arXiv subjects

Qing Gu

Publications and source records attributed to Qing Gu.

At least 37 records · Page 2Linked to original sources

arXiv2025

Steering When Necessary: Flexible Steering Large Language Models with Backtracking

Large language models (LLMs) have achieved remarkable performance across many generation tasks. Nevertheless, effectively aligning them with desired behaviors remains a significant challenge. Activation steering is an effective and cost-efficient approach that directly modifies the activations of LLMs during the inference stage, aligning their responses with the desired behaviors and avoiding the high cost of fine-tuning. Existing methods typically indiscriminately intervene to all generations or rely solely on the question to determine intervention, which limits the accurate assessment of the intervention strength. To this end, we propose the Flexible Activation Steering with Backtracking (FASB) framework, which dynamically determines both the necessity and strength of intervention by tracking the internal states of the LLMs during generation, considering both the question and the generated content. Since intervening after detecting a deviation from the desired behavior is often too late, we further propose the backtracking mechanism to correct the deviated tokens and steer the LLMs toward the desired behavior. Extensive experiments on the TruthfulQA dataset and six multiple-choice datasets demonstrate that our method outperforms baselines. Our code will be released at https://github.com/gjw185/FASB.

cs.CL↗

arXiv2025

An Efficient Transfer Learning Method Based on Adapter with Local Attributes for Speech Emotion Recognition

Existing speech emotion recognition (SER) methods commonly suffer from the lack of high-quality large-scale corpus, partly due to the complex, psychological nature of emotion which makes accurate labeling difficult and time consuming. Recently, transfer learning based methods that exploit the encoders pretrained on large-scale speech corpus (e.g., Wav2Vec2.0 and HuBERT) have shown strong potential for downstream SER tasks. However, task-specific fine-tuning remains necessary for various conversational scenarios of different topics, speakers and languages to achieve satisfactory performance. It generally requires costly encoder retraining for individual SER tasks. To address this issue, we propose to train an adapter with local attributes for efficient transfer learning. Specifically, a weighted average pooling-Transformer (WAP-Transformer) is proposed as a lightweight backbone to enrich the frame-level representation. An adapter with teacher-student branches is exploited for task-agnostic transfer learning, where the student branch is jointly optimized via mask prediction and self-distillation objectives, and the teacher branch is obtained online from the student via exponential moving average (EMA). Meanwhile, local attributes are learned from the teacher branch via unsupervised clustering, which aims to act as a universal model that provides additional semantic-rich supervisions. A statistical attentive pooling (SAP) module is proposed to obtain utterance representation for fine-tuning. To evaluate the effectiveness of the proposed adapter with local attributes, extensive experiments have been conducted on IEMOCAP. Superior performance has been reported, compared to the previous state-of-the-art methods in similar settings.

cs.SD↗

arXiv2025

Cross-Modal Prototype Augmentation and Dual-Grained Prompt Learning for Social Media Popularity Prediction

Social Media Popularity Prediction is a complex multimodal task that requires effective integration of images, text, and structured information. However, current approaches suffer from inadequate visual-textual alignment and fail to capture the inherent cross-content correlations and hierarchical patterns in social media data. To overcome these limitations, we establish a multi-class framework , introducing hierarchical prototypes for structural enhancement and contrastive learning for improved vision-text alignment. Furthermore, we propose a feature-enhanced framework integrating dual-grained prompt learning and cross-modal attention mechanisms, achieving precise multimodal representation through fine-grained category modeling. Experimental results demonstrate state-of-the-art performance on benchmark metrics, establishing new reference standards for multimodal social media analysis.

cs.IR↗

arXiv2025

Hierarchical Vision-Language Reasoning for Multimodal Multiple-Choice Question Answering

Multimodal Large Language Models (MLLMs) have demonstrated remarkable multimodal understanding capabilities in Visual Question Answering (VQA) tasks by integrating visual and textual features. However, under the challenging ten-choice question evaluation paradigm, existing methods still exhibit significant limitations when processing PDF documents with complex layouts and lengthy content. Notably, current mainstream models suffer from a strong bias toward English training data, resulting in suboptimal performance for Japanese and other language scenarios. To address these challenges, this paper proposes a novel Japanese PDF document understanding framework that combines multimodal hierarchical reasoning mechanisms with Colqwen-optimized retrieval methods, while innovatively introducing a semantic verification strategy through sub-question decomposition. Experimental results demonstrate that our framework not only significantly enhances the model's deep semantic parsing capability for complex documents, but also exhibits superior robustness in practical application scenarios.

cs.IR↗

arXiv2025

Token Prepending: A Training-Free Approach for Eliciting Better Sentence Embeddings from LLMs

Extracting sentence embeddings from large language models (LLMs) is a promising direction, as LLMs have demonstrated stronger semantic understanding capabilities. Previous studies typically focus on prompt engineering to elicit sentence embeddings from LLMs by prompting the model to encode sentence information into the embedding of the last token. However, LLMs are mostly decoder-only models with causal attention and the earlier tokens in the sentence cannot attend to the latter tokens, resulting in biased encoding of sentence information and cascading effects on the final decoded token. To this end, we propose a novel Token Prepending (TP) technique that prepends each layer's decoded sentence embedding to the beginning of the sentence in the next layer's input, allowing earlier tokens to attend to the complete sentence information under the causal attention mechanism. The proposed TP technique is a plug-and-play and training-free technique, which means it can be seamlessly integrated with various prompt-based sentence embedding methods and autoregressive LLMs. Extensive experiments on various Semantic Textual Similarity (STS) tasks and downstream classification tasks demonstrate that our proposed TP technique can significantly improve the performance of existing prompt-based sentence embedding methods across different LLMs, while incurring negligible additional inference cost.

cs.CL↗

arXiv2025

Multi-Prompting Decoder Helps Better Language Understanding

Recent Pre-trained Language Models (PLMs) usually only provide users with the inference APIs, namely the emerging Model-as-a-Service (MaaS) setting. To adapt MaaS PLMs to downstream tasks without accessing their parameters and gradients, some existing methods focus on the output-side adaptation of PLMs, viewing the PLM as an encoder and then optimizing a task-specific decoder for decoding the output hidden states and class scores of the PLM. Despite the effectiveness of these methods, they only use a single prompt to query PLMs for decoding, leading to a heavy reliance on the quality of the adopted prompt. In this paper, we propose a simple yet effective Multi-Prompting Decoder (MPD) framework for MaaS adaptation. The core idea is to query PLMs with multiple different prompts for each sample, thereby obtaining multiple output hidden states and class scores for subsequent decoding. Such multi-prompting decoding paradigm can simultaneously mitigate reliance on the quality of a single prompt, alleviate the issue of data scarcity under the few-shot setting, and provide richer knowledge extracted from PLMs. Specifically, we propose two decoding strategies: multi-prompting decoding with optimal transport for hidden states and calibrated decoding for class scores. Extensive experiments demonstrate that our method achieves new state-of-the-art results on multiple natural language understanding datasets under the few-shot setting.

cs.CL↗

arXiv2025

Contrastive Prompting Enhances Sentence Embeddings in LLMs through Inference-Time Steering

Extracting sentence embeddings from large language models (LLMs) is a practical direction, as it requires neither additional data nor fine-tuning. Previous studies usually focus on prompt engineering to guide LLMs to encode the core semantic information of the sentence into the embedding of the last token. However, the last token in these methods still encodes an excess of non-essential information, such as stop words, limiting its encoding capacity. To this end, we propose a Contrastive Prompting (CP) method that introduces an extra auxiliary prompt to elicit better sentence embedding. By contrasting with the auxiliary prompt, CP can steer existing prompts to encode the core semantics of the sentence, rather than non-essential information. CP is a plug-and-play inference-time intervention method that can be combined with various prompt-based methods. Extensive experiments on Semantic Textual Similarity (STS) tasks and downstream classification tasks demonstrate that our method can improve the performance of existing prompt-based methods across different LLMs. Our code will be released at https://github.com/zifengcheng/CP.

cs.CL↗

arXiv2025

THz Emission from Spintronic Microstructure

Recent advancements in spintronics have opened a new avenue in terahertz (THz) radiation sources that may outperform the traditional contact-based metallic counterparts. Inspired by the generation of broadband spintronic THz signals at the interface of a ferromagnet and ultrawide bandgap semiconductors, here we investigated the generation of THz radiation from micro-structured heterostructures of a metallic ferromagnet (Ni80Fe20) and an ultrawide bandgap semiconductor (AlGaN/GaN) that contains a layer of 2D electron gas. By precisely tailoring the dimension of the subwavelength pillars of a THz device, the micro-structured spintronic THz emitter can achieve up to more than three times higher emission intensity compared to that of the un-patterned counterpart. Our study advances the development of the next generation of spintronic THz sources that allow a tailored emission frequency and intensity control and, further, are compatible with existing integrated wide-bandgap semiconductor circuits.

cond-mat.mes-hall↗

arXiv2025

High-efficiency 2D Grating Design for the Magneto-Optical Trap: Enhancing Intensity Balance and Reducing Optical Complexity

Diffraction gratings integrated into an atomic, molecular, and optical (AMO) setup offer a compact and efficient route toward atom cooling and trapping, thus preparing magneto-optical traps (MOT) for insertion into future scalable quantum systems. Here, we propose and numerically validate a two-dimensional (2D) diffraction grating that satisfies the required optical conditions for laser cooling, namely, radiation pressure balance, specular reflection cancellation, and circular polarization handedness reversal upon diffraction, thus achieving an optical molasses, a necessary condition in MOT. Using Rigorous Coupled Wave Analysis (RCWA) and a Genetic Algorithm (GA), we optimize the grating's geometry to maximize key figures of merit. The grating consists of a 2D square lattice of nanoholes and is designed to diffract normally incident 780 nm circularly polarized light into the four first orders, each with a diffraction efficiency of 0.24, of which 99.7% has the correct circular handedness. Our 2D diffraction grating can enhance the performance of GMOTs employing 2D gratings by improving the axial radiation pressure balance and providing a high degree of circular polarization. Furthermore, we investigated the robustness of our 2D grating to ensure high manufacturing resilience.

physics.optics↗

arXiv2025

Ensembling Diffusion Models via Adaptive Feature Aggregation

The success of the text-guided diffusion model has inspired the development and release of numerous powerful diffusion models within the open-source community. These models are typically fine-tuned on various expert datasets, showcasing diverse denoising capabilities. Leveraging multiple high-quality models to produce stronger generation ability is valuable, but has not been extensively studied. Existing methods primarily adopt parameter merging strategies to produce a new static model. However, they overlook the fact that the divergent denoising capabilities of the models may dynamically change across different states, such as when experiencing different prompts, initial noises, denoising steps, and spatial locations. In this paper, we propose a novel ensembling method, Adaptive Feature Aggregation (AFA), which dynamically adjusts the contributions of multiple models at the feature level according to various states (i.e., prompts, initial noises, denoising steps, and spatial locations), thereby keeping the advantages of multiple diffusion models, while suppressing their disadvantages. Specifically, we design a lightweight Spatial-Aware Block-Wise (SABW) feature aggregator that adaptive aggregates the block-wise intermediate features from multiple U-Net denoisers into a unified one. The core idea lies in dynamically producing an individual attention map for each model's features by comprehensively considering various states. It is worth noting that only SABW is trainable with about 50 million parameters, while other models are frozen. Both the quantitative and qualitative experiments demonstrate the effectiveness of our proposed Adaptive Feature Aggregation method.

cs.CV↗

arXiv2025

Plasmonic Double-hole Bull's Eye Nano-antenna for Far-field Polarization Control

Plasmonic polarization conversion offers significant advantages over conventional methods, including a smaller device footprint and easier integration into photonic circuits. In this work, we numerically and experimentally investigate the polarization conversion properties of a plasmonic double-hole structure surrounded by circular nanograting, i.e., a bull's eye antenna. Using a combination of polarimetric imaging via back focal plane (BFP) microscopy and the Stokes parameter analysis, we demonstrate the functionality of our structure as a miniature on chip polarization converter. Our results show that this nanostructure enables complex polarization transformations, including converting linear to circular polarization and vice versa. The polarization conversion efficiency is found to be dependent on the periodicity of the circular gratings and is particularly pronounced in the central region of the Fourier space. Moreover, the strong asymmetric scattering leads to distinctive patterns in the Stokes parameters across various incident polarization states. This work provides insights into the plasmonic manipulation of light polarization at the nanoscale, with potential applications in miniature on-chip polarization convertors, polarization-controlled emitters, and advanced sensing technologies.

physics.optics↗

arXiv2025

Single-defect mode lasing in a non-Hermitian 1D trivial SSH lattice

We demonstrate robust single-mode lasing in a non-Hermitian 1D trivial Su-Schrieffer-Heeger defective lattice. This structure exhibits a significantly lower threshold power compared to the non-defective lattice, with single-mode lasing sustained across a wide range of pump powers. Notably, our proposed device requires pumping of only the defect ring, leading to minimal required pump power. Moreover, lasing is not confined to the edges but can occur at any point within the lattice.

physics.optics↗

arXiv2025

Implicit Location-Caption Alignment via Complementary Masking for Weakly-Supervised Dense Video Captioning

Weakly-Supervised Dense Video Captioning (WSDVC) aims to localize and describe all events of interest in a video without requiring annotations of event boundaries. This setting poses a great challenge in accurately locating the temporal location of event, as the relevant supervision is unavailable. Existing methods rely on explicit alignment constraints between event locations and captions, which involve complex event proposal procedures during both training and inference. To tackle this problem, we propose a novel implicit location-caption alignment paradigm by complementary masking, which simplifies the complex event proposal and localization process while maintaining effectiveness. Specifically, our model comprises two components: a dual-mode video captioning module and a mask generation module. The dual-mode video captioning module captures global event information and generates descriptive captions, while the mask generation module generates differentiable positive and negative masks for localizing the events. These masks enable the implicit alignment of event locations and captions by ensuring that captions generated from positively and negatively masked videos are complementary, thereby forming a complete video description. In this way, even under weak supervision, the event location and event caption can be aligned implicitly. Extensive experiments on the public datasets demonstrate that our method outperforms existing weakly-supervised methods and achieves competitive results compared to fully-supervised methods.

cs.CV↗

arXiv2024

Leveraging Multiplexed Metasurfaces for Multi-Task Learning with All-Optical Diffractive Processors

Diffractive Neural Networks (DNNs) leverage the power of light to enhance computational performance in machine learning, offering a pathway to high-speed, low-energy, and large-scale neural information processing. However, most existing DNN architectures are optimized for single tasks and thus lack the flexibility required for the simultaneous execution of multiple tasks within a unified artificial intelligence platform. In this work, we utilize the polarization and wavelength degrees of freedom of light to achieve optical multi-task identification using the MNIST, FMNIST, and KMNIST datasets. Employing bilayer cascaded metasurfaces, we construct dual-channel DNNs capable of simultaneously classifying two tasks, using polarization and wavelength multiplexing schemes through a meta-atom library. Numerical evaluations demonstrate performance accuracies comparable to those of individually trained single-channel, single-task DNNs. Extending this approach to three-task parallel recognition reveals an expected performance decline yet maintains satisfactory classification accuracies of greater than 80% for all tasks. We further introduce a novel end-to-end joint optimization framework to redesign the three-task classifier, demonstrating substantial improvements over the meta-atom library design and offering the potential for future multi-channel DNN designs. Our study could pave the way for the development of ultrathin, high-speed, and high-throughput optical neural computing systems.

physics.optics↗

arXiv2024

Prototype based Masked Audio Model for Self-Supervised Learning of Sound Event Detection

A significant challenge in sound event detection (SED) is the effective utilization of unlabeled data, given the limited availability of labeled data due to high annotation costs. Semi-supervised algorithms rely on labeled data to learn from unlabeled data, and the performance is constrained by the quality and size of the former. In this paper, we introduce the Prototype based Masked Audio Model~(PMAM) algorithm for self-supervised representation learning in SED, to better exploit unlabeled data. Specifically, semantically rich frame-level pseudo labels are constructed from a Gaussian mixture model (GMM) based prototypical distribution modeling. These pseudo labels supervise the learning of a Transformer-based masked audio model, in which binary cross-entropy loss is employed instead of the widely used InfoNCE loss, to provide independent loss contributions from different prototypes, which is important in real scenarios in which multiple labels may apply to unsupervised data frames. A final stage of fine-tuning with just a small amount of labeled data yields a very high performing SED model. On like-for-like tests using the DESED task, our method achieves a PSDS1 score of 62.5\%, surpassing current state-of-the-art models and demonstrating the superiority of the proposed technique.

cs.SD↗

arXiv2024

LongRecipe: Recipe for Efficient Long Context Generalization in Large Language Models

Large language models (LLMs) face significant challenges in handling long-context tasks because of their limited effective context window size during pretraining, which restricts their ability to generalize over extended sequences. Meanwhile, extending the context window in LLMs through post-pretraining is highly resource-intensive. To address this, we introduce LongRecipe, an efficient training strategy for extending the context window of LLMs, including impactful token analysis, position index transformation, and training optimization strategies. It simulates long-sequence inputs while maintaining training efficiency and significantly improves the model's understanding of long-range dependencies. Experiments on three types of LLMs show that LongRecipe can utilize long sequences while requiring only 30% of the target context window size, and reduces computational training resource over 85% compared to full sequence training. Furthermore, LongRecipe also preserves the original LLM's capabilities in general tasks. Ultimately, we can extend the effective context window of open-source LLMs from 8k to 128k, achieving performance close to GPT-4 with just one day of dedicated training using a single GPU with 80G memory. Our code is released at https://github.com/zhiyuanhubj/LongRecipe.

cs.CL↗

arXiv2024

USTC-KXDIGIT System Description for ASVspoof5 Challenge

This paper describes the USTC-KXDIGIT system submitted to the ASVspoof5 Challenge for Track 1 (speech deepfake detection) and Track 2 (spoofing-robust automatic speaker verification, SASV). Track 1 showcases a diverse range of technical qualities from potential processing algorithms and includes both open and closed conditions. For these conditions, our system consists of a cascade of a frontend feature extractor and a back-end classifier. We focus on extensive embedding engineering and enhancing the generalization of the back-end classifier model. Specifically, the embedding engineering is based on hand-crafted features and speech representations from a self-supervised model, used for closed and open conditions, respectively. To detect spoof attacks under various adversarial conditions, we trained multiple systems on an augmented training set. Additionally, we used voice conversion technology to synthesize fake audio from genuine audio in the training set to enrich the synthesis algorithms. To leverage the complementary information learned by different model architectures, we employed activation ensemble and fused scores from different systems to obtain the final decision score for spoof detection. During the evaluation phase, the proposed methods achieved 0.3948 minDCF and 14.33% EER in the close condition, and 0.0750 minDCF and 2.59% EER in the open condition, demonstrating the robustness of our submitted systems under adversarial conditions. In Track 2, we continued using the CM system from Track 1 and fused it with a CNN-based ASV system. This approach achieved 0.2814 min-aDCF in the closed condition and 0.0756 min-aDCF in the open condition, showcasing superior performance in the SASV system.

cs.SD↗

arXiv2024

A Debiased Nearest Neighbors Framework for Multi-Label Text Classification

Multi-Label Text Classification (MLTC) is a practical yet challenging task that involves assigning multiple non-exclusive labels to each document. Previous studies primarily focus on capturing label correlations to assist label prediction by introducing special labeling schemes, designing specific model structures, or adding auxiliary tasks. Recently, the $k$ Nearest Neighbor ($k$NN) framework has shown promise by retrieving labeled samples as references to mine label co-occurrence information in the embedding space. However, two critical biases, namely embedding alignment bias and confidence estimation bias, are often overlooked, adversely affecting prediction performance. In this paper, we introduce a DEbiased Nearest Neighbors (DENN) framework for MLTC, specifically designed to mitigate these biases. To address embedding alignment bias, we propose a debiased contrastive learning strategy, enhancing neighbor consistency on label co-occurrence. For confidence estimation bias, we present a debiased confidence estimation strategy, improving the adaptive combination of predictions from $k$NN and inductive binary classifications. Extensive experiments conducted on four public benchmark datasets (i.e., AAPD, RCV1-V2, Amazon-531, and EUR-LEX57K) showcase the effectiveness of our proposed method. Besides, our method does not introduce any extra parameters.

cs.CL↗