bluuebunny/crossref_metadata_2025 huggingface dataset:
MFTraj: Map-Free, Behavior-Driven Trajectory Prediction for Autonomous Driving
Authors: Liao Haicheng , Li Zhenning , Wang Chengyue , Shen Huanming , Liao Dongping , Wang Bonan , Li Guofa , Xu Chengzhong
Published: July 2024

This paper introduces a trajectory prediction model tailored for autonomous driving, focusing on capturing complex interactions in dynamic traffic scenarios without reliance on high-definition maps. The model, termed MFTraj, harnesses historical trajectory data combined with a novel dynamic geometric graph-based behavior-aware module. At its core, an adaptive structure-aware interactive graph convolutional network captures both positional and behavioral features of road users, preserving spatial-temporal intricacies. Enhanced by a linear attention mechanism, the model achieves computational efficiency and reduced parameter overhead. Evaluations on the Argoverse, NGSIM, HighD, and MoCAD datasets underscore MFTraj's robustness and adaptability, outperforming numerous benchmarks even in data-challenged scenarios without the need for additional information such as HD maps or vectorized maps. Importantly, it maintains competitive performance even in scenarios with substantial missing data (12.5%-50%), outperforming most existing state-of-the-art models. The results and methodology suggest a significant advancement in autonomous driving trajectory prediction, paving the way for safer and efficient autonomous systems.
Enhancing Multimodal Knowledge Graph Representation Learning through Triple Contrastive Learning
Authors: Lu Yuxing , Zhao Weichen , Sun Nan , Wang Jinzhuo
Published: July 2024

Multimodal knowledge graphs incorporate multimodal information rather than pure symbols, which significantly enhance the representation of knowledge graphs and their capacity to understand the world. Despite these advancements, existing multimodal fusion techniques still face significant challenges in representing modalities and fully integrating the diverse attributes of entities, particularly when dealing with more than one modality. To address this issue, this article proposes a Knowledge Graph Multimodal Representation Learning (KG-MRI) method. This method utilizes foundation models to represent different modalities and incorporates a triple contrastive learning model and a dual-phase training strategy to effectively fuse the different modalities with knowledge graph embeddings. We conducted comprehensive comparisons with several different knowledge graph embedding methods to validate the effectiveness of our KG-MRI model. Furthermore validation on a real-world Non-Alcohol Fatty Liver Disease (NAFLD) cohort demonstrated that the vector representations learned through our methodology possess enhanced representational capabilities, showing promise for broader applications in complex multimodal environments.
Enhanced DouDiZhu Card Game Strategy Using Oracle Guiding and Adaptive Deep Monte Carlo Method
Authors: Luo Qian , Tan Tien Ping , Zha Daochen , Zhang Tianqiao
Published: July 2024

Deep Reinforcement Learning (DRL) exhibits significant advancements in games with both perfect and imperfect information, such as Go, Chess, Texas Hold'em, and Dota2. However, DRL encounters considerable challenges when tackling card game DouDiZhu because of the imperfect information, large state-action space, and the sparse reward issue. This paper presents OADMCDou, which combines Oracle Guiding and Adaptive Deep Monte Carlo Method to address the challenges in DouDiZhu. Oracle Guiding trains an Oracle agent with both imperfect and perfect information, gradually reducing reliance on imperfect information to transition to a standard agent. Adaptive Deep Monte Carlo uses gradient weight clipping and constrains the magnitude of updates to prevent extreme policy updates. We conduct extensive experiments to evaluate the effectiveness of the proposed methods, demonstrating OADMCDou's superior performance over the state-of-the-art DouDiZhu AI, DouZero. This superiority over DouZero is reflected in two metrics: a 95% confidence interval of 0.104 ± 0.041 for performance, and a 28.6% reduction in loss.
IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making
Authors: Niu Hui , Li Siyuan , Zheng Jiahao , Lin Zhouchi , An Bo , Li Jian , Guo Jian
Published: July 2024

Market making (MM) via Reinforcement Learning (RL) has attracted significant attention in financial trading. Most existing RL-based MM methods focus on optimizing single-price level strategies which fail at frequent order cancellations and loss of queue priority. By comparison, strategies involving multiple price levels align better with actual trading scenarios. However, given the complexity that multi-price level RL strategies involve a comprehensive trading action space, the challenge of effectively training RL persists. Inspired by the effective workflow of professional human market makers, we propose Imitative Market Maker (IMM), a novel RL framework leveraging knowledge from both suboptimal signal-based experts and direct policy interactions. Our framework starts with introducing effective state and action formulations that well encode information about multiprice level orders. Furthermore, IMM integrates a representation learning unit capable of capturing both short- and long-term market trends to mitigate adverse selection risk. Subsequently, IMM designs an expert strategy based on predictive signals, and trains the agent through the integration of RL and imitation learning techniques to achieve efficient learning. Extensive experimental results on four real-world market datasets demonstrate the superiority of IMM against current RL-based MM strategies.
MacMic: Executing Iceberg Orders via Hierarchical Reinforcement Learning
Authors: Niu Hui , Li Siyuan , Li Jian
Published: July 2024

In recent years, there has been a growing interest in applying reinforcement learning (RL) techniques to order execution owing to RL’s strong sequential decision-making ability. However, realistic order execution tasks usually involve a large fine-grained action space and a long trading duration. The former hinders the RL agents from efficient exploration. The latter increases the task complexity, since the agent must capture price advantages throughout the day as well as micro changes within a few seconds on the limited order books. In addressing these challenges, we propose MacMic, a novel Hierarchical RL-based order execution approach that captures market patterns and executes orders from different temporal scales. MacMic employs a high-level agent to split the parent order into smaller slices at coarse-grained time steps. Then a low-level agent is adopted to execute these slices by placing fixed-size sub-orders at a continuous time. Besides, to balance the multifaceted objectives of the two tasks, MacMic pretrains a causal stacking hidden Markov model (SHMM) to obtain both effective macro-level and micro-level market states. Comprehensive experimental results on 200 stocks across the US and China A-share markets validate the effectiveness of the proposed method.
SACNN: Self Attention-based Convolutional Neural Network for Fraudulent Behaviour Detection in Sports
Authors: Rahman Maxx Richard , Abdel Khaliq Lotfy , Piper Thomas , Geyer Hans , Equey Tristan , Baume Norbert , Aikin Reid , Maass Wolfgang
Published: July 2024

Doping practices in sports by unscrupulous athletes have been an important societal issue for several decades. Recently, sample swapping has been raised as a potential practice performed by athletes to swap their doped samples with clean samples to evade the positive doping test. So far, the only proven method to detect such cases is by performing DNA analysis on samples. However, it is expensive and time-consuming, which goes beyond the budgetary limits of anti-doping organisations when implementing to all the samples collected during sports events. Therefore, in this paper, we propose a self attention-based convolutional neural network (SACNN) that incorporates both spatial and temporal behaviour of the longitudinal profile and generates embedding maps for solving the fraud detection problem in sports. We conduct extensive experiments on the real-world datasets. The result shows that SACNN outperforms other state-of-the-art baseline models for sequential anomaly detection. Moreover, we conduct a study with domain experts on real-world profiles using both DNA analysis and our proposed method; the result demonstrates the effectiveness of our proposed method and the impact it could bring to the society.
Multi-TA: Multilevel Temporal Augmentation for Robust Septic Shock Early Prediction
Authors: Sohn Hyunwoo , Park Kyungjin , Park Baekkwan , Chi Min
Published: July 2024

Early predicting the onset of a disease is critical to timely and accurate clinical decision-making, where a model determines whether a patient will develop the disease n hours later. While deep learning algorithms have demonstrated great success using multivariate irregular time-series data such as electronic health records (EHRs), they often lack temporal robustness due to data scarcity problems becoming more prominent at multilevel as n increases. At event-level, the decreasing number of available events per trajectory increases uncertainty in anticipating future disease behavior. At trajectory-level, the scarcity of patient trajectories limits diversity in the training population, hindering the model's generalization. This work introduces Multi-TA, a multilevel temporal augmentation framework that leverages BERT-based temporal EHRs representation learning and a unified data augmentation approach, effectively addressing data scarcity issues at both event and trajectory levels. Validated on two real-world EHRs for septic shock, Multi-TA outperforms mixup and GAN-based state-of-the-art models across eight prediction windows, demonstrating improved temporal robustness. Further, we provide in-depth analyses on data augmentation.
BeyondVision: An EMG-driven Micro Hand Gesture Recognition Based on Dynamic Segmentation
Authors: Wang Nana , Niu Jianwei , Liu Xuefeng , Yu Dongqin , Zhu Guogang , Wu Xinghao , Xu Mingliang , Su Hao
Published: July 2024

Hand gesture recognition (HGR) plays a pivotal role in natural and intuitive human-computer interactions. Recent HGR methods focus on recognizing gestures from vision-based images or videos. However, vision-based methods are limited in recognizing micro hand gestures (MHGs) (e.g., pinch within 1cm) and gestures with occluded fingers. To address these issues, combined with the electromyography (EMG) technique, we propose BeyondVision, an EMG-driven MHG recognition system based on deep learning. BeyondVision consists of a wristband-style EMG sampling device and a tailored lightweight neural network BV-Net that can accurately translate EMG signals of MHGs to control commands in real-time. Moreover, we propose a post-processing mechanism and a weight segmentation algorithm to effectively improve the accuracy rate of MHG recognition. Subjective and objective experimental results show that our approach achieves over 95% average recognition rate, 2000Hz sampling frequency, and real-time micro gesture recognition. Our technique has been applied in a commercially available product, introduced at: https://github.com/tyc333/NoBarriers.
MediTab: Scaling Medical Tabular Data Predictors via Data Consolidation, Enrichment, and Refinement
Authors: Wang Zifeng , Gao Chufan , Xiao Cao , Sun Jimeng
Published: July 2024

Tabular data prediction has been employed in medical applications such as patient health risk prediction. However, existing methods usually revolve around the algorithm design while overlooking the significance of data engineering. Medical tabular datasets frequently exhibit significant heterogeneity across different sources, with limited sample sizes per source. As such, previous predictors are often trained on manually curated small datasets that struggle to generalize across different tabular datasets during inference. This paper proposes to scale medical tabular data predictors (MediTab) to various tabular inputs with varying features. The method uses a data engine that leverages large language models (LLMs) to consolidate tabular samples to overcome the barrier across tables with distinct schema. It also aligns out-domain data with the target task using a "learn, annotate, and refinement'' pipeline. The expanded training data then enables the pre-trained MediTab to infer for arbitrary tabular input in the domain without fine-tuning, resulting in significant improvements over supervised baselines: it reaches an average ranking of 1.57 and 1.00 on 7 patient outcome prediction datasets and 3 trial outcome prediction datasets, respectively. In addition, MediTab exhibits impressive zero-shot performances: it outperforms supervised XGBoost models by 8.9% and 17.2% on average in two prediction tasks, respectively.
RSAP-DFM: Regime-Shifting Adaptive Posterior Dynamic Factor Model for Stock Returns Prediction
Authors: Xiang Quanzhou , Chen Zhan , Sun Qi , Jiang Rujun
Published: July 2024

As the latest development of asset pricing research, how to use machine learning to improve the performance of factor models has become a topic of concern in recent years. The variability of the instantaneous macro environment brings great difficulties to quantitative investment, so the extended factor model must learn how to self-adapt to extract the macro pattern from the massive stock volume and price information, and how to continuously map the extracted macro pattern to the stock investment is also an open question. To this end, we propose the first continuous regime-based dynamic factor model, RSAP-DFM, which adaptively extracts continuous macroeconomic information and completes the dynamic explicit mapping of stock returns for the first time through dual regime shifting, while the adversarial posterior factors effectively correct the mapping deviation of prior factors. In addition, our model integrates an innovative two-stage optimization algorithm and normally distributed sampling, which further enhances the robustness of the model. Performance on three real stock datasets validates the validity of our model, which exceeds any previous methods available.
Trade When Opportunity Comes: Price Movement Forecasting via Locality-Aware Attention and Iterative Refinement Labeling
Authors: Zeng Liang , Wang Lei , Niu Hui , Zhang Ruchen , Wang Ling , Li Jian
Published: July 2024

Price movement forecasting, aimed at predicting financial asset trends based on current market information, has achieved promising advancements through machine learning (ML) methods. Most existing ML methods, however, struggle with the extremely low signal-to-noise ratio and stochastic nature of financial data, often mistaking noises for real trading signals without careful selection of potentially profitable samples. To address this issue, we propose LARA, a novel price movement forecasting framework with two main components: Locality-Aware Attention (LA-Attention) and Iterative Refinement Labeling (RA-Labeling). (1) LA-Attention, enhanced by metric learning techniques, automatically extracts the potentially profitable samples through masked attention scheme and task-specific distance metrics. (2) RA-Labeling further iteratively refines the noisy labels of potentially profitable samples, and combines the learned predictors robust to the unseen and noisy samples. In a set of experiments on three real-world financial markets: stocks, cryptocurrencies, and ETFs, LARA significantly outperforms several machine learning based methods on the Qlib quantitative investment platform. Extensive ablation studies confirm LARA's superior ability in capturing more reliable trading opportunities.
Distribution-Independent Cell Type Identification for Single-Cell RNA-seq Data
Authors: Zhai Yuyao , Chen Liang , Deng Minghua
Published: July 2024

Automatic cell type annotation aims to transfer the label knowledge from label-abundant reference data to label-scarce target data, which makes encouraging progress in single-cell RNA-seq data analysis. While previous works have focused on classifying close-set cells and detecting open-set cells during testing, it is still essential to be able to classify unknown cell types as human beings. Additionally, few efforts have been devoted to addressing the challenge of common long-tail dilemma in cell type annotation data. Therefore, in this paper, we propose an innovative distribution-independent universal cell type identification framework called scDET from the perspective of autonomously equilibrated dual-consultative contrastive learning. Our model can generate fine-grained predictions for both close-set and open-set cell types in a long-tailed open-world environment. scDET consists of a contrastive-learning branch and a pseudo-labeling branch, which work collaboratively to provide interactive supervision. Specifically, the contrastive-learning branch provides reliable distribution estimation to regularize the predictions of the pseudo-labeling branch, which in turn guides itself through self-balanced knowledge transfer and a designed novel soft contrastive loss. Extensive experimental results on various evaluation datasets demonstrate the superior performance of scDET over other state-of-the-art single-cell clustering and annotation methods.
Heterogeneous Causal Metapath Graph Neural Network for Gene-Microbe-Disease Association Prediction
Authors: Zhang Kexin , Huang Feng , Liu Luotao , Xiong Zhankun , Zhang Hongyu , Quan Yuan , Zhang Wen
Published: July 2024

The recent focus on microbes in human medicine highlights their potential role in the genetic framework of diseases. To decode the complex interactions among genes, microbes, and diseases, computational predictions of gene-microbe-disease (GMD) associations are crucial. Existing methods primarily address gene-disease and microbe-disease associations, but the more intricate triple-wise GMD associations remain less explored. In this paper, we propose a Heterogeneous Causal Metapath Graph Neural Network (HCMGNN) to predict GMD associations. HCMGNN constructs a heterogeneous graph linking genes, microbes, and diseases through their pairwise associations, and utilizes six predefined causal metapaths to extract directed causal subgraphs, which facilitate the multi-view analysis of causal relations among three entity types. Within each subgraph, we employ a causal semantic sharing message passing network for node representation learning, coupled with an attentive fusion method to integrate these representations for predicting GMD associations. Our extensive experiments show that HCMGNN effectively predicts GMD associations and addresses association sparsity issue by enhancing the graph's semantics and structure.
MEDVOC: Vocabulary Adaptation for Fine-tuning Pre-trained Language Models on Medical Text Summarization
Authors: Balde Gunjan , Roy Soumyadeep , Mondal Mainack , Ganguly Niloy
Published: July 2024

This work presents a dynamic vocabulary adaptation strategy, MEDVOC, for fine-tuning pre-trained language models (PLMs) like BertSumAbs, BART, and PEGASUS for improved medical text summarization. In contrast to existing domain adaptation approaches in summarization, MEDVOC treats vocabulary as an optimizable parameter and optimizes the PLM vocabulary based on fragment score conditioned only on the downstream task's reference summaries. Unlike previous works on vocabulary adaptation (limited only to classification tasks), optimizing vocabulary based on summarization tasks requires an extremely costly intermediate fine-tuning step on large summarization datasets. To that end, our novel fragment score-based hyperparameter search very significantly reduces this fine-tuning time --- from 450 days to less than 2 days on average. Furthermore, while previous works on vocabulary adaptation are often primarily tied to single PLMs, MEDVOC is designed to be deployable across multiple PLMs (with varying model vocabulary sizes, pre-training objectives, and model sizes) --- bridging the limited vocabulary overlap between the biomedical literature domain and PLMs. MEDVOC outperforms baselines by 15.74% in terms of Rouge-L in zero-shot setting and shows gains of 17.29% in high Out-Of-Vocabulary (OOV) concentrations. Our human evaluation shows MEDVOC generates more faithful medical summaries (88% compared to 59% in baselines).
FactCHD: Benchmarking Fact-Conflicting Hallucination Detection
Authors: Chen Xiang , Song Duanzheng , Gui Honghao , Wang Chenxi , Zhang Ningyu , Jiang Yong , Huang Fei , Lyu Chengfei , Zhang Dan , Chen Huajun
Published: July 2024

Despite their impressive generative capabilities, LLMs are hindered by fact-conflicting hallucinations in real-world applications. The accurate identification of hallucinations in texts generated by LLMs, especially in complex inferential scenarios, is a relatively unexplored area. To address this gap, we present FactCHD, a dedicated benchmark designed for the detection of fact-conflicting hallucinations from LLMs. FactCHD features a diverse dataset that spans various factuality patterns, including vanilla, multi-hop, comparison, and set operation. A distinctive element of FactCHD is its integration of fact-based evidence chains, significantly enhancing the depth of evaluating the detectors' explanations. Experiments on different LLMs expose the shortcomings of current approaches in detecting factual errors accurately. Furthermore, we introduce TRUTH-TRIANGULATOR which synthesizes reflective considerations by tool-enhanced ChatGPT and LoRA-tuning based on Llama2, aiming to yield more credible detection through the amalgamation of predictive results and evidence.
Position Debiasing Fine-Tuning for Causal Perception in Long-Term Dialogue
Authors: Fan Shixuan , Wei Wei , Li Wendi , Mao Xian-Ling , Xie Wenfeng , Chen Dangyang
Published: July 2024

The core of the dialogue system is to generate relevant, informative, and human-like responses based on extensive dialogue history. Recently, dialogue generation domain has seen mainstream adoption of large language models (LLMs), due to its powerful capability in generating utterances. However, there is a natural deficiency for such models, that is, inherent position bias, which may lead them to pay more attention to the nearby utterances instead of causally relevant ones, resulting in generating irrelevant and generic responses in long-term dialogue. To alleviate such problem, in this paper, we propose a novel method, named Causal Perception long-term Dialogue framework (CPD), which employs perturbation-based causal variable discovery method to extract casually relevant utterances from the dialogue history and enhances model causal perception during fine-tuning. Specifically, a local-position awareness method is proposed in CPD for inter-sentence position correlation elimination, which helps models extract causally relevant utterances based on perturbations. Then, a casual-perception fine-tuning strategy is also proposed, to enhance the capability of discovering the causal invariant factors, by differently perturbing causally relevant and non-casually relevant ones for response generation. Experimental results on two datasets prove that our proposed method can effectively alleviate the position bias for multiple LLMs and achieve significant progress compared with existing baselines.
ADELT: Transpilation between Deep Learning Frameworks
Authors: Gong Linyuan , Wang Jiayi , Cheung Alvin
Published: July 2024

We propose the Adversarial DEep Learning Transpiler (ADELT), a novel approach to source-to-source transpilation between deep learning frameworks. ADELT uniquely decouples code skeleton transpilation and API keyword mapping. For code skeleton transpilation, it uses few-shot prompting on large language models (LLMs), while for API keyword mapping, it uses contextual embeddings from a code-specific BERT. These embeddings are trained in a domain-adversarial setup to generate a keyword translation dictionary. ADELT is trained on an unlabeled web-crawled deep learning corpus, without relying on any hand-crafted rules or parallel data. It outperforms state-of-the-art transpilers, improving pass@1 rate by 16.2 pts and 15.0 pts for PyTorch-Keras and PyTorch-MXNet transpilation pairs respectively. We provide open access to our code at https://github.com/gonglinyuan/adelt
GRASP: A Novel Benchmark for Evaluating Language GRounding and Situated Physics Understanding in Multimodal Language Models
Authors: Jassim Serwan , Holubar Mario , Richter Annika , Wolff Cornelius , Ohmer Xenia , Bruni Elia
Published: July 2024

This paper presents GRASP, a novel benchmark to evaluate the language grounding and physical understanding capabilities of video-based multimodal large language models (LLMs). This evaluation is accomplished via a two-tier approach leveraging Unity simulations. The first level tests for language grounding by assessing a model's ability to relate simple textual descriptions with visual information. The second level evaluates the model's understanding of "Intuitive Physics" principles, such as object permanence and continuity. In addition to releasing the benchmark, we use it to evaluate several state-of-the-art multimodal LLMs. Our evaluation reveals significant shortcomings in the language grounding and intuitive physics capabilities of these models. Although they exhibit at least some grounding capabilities, particularly for colors and shapes, these capabilities depend heavily on the prompting strategy. At the same time, all models perform below or at the chance level of 50% in the Intuitive Physics tests, while human subjects are on average 80% correct. These identified limitations underline the importance of using benchmarks like GRASP to monitor the progress of future models in developing these competencies.
LLMem: Estimating GPU Memory Usage for Fine-Tuning Pre-Trained LLMs
Authors: Kim Taeho , Wang Yanming , Chaturvedi Vatshank , Gupta Lokesh , Kim Seyeon , Kwon Yongin , Ha Sangtae
Published: July 2024

Fine-tuning pre-trained large language models (LLMs) with limited hardware presents challenges due to GPU memory constraints. Various distributed fine-tuning methods have been proposed to alleviate memory constraints on GPU. However, determining the most effective method for achieving rapid fine-tuning while preventing GPU out-of-memory issues in a given environment remains unclear. To address this challenge, we introduce LLMem, a solution that estimates the GPU memory consumption when applying distributed fine-tuning methods across multiple GPUs and identifies the optimal method. We conduct GPU memory usage estimation prior to fine-tuning, leveraging the fundamental structure of transformer-based decoder models and the memory usage distribution of each method. Experimental results show that LLMem accurately estimates peak GPU memory usage on a single GPU, with an error rate of up to 1.6%. Additionally, it shows an average error rate of 3.0% when applying distributed fine-tuning methods to LLMs with more than a billion parameters on multi-GPU setups.
Evolutionary Generalized Zero-Shot Learning
Authors: Chen Dubing , Jiang Chenyi , Zhang Haofeng
Published: July 2024

Attribute-based Zero-Shot Learning (ZSL) has revolutionized the ability of models to recognize new classes not seen during training. However, with the advancement of large-scale models, the expectations have risen. Beyond merely achieving zero-shot generalization, there is a growing demand for universal models that can continually evolve in expert domains using unlabeled data. To address this, we introduce a scaled-down instantiation of this challenge: Evolutionary Generalized Zero-Shot Learning (EGZSL). This setting allows a low-performing zero-shot model to adapt to the test data stream and evolve online. We elaborate on three challenges of this special task, \ie, catastrophic forgetting, initial prediction bias, and evolutionary data class bias. Moreover, we propose targeted solutions for each challenge, resulting in a generic method capable of continuous evolution from a given initial IGZSL model. Experiments on three popular GZSL benchmark datasets demonstrate that our model can learn from the test data stream while other baselines fail. The codes are available at https://github.com/cdb342/EGZSL.
Reframing Spatial Reasoning Evaluation in Language Models: A Real-World Simulation Benchmark for Qualitative Reasoning
Authors: Li Fangjun , Hogg David C. , Cohn Anthony G.
Published: July 2024

Spatial reasoning plays a vital role in both human cognition and machine intelligence, prompting new research into language models' (LMs) capabilities in this regard. However, existing benchmarks reveal shortcomings in evaluating qualitative spatial reasoning (QSR). These benchmarks typically present oversimplified scenarios or unclear natural language descriptions, hindering effective evaluation. We present a novel benchmark for assessing QSR in LMs, which is grounded in realistic 3D simulation data, offering a series of diverse room layouts with various objects and their spatial relationships. This approach provides a more detailed and context-rich narrative for spatial reasoning evaluation, diverging from traditional, toy-task-oriented scenarios. Our benchmark encompasses a broad spectrum of qualitative spatial relationships, including topological, directional, and distance relations. These are presented with different viewing points, varied granularities, and density of relation constraints to mimic real-world complexities. A key contribution is our logic-based consistency-checking tool, which enables the assessment of multiple plausible solutions, aligning with real-world scenarios where spatial relationships are often open to interpretation. Our benchmark evaluation of advanced LMs reveals their strengths and limitations in spatial reasoning. They face difficulties with multi-hop spatial reasoning and interpreting a mix of different view descriptions, pointing to areas for future improvement.
LocMoE: A Low-overhead MoE for Large Language Model Training
Authors: Li Jing , Sun Zhijie , He Xuan , Zeng Li , Lin Yi , Li Entong , Zheng Binfan , Zhao Rongqian , Chen Xin
Published: July 2024

The Mixtures-of-Experts (MoE) model is a widespread distributed and integrated learning method for large language models (LLM), which is favored due to its ability to sparsify and expand models efficiently. However, the performance of MoE is limited by load imbalance and high latency of All-to-All communication, along with relatively redundant computation owing to large expert capacity. Load imbalance may result from existing routing policies that consistently tend to select certain experts. The frequent inter-node communication in the All-to-All procedure also significantly prolongs the training time. To alleviate the above performance problems, we propose a novel routing strategy that combines load balance and locality by converting partial inter-node communication to that of intra-node. Notably, we elucidate that there is a minimum threshold for expert capacity, calculated through the maximal angular deviation between the gating weights of the experts and the assigned tokens. We port these modifications on the PanGu-Σ model based on the MindSpore framework with multi-level routing and conduct experiments on Ascend clusters. The experiment results demonstrate that the proposed LocMoE reduces training time per epoch by 12.68% to 22.24% compared to classical routers, such as hash router and switch router, without impacting the model accuracy.
Improving Zero-Shot Cross-Lingual Transfer via Progressive Code-Switching
Authors: Li Zhuoran , Hu Chunming , Chen Junfan , Chen Zhijun , Guo Xiaohui , Zhang Richong
Published: July 2024

Code-switching is a data augmentation scheme mixing words from multiple languages into source lingual text. It has achieved considerable generalization performance of cross-lingual transfer tasks by aligning cross-lingual contextual word representations. However, uncontrolled and over-replaced code-switching would augment dirty samples to model training. In other words, the excessive code-switching text samples will negatively hurt the models' cross-lingual transferability. To this end, we propose a Progressive Code-Switching (PCS) method to gradually generate moderately difficult code-switching examples for the model to discriminate from easy to hard. The idea is to incorporate progressively the preceding learned multilingual knowledge using easier code-switching data to guide model optimization on succeeding harder code-switching data. Specifically, we first design a difficulty measurer to measure the impact of replacing each word in a sentence based on the word relevance score. Then a code-switcher generates the code-switching data of increasing difficulty via a controllable temperature variable. In addition, a training scheduler decides when to sample harder code-switching data for model training. Experiments show our model achieves state-of-the-art results on three different zero-shot cross-lingual transfer tasks across ten languages.
Separate in the Speech Chain: Cross-Modal Conditional Audio-Visual Target Speech Extraction
Authors: Mu Zhaoxi , Yang Xinyu
Published: July 2024

The integration of visual cues has revitalized the performance of the target speech extraction task, elevating it to the forefront of the field. Nevertheless, this multi-modal learning paradigm often encounters the challenge of modality imbalance. In audio-visual target speech extraction tasks, the audio modality tends to dominate, potentially overshadowing the importance of visual guidance. To tackle this issue, we propose AVSepChain, drawing inspiration from the speech chain concept. Our approach partitions the audio-visual target speech extraction task into two stages: speech perception and speech production. In the speech perception stage, audio serves as the dominant modality, while visual information acts as the conditional modality. Conversely, in the speech production stage, the roles are reversed. This transformation of modality status aims to alleviate the problem of modality imbalance. Additionally, we introduce a contrastive semantic matching loss to ensure that the semantic information conveyed by the generated speech aligns with the semantic information conveyed by lip movements during the speech production stage. Through extensive experiments conducted on multiple benchmark datasets for audio-visual target speech extraction, we showcase the superior performance achieved by our proposed method.
Cross-Domain Few-Shot Semantic Segmentation via Doubly Matching Transformation
Authors: Chen Jiayi , Quan Rong , Qin Jie
Published: July 2024

Cross-Domain Few-shot Semantic Segmentation (CD-FSS) aims to train generalized models that can segment classes from different domains with a few labeled images. Previous works have proven the effectiveness of feature transformation in addressing CD-FSS. However, they completely rely on support images for feature transformation, and repeatedly utilizing a few support images for each class may easily lead to overfitting and overlooking intra-class appearance differences. In this paper, we propose a Doubly Matching Transformation-based Network (DMTNet) to solve the above issue. Instead of completely relying on support images, we propose Self-Matching Transformation (SMT) to construct query-specific transformation matrices based on query images themselves to transform domain-specific query features into domain-agnostic ones. Calculating query-specific transformation matrices can prevent overfitting, especially for the meta-testing stage where only one or several images are used as support images to segment hundreds or thousands of images. After obtaining domain-agnostic features, we exploit a Dual Hypercorrelation Construction (DHC) module to explore the hypercorrelations between the query image with the foreground and background of the support image, based on which foreground and background prediction maps are generated and supervised, respectively, to enhance the segmentation result. In addition, we propose a Test-time Self-Finetuning (TSF) strategy to more accurately self-tune the query prediction in unseen domains. Extensive experiments on four popular datasets show that DMTNet achieves superior performance over state-of-the-art approaches. Code is available at https://github.com/ChenJiayi68/DMTNet.
ScreenAgent: A Vision Language Model-driven Computer Control Agent
Authors: Niu Runliang , Li Jindong , Wang Shiqi , Fu Yali , Hu Xiyu , Leng Xueyuan , Kong He , Chang Yi , Wang Qi
Published: July 2024

Large Language Models (LLM) can invoke a variety of tools and APIs to complete complex tasks. The computer, as the most powerful and universal tool, could potentially be controlled by a trained LLM agent. Powered by the computer, we can hopefully build a more generalized agent to assist humans in various daily digital works. In this paper, we construct an environment for a Vision Language Model (VLM) agent to interact with a real computer screen. Within this environment, the agent can observe screenshots and manipulate the Graphical User Interface (GUI) by outputting mouse and keyboard actions. We also design an automated control pipeline that includes planning, acting, and reflecting phases, guiding the agent to continuously interact with the environment and complete multi-step tasks. Additionally, we construct the ScreenAgent Dataset, which collects screenshots and action sequences when completing daily computer tasks. Finally, we train a model, ScreenAgent, which achieves comparable computer control capabilities to GPT-4V and demonstrated more precise UI positioning capabilities. Our attempts could inspire further research on building a generalist LLM agent. The code and more detailed information are at https://github.com/niuzaisheng/ScreenAgent.
Contrastive Representation Learning for Self-Supervised Taxonomy Completion
Authors: Niu Yuhang , Xu Hongyuan , Liu Ciyi , Wen Yanlong , Yuan Xiaojie
Published: July 2024

Taxonomy completion, a self-supervised task, aims to add new concepts to an existing taxonomy by attaching them to appropriate hypernym and hyponym pairs. Researchers have proposed several approaches to capture the essential relationships in taxonomy using semantic or structural information. However, they either construct training signals from a single view or simply use a random sampling strategy, making it insufficient to capture various relations in taxonomic structure and learn quality representations. To address this, we propose CoSTC, a contrastive learning framework that captures diverse relations and improves representations for taxonomy completion. It uses two contrasting views, namely intra-view and inter-view, to provide rich self-supervised signals. In intra-view contrasting, we exploit the correlations within queries and within positions by performing instance-level discrimination task. In inter-view contrasting, we use a sampling strategy that considers diversity and hardness to select representative pairs, enhancing the learning of fine-grained query-position relations. Experimental results on three datasets verify the effectiveness of our approach. Our code is available at https://github.com/nyh-a/CoSTC.
Innovative Directional Encoding in Speech Processing: Leveraging Spherical Harmonics Injection for Multi-Channel Speech Enhancement
Authors: Pan Jiahui , Shen Pengjie , Zhang Hui , Zhang Xueliang
Published: July 2024

Multi-channel speech enhancement leverages multiple microphones to extract target speech signals amid background noise. Effectively utilizing directional cues is key for robust enhancement. While deep learning shows promise for multi-channel speech processing, most methods operate on short-time Fourier transform (STFT) coefficients directly. We propose using spherical harmonics transform (SHT) coefficients as auxiliary inputs to models. which concisely represent spatial distributions. SHT allows signals from varying numbers of microphones to be converted into coefficients of a consistent dimension. The proposed technique enables a single model to generalize to microphone arrays with varying configurations, rather than requiring a specialized model for each array layout. We present two architectures with SHT-based auxiliary inputs: parallel and serial. Specifically, the parallel model contains two encoders - one for STFT and another for SHT. By fusing both encoders' outputs in the decoder to estimate the enhanced STFT, it effectively incorporates spatial context. For the serial approach, we first apply SHT to the signals and then take STFT of the transformed signals as network inputs. Evaluations of the TIMIT dataset under fluctuating noise and reverberation demonstrate our model outperforms established benchmarks. Remarkably, these results are attained with reduced computations and parameters. Furthermore, experiments on the MS-SNSD dataset show the proposed method can enhance the generalization ability of networks. The source code is publicly accessible at https://github.com/Pandade1997/SH_injection.
Memorizing Documents with Guidance in Large Language Models
Authors: Park Bumjin , Choi Jaesik
Published: July 2024

Training data plays a pivotal role in AI models. Large language models (LLMs) are trained with massive amounts of documents, and their parameters hold document-related contents. Recently, several studies identified content-specific locations in LLMs by examining the parameters. Instead of the post hoc interpretation, we propose another approach. We propose document-wise memory architecture to track document memories in training. The proposed architecture maps document representations to memory entries, which softly mask memories in the forward process of LLMs. Additionally, we propose document guidance loss, which increases the likelihood of text with document memories and reduces the likelihood of the text with the memories of other documents. Experimental results on Wikitext-103-v1 with Pythia-1B show that the proposed methods provide different memory entries for documents and high recall of document-related content in generation with trained document-wise memories.
Contextualized Speech Recognition: Rethinking Second-Pass Rescoring with Generative Large Language Models
Authors: Tang Yixuan , Tung Anthony K. H.
Published: July 2024

Automatic Speech Recognition (ASR) systems have witnessed notable advancements in recent years. Contextualized ASR tasks require recognizing speech not as isolated utterances but within the broader context in which they occur. Conventional approaches often employ a second-pass paradigm to re-rank initial transcriptions, yet they risk propagating errors across candidate hypotheses, thereby compromising recognition precision. In this study, we introduce a novel framework that diverges from typical second-pass rescoring methods. Given n-best hypotheses, we leverage prompting with a large language model for contextualized second-pass generation. Besides pursuing higher accuracy, we aim to explore the performance boundaries without substantially altering the underlying pre-trained speech and language models. We investigate the effectiveness of the proposed paradigm through zero-shot prompting and strategic low-rank adaptation tuning. On the multi-accent spoken reading comprehension benchmark SQuAD-SRC, both prompting and fine-tuned models outperform the 1-best ASR hypothesis, achieving notable relative Word Error Rate (WER) improvements of 13.6% and 45.9%, respectively. The results suggest that the proposed approach enhances transcription accuracy and contextual understanding.
Correct and Optimal: The Regular Expression Inference Challenge
Authors: Valizadeh Mojtaba , Gorinski Philip John , Iacobacci Ignacio , Berger Martin
Published: July 2024

We propose regular expression inference (REI) as a challenge for code/language modelling, and the wider machine learning community. REI is a supervised machine learning (ML) and program optimisation task, and poses the problem of finding minimal regular expressions from examples: Given two finite sets of strings P and N and a cost function cost(·), the task is to generate an expression r that accepts all strings in P and rejects all strings in N , while no other such expression r′ exists with cost(r′) < cost(r).

REI has advantages as a challenge problem: (i) regular expressions are well-known, widely used, and a natural idealisation of code; (ii) REI’s asymptotic worst-case complexity is well understood; (iii) REI has a small number of easy to understand parameters (e.g. P or N cardinality, string lengths of examples, or the cost function); this lets us easily finetune REI-hardness; (iv) REI, with its emphasis on optimisation, is an unsolved problem for deep learning based ML.

Recently, an REI solver was implemented on GPUs, using program synthesis techniques. This enabled, for the first time, fast generation of minimal regular expressions for complex REI instances. Building on this advance, we generate and publish the first large-scale datasets for REI, and devise and evaluate several initial heuristic and machine learning baselines.

We invite the community to participate and explore ML methods that learn to solve REI problems. We believe that progress in REI directly translates to progress in code/language modelling.
HyDiscGAN: A Hybrid Distributed cGAN for Audio-Visual Privacy Preservation in Multimodal Sentiment Analysis
Authors: Wu Zhuojia , Zhang Qi , Miao Duoqian , Yi Kun , Fan Wei , Hu Liang
Published: July 2024

Multimodal Sentiment Analysis (MSA) aims to identify speakers' sentiment tendencies in multimodal video content, raising serious concerns about privacy risks associated with multimodal data, such as voiceprints and facial images. Recent distributed collaborative learning has been verified as an effective paradigm for privacy preservation in multimodal tasks. However, they often overlook the privacy distinctions among different modalities, struggling to strike a balance between performance and privacy preservation. Consequently, it poses an intriguing question of maximizing multimodal utilization to improve performance while simultaneously protecting necessary modalities. This paper forms the first attempt at modality-specified (i.e., audio and visual) privacy preservation in MSA tasks. We propose a novel Hybrid Distributed cross-modality cGAN framework (HyDiscGAN), which learns multimodality alignment to generate fake audio and visual features conditioned on shareable de-identified textual data. The objective is to leverage the fake features to approximate real audio and visual content to guarantee privacy preservation while effectively enhancing performance. Extensive experiments show that compared with the state-of-the-art MSA model, HyDiscGAN can achieve superior or competitive performance while preserving privacy.
It Ain’t That Bad: Understanding the Mysterious Performance Drop in OOD Generalization for Generative Transformer Models
Authors: Xu Xingcheng , Pan Zihao , Zhang Haipeng , Yang Yanqing
Published: July 2024

Large language models (LLMs) have achieved remarkable proficiency on solving diverse problems. However, their generalization ability is not always satisfying and the generalization problem is common for generative transformer models in general. Researchers take basic mathematical tasks like n-digit addition or multiplication as important perspectives for investigating their generalization behaviors. It is observed that when training models on n-digit operations (e.g., additions) in which both input operands are n-digit in length, models generalize successfully on unseen n-digit inputs (in-distribution (ID) generalization), but fail miserably on longer, unseen cases (out-of-distribution (OOD) generalization). We bring this unexplained performance drop into attention and ask whether there is systematic OOD generalization. Towards understanding LLMs, we train various smaller language models which may share the same underlying mechanism. We discover that the strong ID generalization stems from structured representations, while behind the unsatisfying OOD performance, the models still exhibit clear learned algebraic structures. Specifically, these models map unseen OOD inputs to outputs with learned equivalence relations in the ID domain, which we call the equivalence generalization. These findings deepen our knowledge regarding the generalizability of generative models including LLMs, and provide insights into potential avenues for improvement.