In recent years, table reasoning has garnered substantial research interest, particularly regarding its integration with Large Language Models (LLMs), which have revolutionized natural language applications. Existing LLM-based studies typically achieve step-by-step thinking for table reasoning guided by task semantics. While these approaches emphasize autonomous exploration and enhance fine-grained table understanding, they often overlook systematic thinking in the reasoning process. This oversight can lead to omitted steps, disorganized logic and misleading results, especially in complex scenarios. In this paper, we proposePoTable, a novel stage-oriented plan-then-execute approach that incorporates systematic thinking into table reasoning. Specifically,PoTableinvolves several distinct analytical stages with clear objectives to provide adequate guidance. To accomplish stage-specific goals,PoTableemploys a plan-then-execute mechanism: it first plans the operation chain based on the stage objective, and then executes operations sequentially through code generation, real-time running and feedback processing. Consequently,PoTableproduces reliable table reasoning results with highly accurate, step-wise commented and completely executable programs. It mirrors the workflow of a professional data analyst, offering advantages in both accuracy and explainability. Finally, we conduct extensive experiments on four datasets from the WikiTQ and TabFact benchmarks, where the results demonstrate the effectiveness, efficiency and explainability ofPoTable. Our code is available at:https://github.com/Double680/PoTable.

Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by retrieving relevant document from external knowledge sources. By referencing this external knowledge, RAG effectively reduces the generation of factually incorrect content and addresses hallucination issues within LLMs. Recently, there has been growing attention to improving the performance and efficiency of RAG systems from various perspectives. While these advancements have yielded significant results, the application of RAG in domains with considerable societal implications raises a critical question about fairness: What impact does the introduction of the RAG paradigm have on the fairness of LLMs? To address this question, we conduct extensive experiments by varying the LLMs, retrievers, and retrieval sources. Our experimental analysis reveals that the scale of the LLMs plays a significant role in influencing fairness outcomes within the RAG framework. When the model scale is smaller than 8B, the integration of retrieval mechanisms often exacerbates unfairness in small-scale LLMs (e.g., LLaMA3.2-1B, Mistral-7B, and LLaMA3-8B). To mitigate the fairness issues introduced by RAG for small-scale LLMs, we propose two approaches, FairFT and FairFilter. Specifically, in FairFT, we align the retriever with the LLM in terms of fairness, enabling it to retrieve documents that facilitate fairer model outputs. In FairFilter, we propose a fairness filtering mechanism to filter out biased content after retrieval. Finally, we validate our proposed approaches on real-world datasets, demonstrating their effectiveness in improving fairness while maintaining performance.

User modeling serves as a crucial foundation for researchers to capture useful potential characteristics, playing a pivotal role in various applications such as recommender systems. One common challenge in user modeling is the cold-start problem, where interactions are notably limited for new users. To tackle this issue, the paradigm of meta-learning has been introduced to user modeling, yielding promising results. Similar to a guidebook for a new traveler, meta-learning significantly influences decision-making for new users in critical scenarios, such as career recommendations. Consequently, the issue of fairness in meta-learning has become paramount. Several methods have been proposed to mitigate unfairness in meta-learning and have shown promising results. However, a fundamental question remains unexplored: What is the critical factor leading to unfairness in meta-learned user modeling? Through theoretical analysis that integrates the meta-learning paradigm with group fairness metrics, we identify group proportion imbalance as a critical factor. Subsequently, another question arises: How can we mitigate the influence of this factor to enhance fairness while ensuring accuracy? To this end, we introduce a novel F airness-aware A daptive S ampling framework for me T a-learning, abbreviated as FAST. Its core concept involves adaptively adjusting the sampling distribution for different user groups during the interleaved training process of meta-learning. Moreover, we provide theoretical guarantees demonstrating the convergence of FAST, showcasing its potential to effectively eliminate unfairness. Furthermore, to ensure model accuracy, we enhance FAST with FAST+ by introducing a hybrid sampling strategy at an individual level. This strategy prioritizes fairness and thoroughly explores important users during the sampling process, allowing for a better accuracy-fairness tradeoff. Finally, we conduct extensive experiments on real-world datasets, which demonstrate the effectiveness of both FAST and FAST+ frameworks.

Programming Knowledge Tracking (PKT) aims to dynamically diagnose learners' mastery levels of programming knowledge based on their coding activities, facilitating more effective and personalized programming education. However, current PKT studies primarily focus on the implicit relationship between code content and knowledge assessment, often overlooking two types of noise signals in long-term programming activities: unwanted signals from unrelated submissions and weak signals from minor modifications. This practical challenge significantly limits model performance and application. To address this issue, we propose Coda, a Code graph-based tuning adaptor designed to enhance existing PKT models by identifying and mitigating the impact of noise. Specifically, Coda first transforms the loose code sequences submitted by each learner into a compact code graph. By leveraging this code graph, unwanted signals can be identified from a semantic similarity perspective. We then apply a cluster-aware GCN to the code graph, which improves the discrimination of weak signals and enables their clustering for identification. Finally, a lightweight yet effective adaptor is incorporated into the PKT task through optimization with two noise feature-based constraints and a navigational regularization term, to correct knowledge states affected by noise. It is worth mentioning that the Coda framework is model-agnostic and can be adapted to most existing PKT solutions. Extensive experimental results on four real-world datasets demonstrate that Coda effectively performs the PKT task in the presence of noisy programming records, outperforming typical baselines.

In the pursuit of enhancing software reusability and developer productivity, code search has emerged as a key area, aimed at retrieving code snippets relevant to functionalities based on natural language queries. Despite significant progress in self-supervised code pre-training utilizing the vast amount of code data in repositories, existing methods have primarily focused on leveraging contrastive learning to align natural language with function-level code snippets. These studies have overlooked the abundance of fine-grained (such as block-level and statement-level) code snippets prevalent within the function-level code snippets, which results in suboptimal performance across all levels of granularity. To address this problem, we first construct a multi-granularity code search dataset called MGCodeSearchNet, which contains 536K+ pairs of natural language and code snippets. Subsequently, we introduce a novel Multi-Granularity Self-Supervised contrastive learning code Search framework (MGS3). First, MGS3 features a Hierarchical Multi-Granularity Representation module (HMGR), which leverages syntactic structural relationships for hierarchical representation and aggregates fine-grained information into coarser-grained representations. Then, during the contrastive learning phase, we endeavor to construct positive samples of the same granularity for fine-grained code, and introduce in-function negative samples for fine-grained code. Finally, we conduct extensive experiments on code search benchmarks across various granularities, demonstrating that the framework exhibits outstanding performance in code search tasks of multiple granularities. These experiments also showcase its model-agnostic nature and compatibility with existing pre-trained code representation models.

An intelligent code search engine tries to find and suggest a code piece given a developer's query quickly from a large-scale program database, which can significantly promote software development efficiency. Existing solutions can search the relevant codes to some extent. However, most of them fail to precisely understand the search intent of developers since they only mine their natural language queries, while ignoring the valuable programming context (e.g., the code written by the developer). In this paper, we study the novel problem of context-aware code search. To promote a step forward, we first provide the CodeSearchNet-C dataset with constructing sufficient programming context from the GitHub website for each query-code instance. The dataset is supplemented on the CodeSearchNet benchmark, ensuring both generality and comparability for relevant research. Then, by analyzing the characteristics of programming context, we propose a novel two-stage Context-aware Code Retrieval (ConCR) framework. In the first stage, we propose a Context Walking algorithm, which simulates the programming habits of different developers. The generated programming context could ensure the diversity of search intent among developers. In the second stage, imitating the reading habits of developers, we introduce a novel Context Hierarchical Encoder, to understand the search intent with contextual information from local to global. Our ConCR framework is general, and we give three implementations on the basis of typical code search models as backbones. Extensive experimental results clearly prove that our ConCR significantly enhances the code search performance, effectively fulfilling developers' needs for efficient code resource searching on the web. These results also verify the necessity of introducing programming context to understand developers' intent.

In digital education, Cognitive Diagnosis (CD) is essential for modeling learners' cognitive states, such as problem-solving ability and knowledge proficiency, by analyzing their response data, like answer correctness. However, traditional CD methods struggle with effectiveness and efficiency. They fail to capture the diversity and uncertainty of learners' cognitive states. Additionally, response prediction can be time-consuming. To address these issues, we propose BoxCD, a contrastive probabilistic box embedding model for cognitive diagnosis. BoxCD utilizes high-dimensional axis-aligned hyper-rectangles (boxes) to represent learners and exercises, with the volume of intersecting boxes used to predict learners' responses. This approach effectively captures semantic diversity and uncertainty while enhancing diagnostic effectiveness. To stabilize box embeddings, we integrate contrastive learning objectives with response prediction goals, optimizing the distance between positive and negative samples of learner and exercise boxes to improve uniformity. Additionally, we develop a rank-based response prediction method that leverages the geometric properties of box embeddings to assess learners' response correctness efficiently. Comprehensive experiments on two real-world datasets demonstrate that BoxCD outperforms traditional CD models in effectiveness and efficiency. This showcases its potential to enhance personalized learning in digital education platforms.

To alleviate the long-standing data sparsity issue in recommender systems, numerous studies in cross-domain recommendation (CDR) have been conducted to facilitate information transfer processes across domains. In recent years, dual-target CDR has been introduced to gain mutual improvements between two domains through more general bidirectional transfer rather than traditional one-way transit. Existing methods in dual-target CDR focus primarily on designing powerful encoders to learn representative cross-domain information, without tackling the fundamental issue of interaction data shortage. In this paper, we present CrossAug, a novel data augmentation approach to leverage interactions more efficiently in two domains. Specifically, we propose intra-domain and inter-domain augmentations based on cross-reconstructed representations in terms of sampled records. To reduce the harm of domain shift, we project domain-shared representations in two domains into a joint space with Householder transformations and apply center alignments. All these modules boost the utilization of interactions with little influence from negative transfer. Extensive experimental results over public datasets demonstrate the effectiveness of CrossAug and its components in dual-target CDR.

Meta-learning has been widely employed to tackle the cold-start problem in user modeling. Similar to a guidebook for a new traveler, meta-learning significantly affects decision-making for new users in crucial scenarios, such as career recommendations. Consequently, the issue of fairness in meta-learning has gained paramount importance. Several methods have been proposed to mitigate unfairness in meta-learning and have shown promising results. However, a fundamental question remains unexplored: What is the critical factor leading to unfairness in meta-learned user modeling? Through the theoretical analysis that integrates the meta-learning paradigm with group fairness metrics, we identify group proportion imbalance as a critical factor. Subsequently, in order to mitigate the impact of this factor, we introduce a novel Fairness-aware Adaptive Sampling framework for meTa-learning, abbreviated as FAST. Its core concept involves adaptively adjusting the sampling distribution for different user groups during the interleaved training process of meta-learning. Furthermore, we provide theoretical guarantees demonstrating the convergence of FAST. Finally, empirical experiments conducted on three datasets reveal that FAST effectively enhances fairness while maintaining high accuracy. The code for FAST is available at https://github.com/zhengz99/FAST.

Cognitive diagnosis (CD) aims to reveal the proficiency of students on specific knowledge concepts and traits of test exercises (e.g., difficulty). It plays a critical role in intelligent education systems by supporting personalized learning guidance. However, recent developments in CD mostly concentrate on improving the accuracy of diagnostic results and often overlook the important and practical task: domain-level zero-shot cognitive diagnosis (DZCD). The primary challenge of DZCD is the deficiency of student behavior data in the target domain due to the absence of student-exercise interactions or unavailability of exercising records for training purposes. To tackle the cold-start issue, we propose a two-stage solution named TechCD (Transferable knowledgE Concept grapH embedding framework for Cognitive Diagnosis). The fundamental notion involves utilizing a pedagogical knowledge concept graph (KCG) as a mediator to connect disparate domains, allowing the transmission of student cognitive signals from established domains to the zero-shot cold-start domain. Specifically, a naive yet effective graph convolutional network (GCN) with the bottom-layer discarding operation is initially employed over the KCG to learn transferable student cognitive states and domain-specific exercise traits. Moreover, we give three implementations of the general TechCD framework following the typical cognitive diagnosis solutions. Finally, extensive experiments on real-world datasets not only prove that Tech can effectively perform zero-shot diagnosis, but also give some popular applications such as exercise recommendation.