dataset consisting of 7 million Chinese and English conversations, featuring 42,002 speakers, and totaling over 60,000 hours, This contributes significantly to the advancement of research and development in this field. The demos can be accessed at \url{https://huggingface.co/spaces/KE-Team/KE-Omni}.