The task of converting monolithic programs to mi-croservices architecture is complex, hindered by the intertwined nature of program control and data flows. Conventional methods for microservice extraction often fall short in capturing the essen-tial connections within a monolithic structure and in propagating properties to distant neighbors for effective clustering. To address these issues, we introduce an innovative graph-based deep clustering technique that utilizes both control flow and data flow graphs. This approach offers a thorough analysis of class interactions within monolithic applications, facilitating accurate identification and extraction of microservices. Furthermore, we present the Microservice Extraction Graph Neural Network (MEGNN), an advanced graph attention network designed to enhance message transmission depth and enable nodes to assimilate features from k-hop neighbors. This method extends the reach of message distribution across node chains and mitigates the issue of feature homogenization, leading to more cohesive clustering of related nodes and improving the quality of microservices extraction. Experimental evaluations on data from three publicly accessible Java monolithic programs confirm that our proposed method surpasses existing techniques in microservices extraction efficacy.

Damai Dai, Chengqi Deng, Chenggang Zhao, R.x. Xu, Huazuo Gao, Deli Chen, Jiashi Li, Wangding Zeng, Xingkai Yu, Y. Wu, Zhenda Xie, Y.k. Li, Panpan Huang, Fuli Luo, Chong Ruan, Zhifang Sui, Wenfeng Liang. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Peiyi Wang, Lei Li, Zhihong Shao, Runxin Xu, Damai Dai, Yifei Li, Deli Chen, Yu Wu, Zhifang Sui. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Parameter-efficient fine-tuning (PEFT) is crucial for customizing Large Language Models (LLMs) with constrained resources.Although there have been various PEFT methods for dense-architecture LLMs, PEFT for sparsearchitecture LLMs is still underexplored.In this work, we study the PEFT method for LLMs with the Mixture-of-Experts (MoE) architecture and the contents of this work are mainly threefold: (1) We investigate the dispersion degree of the activated experts in customized tasks, and found that the routing distribution for a specific task tends to be highly concentrated, while the distribution of activated experts varies significantly across different tasks.(2) We propose Expert-Specialized Fine-Tuning, or ESFT, which tunes the experts most relevant to downstream tasks while freezing the other experts and modules; experimental results demonstrate that our method not only improves the tuning efficiency, but also matches or even surpasses the performance of fullparameter fine-tuning.(3) We further analyze the impact of the MoE architecture on expertspecialized fine-tuning.We find that MoE models with finer-grained experts are more advantageous in selecting the combination of experts that are most relevant to downstream tasks, thereby enhancing both the training efficiency and effectiveness.Our code is available at https://github.com/deepseek-ai/ESFT.

As large language models (LLMs) generate texts with increasing fluency and realism, there is a growing need to identify the source of texts to prevent the abuse of LLMs. Text watermarking techniques have proven reliable in distinguishing whether a text is generated by LLMs by injecting hidden patterns. However, we argue that existing LLM watermarking methods are encoding-inefficient and cannot flexibly meet the diverse information encoding needs (such as encoding model version, generation time, user id, etc.). In this work, we conduct the first systematic study on the topic of **Codable Text Watermarking for LLMs** (CTWL) that allows text watermarks to carry multi-bit customizable information. First of all, we study the taxonomy of LLM watermarking technologies and give a mathematical formulation for CTWL. Additionally, we provide a comprehensive evaluation system for CTWL: (1) watermarking success rate, (2) robustness against various corruptions, (3) coding rate of payload information, (4) encoding and decoding efficiency, (5) impacts on the quality of the generated text. To meet the requirements of these non-Pareto-improving metrics, we follow the most prominent vocabulary partition-based watermarking direction, and devise an advanced CTWL method named **Balance-Marking**. The core idea of our method is to use a proxy language model to split the vocabulary into probability-balanced parts, thereby effectively maintaining the quality of the watermarked text. Our code is available at https://github.com/lancopku/codable-watermarking-for-llm.

Wheat (Triticum spp.) is widely grown and receives large amounts of nitrogen fertiliser. Grain protein content (GPC) is key to its nutritional and economic value and is influenced by genetic, management and environmental factors. Accurate pre-harvest estimation of GPC patterns could improve N efficiency and profit. Some plant traits with physiological links to GPC have been identified, but assessment of traits retrievable from satellite imagery could advance GPC estimation at commercial scales. By radiative transfer model inversion of Sentinel-2 imagery, we retrieved chlorophyll (Ca+b), leaf dry matter (Cm), leaf water content (Cw) and leaf area index (LAI) for > 6,000 hectares of commercial wheat across two years. Farmers supplied ~90,000 GPC data points collected during harvest. Using a gradient boosted machine, we predicted GPC (R2= 0.86, RMSE = 0.56) and found that Cwexplained most GPC variability under stress conditions but that contributions to GPC were distributed among plant traits in milder conditions.

In-context learning (ICL) emerges as a promising capability of large language models (LLMs) by providing them with demonstration examples to perform diverse tasks.However, the underlying mechanism of how LLMs learn from the provided context remains under-explored.In this paper, we investigate the working mechanism of ICL through an information flow lens.Our findings reveal that label words in the demonstration examples function as anchors:(1) semantic information aggregates into label word representations during the shallow computation layers' processing; (2) the consolidated information in label words serves as a reference for LLMs' final predictions.Based on these insights, we introduce an anchor re-weighting method to improve ICL performance, a demonstration compression technique to expedite inference, and an analysis framework for diagnosing ICL errors in GPT2-XL.The promising applications of our findings again validate the uncovered ICL working mechanism and pave the way for future studies. 1

Federated learning algorithms enable neural network models to be trained across multiple decentralized edge devices without sharing private data. However, they are susceptible to backdoor attacks launched by malicious clients. Existing robust federated aggregation algorithms heuristically detect and exclude suspicious clients based on their parameter distances, but they are ineffective on Natural Language Processing (NLP) tasks. The main reason is that, although text backdoor patterns are obvious at the underlying dataset level, they are usually hidden at the parameter level, since injecting backdoors into texts with discrete feature space has less impact on the statistics of the model parameters. To settle this issue, we propose to identify backdoor clients by explicitly modeling the data divergence among clients in federated NLP systems. Through theoretical analysis, we derive the f-divergence indicator to estimate the client data divergence with aggregation updates and Hessians. Furthermore, we devise a dataset synthesization method with a Hessian reassignment mechanism guided by the diffusion theory to address the key challenge of inaccessible datasets in calculating clients' data Hessians. We then present the novel Federated F-Divergence-Based Aggregation~(\textbf{Fed-FA}) algorithm, which leverages the f-divergence indicator to detect and discard suspicious clients. Extensive empirical results show that Fed-FA outperforms all the parameter distance-based methods in defending against backdoor attacks among various natural language backdoor attack scenarios.

Graph Contrastive Learning (GCL) has proven highly effective in promoting the performance of Semi-Supervised Node Classification (SSNC). However, existing GCL methods are generally transferred from other fields like CV or NLP, whose underlying working mechanism remains underexplored. In this work, we first deeply probe the working mechanism of GCL in SSNC, and find that the promotion brought by GCL is severely unevenly distributed: the improvement mainly comes from subgraphs with less annotated information, which is fundamentally different from contrastive learning in other fields. However, existing GCL methods generally ignore this uneven distribution of annotated information and apply GCL evenly to the whole graph. To remedy this issue and further improve GCL in SSNC, we propose the Topology InFormation gain-Aware Graph Contrastive Learning (TIFA-GCL) framework that considers the annotated information distribution across graph in GCL. Extensive experiments on six benchmark graph datasets, including the enormous OGB-Products graph, show that TIFA-GCL can bring a larger improvement than existing GCL methods in both transductive and inductive settings. Further experiments demonstrate the generalizability and interpretability of TIFA-GCL.

The class imbalance problem, as an important issue in learning node representations, has drawn increasing attention from the community. Although the imbalance considered by existing studies roots from the unequal quantity of labeled examples in different classes (quantity imbalance), we argue that graph data expose a unique source of imbalance from the asymmetric topological properties of the labeled nodes, i.e., labeled nodes are not equal in terms of their structural role in the graph (topology imbalance). In this work, we first probe the previously unknown topology-imbalance issue, including its characteristics, causes, and threats to semisupervised node classification learning. We then provide a unified view to jointly analyzing the quantity- and topology- imbalance issues by considering the node influence shift phenomenon with the Label Propagation algorithm. In light of our analysis, we devise an influence conflict detection–based metric Totoro to measure the degree of graph topology imbalance and propose a model-agnostic method ReNode to address the topology-imbalance issue by re-weighting the influence of labeled nodes adaptively based on their relative positions to class boundaries. Systematic experiments demonstrate the effectiveness and generalizability of our method in relieving topology-imbalance issue and promoting semi-supervised node classification. The further analysis unveils varied sensitivity of different graph neural networks (GNNs) to topology imbalance, which may serve as a new perspective in evaluating GNN architectures.

Graph Neural Networks (GNNs) have achieved promising performance on a wide range of graph-based tasks. Despite their success, one severe limitation of GNNs is the over-smoothing issue (indistinguishable representations of nodes in different classes). In this work, we present a systematic and quantitative study on the over-smoothing issue of GNNs. First, we introduce two quantitative metrics, MAD and MADGap, to measure the smoothness and over-smoothness of the graph nodes representations, respectively. Then, we verify that smoothing is the nature of GNNs and the critical factor leading to over-smoothness is the low information-to-noise ratio of the message received by the nodes, which is partially determined by the graph topology. Finally, we propose two methods to alleviate the over-smoothing issue from the topological view: (1) MADReg which adds a MADGap-based regularizer to the training objective; (2) AdaEdge which optimizes the graph topology based on the model predictions. Extensive experiments on 7 widely-used graph datasets with 10 typical GNN models show that the two proposed methods are effective for relieving the over-smoothing issue, thus improving the performance of various GNN models.

Stock movement prediction is a hot topic in the Fintech area. Previous works usually predict the price movement in a daily basis, although the market impact of news can be absorbed much shorter, and the exact time is hard to estimate. In this work, we propose a more practical objective to predict the overnight stock movement between the previous close price and the open price. As no trading operation occurs after market close, the market impact of overnight news will be reflected by the overnight movement. One big obstacle for such task is the lacking of data, in this work we collect and publish the overnight stock price movement dataset of Reuters Financial News. Another challenge is that the stocks in the market are not independent, which is omitted by previous works. To make use of the connection among stocks, we propose a LSTM Relational Graph Convolutional Network (LSTM-RGCN) model, which models the connection among stocks with their correlation matrix. Extensive experiment results show that our model outperforms the baseline models. Further analysis shows that the introduction of the graph enables our model to predict the movement of stocks that are not directly associated with news as well as the whole market, which is not available in most previous methods.

Integrated nutrient management is important for sustainable agricultural production and protecting environment quality and has been widely investigated around the world. In this article the spatial variability of soil nutrients was investigated and a regionalized nutrient management system was developed using geostatistics and geographic information system technologies. A total of 511 GPS-referenced soil samples were taken in Yongji County, Shanxi province, China, and analysed for major soil nutrients: soil total nitrogen (TN), Olsen extractable phosphorus (OLSENP) and extractable potassium (EXTK). Low concentrations of nitrogen (N) and phosphorus (P) were found and they are likely to be the main limiting nutrients for crop growth in this county. Within the county moderate spatial dependence was found for all three soil variables, but at different spatial scales. The spatial distributions of TN, OLSENP and EXTK were estimated by using kriging interpolation. The cropped areas of the county were divided into fertilizer management categories consisting of four classes of TN, three classes of OLSENP and two classes of EXTK. For the targeted crop yields, regionalized fertilization maps of N, P and K in the county were produced using geographic information system. In 3-year field verification trials in two villages the crop yields of the wheat–maize rotation system increased by 10–20%, and farmers' cash income increased by 1550–2610 RMB ha−1 year−1 where regional fertilization recommendations were implemented, in comparison with traditional farmers' practices. The regionalized maps are a practical alternative to site-specific soil nutrient management approaches in areas where it is not practical, because of small farm size or other constraints, to use intensive soil sampling and chemical analyses.