arXiv subjects

Yichuan Li

Publications and source records attributed to Yichuan Li.

At least 19 recordsLinked to original sources

arXiv2025

IHEval: Evaluating Language Models on Following the Instruction Hierarchy

The instruction hierarchy, which establishes a priority order from system messages to user messages, conversation history, and tool outputs, is essential for ensuring consistent and safe behavior in language models (LMs). Despite its importance, this topic receives limited attention, and there is a lack of comprehensive benchmarks for evaluating models' ability to follow the instruction hierarchy. We bridge this gap by introducing IHEval, a novel benchmark comprising 3,538 examples across nine tasks, covering cases where instructions in different priorities either align or conflict. Our evaluation of popular LMs highlights their struggle to recognize instruction priorities. All evaluated models experience a sharp performance decline when facing conflicting instructions, compared to their original instruction-following performance. Moreover, the most competitive open-source model only achieves 48% accuracy in resolving such conflicts. Our results underscore the need for targeted optimization in the future development of LMs.

cs.CL↗

arXiv2024

From Critique to Clarity: A Pathway to Faithful and Personalized Code Explanations with Large Language Models

In the realm of software development, providing accurate and personalized code explanations is crucial for both technical professionals and business stakeholders. Technical professionals benefit from enhanced understanding and improved problem-solving skills, while business stakeholders gain insights into project alignments and transparency. Despite the potential, generating such explanations is often time-consuming and challenging. This paper presents an innovative approach that leverages the advanced capabilities of large language models (LLMs) to generate faithful and personalized code explanations. Our methodology integrates prompt enhancement, self-correction mechanisms, personalized content customization, and interaction with external tools, facilitated by collaboration among multiple LLM agents. We evaluate our approach using both automatic and human assessments, demonstrating that our method not only produces accurate explanations but also tailors them to individual user preferences. Our findings suggest that this approach significantly improves the quality and relevance of code explanations, offering a valuable tool for developers and stakeholders alike.

cs.SE↗

arXiv2024

Let's Ask GNN: Empowering Large Language Model for Graph In-Context Learning

Textual Attributed Graphs (TAGs) are crucial for modeling complex real-world systems, yet leveraging large language models (LLMs) for TAGs presents unique challenges due to the gap between sequential text processing and graph-structured data. We introduce AskGNN, a novel approach that bridges this gap by leveraging In-Context Learning (ICL) to integrate graph data and task-specific information into LLMs. AskGNN employs a Graph Neural Network (GNN)-powered structure-enhanced retriever to select labeled nodes across graphs, incorporating complex graph structures and their supervision signals. Our learning-to-retrieve algorithm optimizes the retriever to select example nodes that maximize LLM performance on graph. Experiments across three tasks and seven LLMs demonstrate AskGNN's superior effectiveness in graph task performance, opening new avenues for applying LLMs to graph-structured data without extensive fine-tuning.

cs.LG↗

arXiv2024

Reducing and Exploiting Data Augmentation Noise through Meta Reweighting Contrastive Learning for Text Classification

Data augmentation has shown its effectiveness in resolving the data-hungry problem and improving model's generalization ability. However, the quality of augmented data can be varied, especially compared with the raw/original data. To boost deep learning models' performance given augmented data/samples in text classification tasks, we propose a novel framework, which leverages both meta learning and contrastive learning techniques as parts of our design for reweighting the augmented samples and refining their feature representations based on their quality. As part of the framework, we propose novel weight-dependent enqueue and dequeue algorithms to utilize augmented samples' weight/quality information effectively. Through experiments, we show that our framework can reasonably cooperate with existing deep learning models (e.g., RoBERTa-base and Text-CNN) and augmentation techniques (e.g., Wordnet and Easydata) for specific supervised learning tasks. Experiment results show that our framework achieves an average of 1.6%, up to 4.3% absolute improvement on Text-CNN encoders and an average of 1.4%, up to 4.4% absolute improvement on RoBERTa-base encoders on seven GLUE benchmark datasets compared with the best baseline. We present an indepth analysis of our framework design, revealing the non-trivial contributions of our network components. Our code is publicly available for better reproducibility.

cs.CL↗

arXiv2024

Empowering Large Language Models for Textual Data Augmentation

With the capabilities of understanding and executing natural language instructions, Large language models (LLMs) can potentially act as a powerful tool for textual data augmentation. However, the quality of augmented data depends heavily on the augmentation instructions provided, and the effectiveness can fluctuate across different downstream tasks. While manually crafting and selecting instructions can offer some improvement, this approach faces scalability and consistency issues in practice due to the diversity of downstream tasks. In this work, we address these limitations by proposing a new solution, which can automatically generate a large pool of augmentation instructions and select the most suitable task-informed instructions, thereby empowering LLMs to create high-quality augmented data for different downstream tasks. Empirically, the proposed approach consistently generates augmented data with better quality compared to non-LLM and LLM-based data augmentation methods, leading to the best performance on 26 few-shot learning tasks sourced from a wide range of application domains.

cs.CL↗

arXiv2024

Efficient Reinforcement Learning of Task Planners for Robotic Palletization through Iterative Action Masking Learning

The development of robotic systems for palletization in logistics scenarios is of paramount importance, addressing critical efficiency and precision demands in supply chain management. This paper investigates the application of Reinforcement Learning (RL) in enhancing task planning for such robotic systems. Confronted with the substantial challenge of a vast action space, which is a significant impediment to efficiently apply out-of-the-shelf RL methods, our study introduces a novel method of utilizing supervised learning to iteratively prune and manage the action space effectively. By reducing the complexity of the action space, our approach not only accelerates the learning phase but also ensures the effectiveness and reliability of the task planning in robotic palletization. The experimental results underscore the efficacy of this method, highlighting its potential in improving the performance of RL applications in complex and high-dimensional environments like logistics palletization.

cs.RO↗

arXiv2024

DBPF: A Framework for Efficient and Robust Dynamic Bin-Picking

Efficiency and reliability are critical in robotic bin-picking as they directly impact the productivity of automated industrial processes. However, traditional approaches, demanding static objects and fixed collisions, lead to deployment limitations, operational inefficiencies, and process unreliability. This paper introduces a Dynamic Bin-Picking Framework (DBPF) that challenges traditional static assumptions. The DBPF endows the robot with the reactivity to pick multiple moving arbitrary objects while avoiding dynamic obstacles, such as the moving bin. Combined with scene-level pose generation, the proposed pose selection metric leverages the Tendency-Aware Manipulability Network optimizing suction pose determination. Heuristic task-specific designs like velocity-matching, dynamic obstacle avoidance, and the resight policy, enhance the picking success rate and reliability. Empirical experiments demonstrate the importance of these components. Our method achieves an average 84% success rate, surpassing the 60% of the most comparable baseline, crucially, with zero collisions. Further evaluations under diverse dynamic scenarios showcase DBPF's robust performance in dynamic bin-picking. Results suggest that our framework offers a promising solution for efficient and reliable robotic bin-picking under dynamics.

cs.RO↗

arXiv2024

MEND: Meta dEmonstratioN Distillation for Efficient and Effective In-Context Learning

Large Language models (LLMs) have demonstrated impressive in-context learning (ICL) capabilities, where a LLM makes predictions for a given test input together with a few input-output pairs (demonstrations). Nevertheless, the inclusion of demonstrations leads to a quadratic increase in the computational overhead of the self-attention mechanism. Existing solutions attempt to distill lengthy demonstrations into compact vectors. However, they often require task-specific retraining or compromise LLM's in-context learning performance. To mitigate these challenges, we present Meta dEmonstratioN Distillation (MEND), where a language model learns to distill any lengthy demonstrations into vectors without retraining for a new downstream task. We exploit the knowledge distillation to enhance alignment between MEND and LLM, achieving both efficiency and effectiveness simultaneously. MEND is endowed with the meta-knowledge of distilling demonstrations through a two-stage training process, which includes meta-distillation pretraining and fine-tuning. Comprehensive evaluations across seven diverse ICL task partitions using decoder-only (GPT-2) and encoder-decoder (T5) attest to MEND's prowess. It not only matches but often outperforms the Vanilla ICL as well as other state-of-the-art distillation models, while significantly reducing the computational demands. This innovation promises enhanced scalability and efficiency for the practical deployment of large language models

cs.CL↗

arXiv2023

GRENADE: Graph-Centric Language Model for Self-Supervised Representation Learning on Text-Attributed Graphs

Self-supervised representation learning on text-attributed graphs, which aims to create expressive and generalizable representations for various downstream tasks, has received increasing research attention lately. However, existing methods either struggle to capture the full extent of structural context information or rely on task-specific training labels, which largely hampers their effectiveness and generalizability in practice. To solve the problem of self-supervised representation learning on text-attributed graphs, we develop a novel Graph-Centric Language model -- GRENADE. Specifically, GRENADE exploits the synergistic effect of both pre-trained language model and graph neural network by optimizing with two specialized self-supervised learning algorithms: graph-centric contrastive learning and graph-centric knowledge alignment. The proposed graph-centric self-supervised learning algorithms effectively help GRENADE to capture informative textual semantics as well as structural context information on text-attributed graphs. Through extensive experiments, GRENADE shows its superiority over state-of-the-art methods. Implementation is available at \url{https://github.com/bigheiniu/GRENADE}.

cs.CL↗

arXiv2023

Two-Stage Grasping: A New Bin Picking Framework for Small Objects

This paper proposes a novel bin picking framework, two-stage grasping, aiming at precise grasping of cluttered small objects. Object density estimation and rough grasping are conducted in the first stage. Fine segmentation, detection, grasping, and pushing are performed in the second stage. A small object bin picking system has been realized to exhibit the concept of two-stage grasping. Experiments have shown the effectiveness of the proposed framework. Unlike traditional bin picking methods focusing on vision-based grasping planning using classic frameworks, the challenges of picking cluttered small objects can be solved by the proposed new framework with simple vision detection and planning.

cs.RO↗

arXiv2023

KEPLET: Knowledge-Enhanced Pretrained Language Model with Topic Entity Awareness

In recent years, Pre-trained Language Models (PLMs) have shown their superiority by pre-training on unstructured text corpus and then fine-tuning on downstream tasks. On entity-rich textual resources like Wikipedia, Knowledge-Enhanced PLMs (KEPLMs) incorporate the interactions between tokens and mentioned entities in pre-training, and are thus more effective on entity-centric tasks such as entity linking and relation classification. Although exploiting Wikipedia's rich structures to some extent, conventional KEPLMs still neglect a unique layout of the corpus where each Wikipedia page is around a topic entity (identified by the page URL and shown in the page title). In this paper, we demonstrate that KEPLMs without incorporating the topic entities will lead to insufficient entity interaction and biased (relation) word semantics. We thus propose KEPLET, a novel Knowledge-Enhanced Pre-trained LanguagE model with Topic entity awareness. In an end-to-end manner, KEPLET identifies where to add the topic entity's information in a Wikipedia sentence, fuses such information into token and mentioned entities representations, and supervises the network learning, through which it takes topic entities back into consideration. Experiments demonstrated the generality and superiority of KEPLET which was applied to two representative KEPLMs, achieving significant improvements on four entity-centric tasks.

cs.CL↗

arXiv2023

Communication Efficient Curvature Aided Primal-dual Algorithms for Decentralized Optimization

This paper presents a family of algorithms for decentralized convex composite problems. We consider the setting of a network of agents that cooperatively minimize a global objective function composed of a sum of local functions plus a regularizer. Through the use of intermediate consensus variables, we remove the need for inner communication loops between agents when computing curvature-guided updates. A general scheme is presented which unifies the analysis for a plethora of computing choices, including gradient descent, Newton updates, and BFGS updates. Our analysis establishes sublinear convergence rates under convex objective functions with Lipschitz continuous gradients, as well as linear convergence rates when the local functions are further assumed to be strongly convex. Moreover, we explicitly characterize the acceleration due to curvature information. Last but not the least, we present an asynchronous implementation for the proposed algorithms, which removes the need for a central clock, with linear convergence rates established in expectation under strongly convex objectives. We ascertain the effectiveness of the proposed methods with numerical experiments on benchmark datasets.

math.OC↗

arXiv2022

Distributed Primal-dual Optimization for Heterogeneous Multi-agent Systems

Heterogeneous networks comprise agents with varying capabilities in terms of computation, storage, and communication. In such settings, it is crucial to factor in the operating characteristics in allowing agents to choose appropriate updating schemes, so as to better distribute computational tasks and utilize the network more efficiently. We consider the multi-agent optimization problem of cooperatively minimizing the sum of local strongly convex objectives. We propose an asynchronous distributed primal-dual protocol, which allows for the primal update steps to be agent-dependent (an agent can opt between first-order or Newton updates). Our analysis introduces a unifying framework for such hybrid optimization scheme and establishes global linear convergence in expectation, under strongly convex objectives and general agent activation schemes. Numerical experiments on real life datasets attest to the merits of the proposed algorithm.

math.OC↗

arXiv2022

Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for Robotic Bin Picking

In this paper, we propose an iterative self-training framework for sim-to-real 6D object pose estimation to facilitate cost-effective robotic grasping. Given a bin-picking scenario, we establish a photo-realistic simulator to synthesize abundant virtual data, and use this to train an initial pose estimation network. This network then takes the role of a teacher model, which generates pose predictions for unlabeled real data. With these predictions, we further design a comprehensive adaptive selection scheme to distinguish reliable results, and leverage them as pseudo labels to update a student model for pose estimation on real data. To continuously improve the quality of pseudo labels, we iterate the above steps by taking the trained student model as a new teacher and re-label real data using the refined teacher model. We evaluate our method on a public benchmark and our newly-released dataset, achieving an ADD(-S) improvement of 11.49% and 22.62% respectively. Our method is also able to improve robotic bin-picking success by 19.54%, demonstrating the potential of iterative sim-to-real solutions for robotic applications.

cs.RO↗

arXiv2022

FedADMM: A Robust Federated Deep Learning Framework with Adaptivity to System Heterogeneity

Federated Learning (FL) is an emerging framework for distributed processing of large data volumes by edge devices subject to limited communication bandwidths, heterogeneity in data distributions and computational resources, as well as privacy considerations. In this paper, we introduce a new FL protocol termed FedADMM based on primal-dual optimization. The proposed method leverages dual variables to tackle statistical heterogeneity, and accommodates system heterogeneity by tolerating variable amount of work performed by clients. FedADMM maintains identical communication costs per round as FedAvg/Prox, and generalizes them via the augmented Lagrangian. A convergence proof is established for nonconvex objectives, under no restrictions in terms of data dissimilarity or number of participants per round of the algorithm. We demonstrate the merits through extensive experiments on real datasets, under both IID and non-IID data distributions across clients. FedADMM consistently outperforms all baseline methods in terms of communication efficiency, with the number of rounds needed to reach a prescribed accuracy reduced by up to 87%. The algorithm effectively adapts to heterogeneous data distributions through the use of dual variables, without the need for hyperparameter tuning, and its advantages are more pronounced in large-scale systems.

cs.LG↗

arXiv2022

A Communication Efficient Quasi-Newton Method for Large-scale Distributed Multi-agent Optimization

We propose a communication efficient quasi-Newton method for large-scale multi-agent convex composite optimization. We assume the setting of a network of agents that cooperatively solve a global minimization problem with strongly convex local cost functions augmented with a non-smooth convex regularizer. By introducing consensus variables, we obtain a block-diagonal Hessian and thus eliminate the need for additional communication when approximating the objective curvature information. Moreover, we reduce computational costs of existing primal-dual quasi-Newton methods from $\mathcal{O}(d^3)$ to $\mathcal{O}(cd)$ by storing $c$ pairs of vectors of dimension $d$. An asynchronous implementation is presented that removes the need for coordination. Global linear convergence rate in expectation is established, and we demonstrate the merit of our algorithm numerically with real datasets.

math.OC↗

arXiv2021

Analogue Radio Over Fiber for Next-Generation RAN: Challenges and Opportunities

The radio access network (RAN) connects the users to the core networks, where typically digitised radio over fiber (D-RoF) links are employed. The data rate of the RAN is limited by the hardware constraints of the D-RoF-based backhaul and fronthaul. In order to break this bottleneck, the potential of the analogue radio over fiber (A-RoF) based RAN techniques are critically appraised for employment in the next-generation systems, where increased-rate massive multiple-input-multiple-output (massive-MIMO) and millimeter wave (mmWave) techniques will be implemented. We demonstrate that huge bandwidth and power-consumption cost benefits may accrue upon using A-RoF for next-generation RANs. We provide an overview of the recent A-RoF research and a performance comparison of A-RoF and D-RoF, concluding with further insights on the future potential of A-RoF.

eess.SP↗

arXiv2021

Analogue Radio over Fiber aided Multi-service Communications for High Speed Trains

High speed trains (HST) have gradually become an essential means of transportation, where given our digital world, it is expected that passengers will be connected all the time. More specifically, the on-board passengers require fast mobile connections, which cannot be provided by the currently implemented cellular networks. Hence, in this article, we propose an analogue radio over fiber (A-RoF) aided multi-service network architecture for high-speed trains, in order to enhance the quality of service as well as reduce the cost of the radio access network (RAN). The proposed design can simultaneously support sub- 6GHz as well as milimeter wave (mmWave) communications using the same architecture. Explicitly, we design a photonics aided beamforming technique in order to eliminate the bulky high-speed electronic phase-shifters and the hostile broadband mmWave mixers while providing a low-cost RAN solution. Finally, a beamforming range of 180 is demonstrated with a high resolution using our proposed system.

eess.SP↗