pwc-archive/papers-with-abstracts huggingface dataset:
XPath Agent: An Efficient XPath Programming Agent Based on LLM for Web Crawler
Authors: Yu Li, Bryce Wang, Xinyu Luan
Date: 2024-12-17 00:00:00

We present XPath Agent, a production-ready XPath programming agent specifically designed for web crawling and web GUI testing. A key feature of XPath Agent is its ability to automatically generate XPath queries from a set of sampled web pages using a single natural language query. To demonstrate its effectiveness, we benchmark XPath Agent against a state-of-the-art XPath programming agent across a range of web crawling tasks. Our results show that XPath Agent achieves comparable performance metrics while significantly reducing token usage and improving clock-time efficiency. The well-designed two-stage pipeline allows for seamless integration into existing web crawling or web GUI testing workflows, thereby saving time and effort in manual XPath query development. The source code for XPath Agent is available at https://github.com/eavae/feilian.
Level-Navi Agent: A Framework and benchmark for Chinese Web Search Agents
Authors: Chuanrui Hu, Shichong Xie, Baoxin Wang, Bin Chen, Xiaofeng Cong, Jun Zhang
Date: 2024-12-20 00:00:00

Large language models (LLMs), adopted to understand human language, drive the development of artificial intelligence (AI) web search agents. Compared to traditional search engines, LLM-powered AI search agents are capable of understanding and responding to complex queries with greater depth, enabling more accurate operations and better context recognition. However, little attention and effort has been paid to the Chinese web search, which results in that the capabilities of open-source models have not been uniformly and fairly evaluated. The difficulty lies in lacking three aspects: an unified agent framework, an accurately labeled dataset, and a suitable evaluation metric. To address these issues, we propose a general-purpose and training-free web search agent by level-aware navigation, Level-Navi Agent, accompanied by a well-annotated dataset (Web24) and a suitable evaluation metric. Level-Navi Agent can think through complex user questions and conduct searches across various levels on the internet to gather information for questions. Meanwhile, we provide a comprehensive evaluation of state-of-the-art LLMs under fair settings. To further facilitate future research, source code is available at Github.
The Synergy of Automated Pipelines with Prompt Engineering and Generative AI in Web Crawling
Authors: Chau-Jian Huang
Date: 2024-12-29 00:00:00
Tasks: Diversity, Prompt Engineering

Web crawling is a critical technique for extracting online data, yet it poses challenges due to webpage diversity and anti-scraping mechanisms. This study investigates the integration of generative AI tools Claude AI (Sonnet 3.5) and ChatGPT4.0 with prompt engineering to automate web scraping. Using two prompts, PROMPT I (general inference, tested on Yahoo News) and PROMPT II (element-specific, tested on Coupons.com), we evaluate the code quality and performance of AI-generated scripts. Claude AI consistently outperformed ChatGPT-4.0 in script quality and adaptability, as confirmed by predefined evaluation metrics, including functionality, readability, modularity, and robustness. Performance data were collected through manual testing and structured scoring by three evaluators. Visualizations further illustrate Claude AI's superiority. Anti-scraping solutions, including undetected_chromedriver, Selenium, and fake_useragent, were incorporated to enhance performance. This paper demonstrates how generative AI combined with prompt engineering can simplify and improve web scraping workflows.
ACL-rlg: A Dataset for Reading List Generation
Authors: Julien Aubert-Béduchaud, Florian Boudin, Béatrice Daille, Richard Dufour
Date: 2024-12-30 00:00:00
Tasks: Articles, Retrieval

Familiarizing oneself with a new scientific field and its existing literature can be daunting due to the large amount of available articles. Curated lists of academic references, or reading lists, compiled by experts, offer a structured way to gain a comprehensive overview of a domain or a specific scientific challenge. In this work, we introduce ACL-rlg, the largest open expert-annotated reading list dataset. We also provide multiple baselines for evaluating reading list generation and formally define it as a retrieval task. Our qualitative study highlights the fact that traditional scholarly search engines and indexing methods perform poorly on this task, and GPT-4o, despite showing better results, exhibits signs of potential data contamination.
Hgformer: Hyperbolic Graph Transformer for Recommendation
Authors: Xin Yang, Xingrun Li, Heng Chang, Jinze Yang, Xihong Yang, Shengyu Tao, Ningkang Chang, Maiko Shigeno, Junfeng Wang, Dawei Yin, Erxue Min
Date: 2024-12-30 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

The cold start problem is a challenging problem faced by most modern recommender systems. By leveraging knowledge from other domains, cross-domain recommendation can be an effective method to alleviate the cold start problem. However, the modelling distortion for long-tail data, which is widely present in recommender systems, is often overlooked in cross-domain recommendation. In this research, we propose a hyperbolic manifold based cross-domain collaborative filtering model using BiTGCF as the base model. We introduce the hyperbolic manifold and construct new propagation layer and transfer layer to address these challenges. The significant performance improvements across various datasets compared to the baseline models demonstrate the effectiveness of our proposed model.
Contrastive Learning Augmented Social Recommendations
Authors: Lin Wang, Weisong Wang, Xuanji Xiao, Qing Li
Date: 2025-01-03 00:00:00
Tasks: Contrastive Learning, Denoising, Recommendation Systems, Relation

Recommender systems play a pivotal role in modern content platforms, yet traditional behavior-based models often face challenges in addressing cold users with sparse interaction data. Engaging these users, however, remains critical for sustaining platform growth. To tackle this issue, we propose leveraging reconstructed social graph to complement interest representations derived from behavioral data. Despite the widespread availability of social graphs on content platforms, their utility is hindered by social-relation noise and inconsistencies between social and behavioral interests. To mitigate noise propagation in graph data and extract reliable social interests, we introduce a dual-view denoising framework. This approach first applies low-rank singular value decomposition (SVD) to the user-item interaction matrix, generating denoised user embeddings for reconstructing the social graph. It then employs contrastive learning to align the original and reconstructed social graphs. To address the discrepancy between social and behavioral interests, we utilize a mutual distillation mechanism that decomposes interests into four subcategories: aligned social/behavioral interests and social/behavioral-specific interests, enabling effective integration of the two. Empirical results demonstrate the efficacy of our method, particularly in improving recommendations for cold users, by combining social and behavioral data. The implementation of our approach is publicly available at https://github.com/WANGLin0126/CLSRec.
Integrating Domain Knowledge into Large Language Models for Enhanced Fashion Recommendations
Authors: Zhan Shi, Shanglin Yang
Date: 2025-01-03 00:00:00
Tasks: Few-Shot Learning, Language Modeling, Language Modelling, Large Language Model

Fashion, deeply rooted in sociocultural dynamics, evolves as individuals emulate styles popularized by influencers and iconic figures. In the quest to replicate such refined tastes using artificial intelligence, traditional fashion ensemble methods have primarily used supervised learning to imitate the decisions of style icons, which falter when faced with distribution shifts, leading to style replication discrepancies triggered by slight variations in input. Meanwhile, large language models (LLMs) have become prominent across various sectors, recognized for their user-friendly interfaces, strong conversational skills, and advanced reasoning capabilities. To address these challenges, we introduce the Fashion Large Language Model (FLLM), which employs auto-prompt generation training strategies to enhance its capacity for delivering personalized fashion advice while retaining essential domain knowledge. Additionally, by integrating a retrieval augmentation technique during inference, the model can better adjust to individual preferences. Our results show that this approach surpasses existing models in accuracy, interpretability, and few-shot learning capabilities.
Robust Uplift Modeling with Large-Scale Contexts for Real-time Marketing
Authors: Zexu Sun, Qiyu Han, Minqin Zhu, Hao Gong, Dugang Liu, Chen Ma
Date: 2025-01-04 00:00:00
Tasks: Marketing

Improving user engagement and platform revenue is crucial for online marketing platforms. Uplift modeling is proposed to solve this problem, which applies different treatments (e.g., discounts, bonus) to satisfy corresponding users. Despite progress in this field, limitations persist. Firstly, most of them focus on scenarios where only user features exist. However, in real-world scenarios, there are rich contexts available in the online platform (e.g., short videos, news), and the uplift model needs to infer an incentive for each user on the specific item, which is called real-time marketing. Thus, only considering the user features will lead to biased prediction of the responses, which may cause the cumulative error for uplift prediction. Moreover, due to the large-scale contexts, directly concatenating the context features with the user features will cause a severe distribution shift in the treatment and control groups. Secondly, capturing the interaction relationship between the user features and context features can better predict the user response. To solve the above limitations, we propose a novel model-agnostic Robust Uplift Modeling with Large-Scale Contexts (UMLC) framework for Real-time Marketing. Our UMLC includes two customized modules. 1) A response-guided context grouping module for extracting context features information and condensing value space through clusters. 2) A feature interaction module for obtaining better uplift prediction. Specifically, this module contains two parts: a user-context interaction component for better modeling the response; a treatment-feature interaction component for discovering the treatment assignment sensitive feature of each instance to better predict the uplift. Moreover, we conduct extensive experiments on a synthetic dataset and a real-world product dataset to verify the effectiveness and compatibility of our UMLC.
Sustainable Digitalization of Business with Multi-Agent RAG and LLM
Authors: Muhammad Arslan, Saba Munawar, Christophe Cruz
Date: 2025-01-06 00:00:00
Tasks: Articles, Decision Making, Information Retrieval, RAG, Retrieval, Retrieval-augmented Generation

Businesses heavily rely on data sourced from various channels like news articles, financial reports, and consumer reviews to drive their operations, enabling informed decision-making and identifying opportunities. However, traditional manual methods for data extraction are often time-consuming and resource-intensive, prompting the adoption of digital transformation initiatives to enhance efficiency. Yet, concerns persist regarding the sustainability of such initiatives and their alignment with the United Nations (UN)'s Sustainable Development Goals (SDGs). This research aims to explore the integration of Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) as a sustainable solution for Information Extraction (IE) and processing. The research methodology involves reviewing existing solutions for business decision-making, noting that many systems require training new machine learning models, which are resource-intensive and have significant environmental impacts. Instead, we propose a sustainable business solution using pre-existing LLMs that can work with diverse datasets. We link domain-specific datasets to tailor LLMs to company needs and employ a Multi-Agent architecture to divide tasks such as information retrieval, enrichment, and classification among specialized agents. This approach optimizes the extraction process and improves overall efficiency. Through the utilization of these technologies, businesses can optimize resource utilization, improve decision-making processes, and contribute to sustainable development goals, thereby fostering environmental responsibility within the corporate sector.
Political Events using RAG with LLMs
Authors: Muhammad Arslan, Saba Munawar, Christophe Cruz
Date: 2025-01-06 00:00:00
Tasks: Articles, Event Extraction, RAG, Retrieval, Retrieval-augmented Generation

In the contemporary digital landscape, media content stands as the foundation for political news analysis, offering invaluable insights sourced from various channels like news articles, social media updates, speeches, and reports. Natural Language Processing (NLP) has revolutionized Political Information Extraction (IE), automating tasks such as Event Extraction (EE) from these diverse media outlets. While traditional NLP methods often necessitate specialized expertise to build rule-based systems or train machine learning models with domain-specific datasets, the emergence of Large Language Models (LLMs) driven by Generative Artificial Intelligence (GenAI) presents a promising alternative. These models offer accessibility, alleviating challenges associated with model construction from scratch and reducing the dependency on extensive datasets during the training phase, thus facilitating rapid implementation. However, challenges persist in handling domain-specific tasks, leading to the development of the Retrieval-Augmented Generation (RAG) framework. RAG enhances LLMs by integrating external data retrieval, enriching their contextual understanding, and expanding their knowledge base beyond pre-existing training data. To illustrate RAG's efficacy, we introduce the Political EE system, specifically tailored to extract political event information from news articles. Understanding these political insights is essential for remaining informed about the latest political advancements, whether on a national or global scale.
Large language models streamline automated systematic review: A preliminary study
Authors: Xi Chen, Xue Zhang
Date: 2025-01-09 00:00:00
Tasks: PICO

Large Language Models (LLMs) have shown promise in natural language processing tasks, with the potential to automate systematic reviews. This study evaluates the performance of three state-of-the-art LLMs in conducting systematic review tasks. We assessed GPT-4, Claude-3, and Mistral 8x7B across four systematic review tasks: study design formulation, search strategy development, literature screening, and data extraction. Sourced from a previously published systematic review, we provided reference standard including standard PICO (Population, Intervention, Comparison, Outcome) design, standard eligibility criteria, and data from 20 reference literature. Three investigators evaluated the quality of study design and eligibility criteria using 5-point Liker Scale in terms of accuracy, integrity, relevance, consistency and overall performance. For other tasks, the output is defined as accurate if it is the same as the reference standard. Search strategy performance was evaluated through accuracy and retrieval efficacy. Screening accuracy was assessed for both abstracts screening and full texts screening. Data extraction accuracy was evaluated across 1,120 data points comprising 3,360 individual fields. Claude-3 demonstrated superior overall performance in PICO design. In search strategy formulation, GPT-4 and Claude-3 achieved comparable accuracy, outperforming Mistral. For abstract screening, GPT-4 achieved the highest accuracy, followed by Mistral and Claude-3. In data extraction, GPT-4 significantly outperformed other models. LLMs demonstrate potential for automating systematic review tasks, with GPT-4 showing superior performance in search strategy formulation, literature screening and data extraction. These capabilities make them promising assistive tools for researchers and warrant further development and validation in this field.
TutorLLM: Customizing Learning Recommendations with Knowledge Tracing and Retrieval-Augmented Generation
Authors: Zhaoxing Li, Vahid Yazdanpanah, Jindi Wang, Wen Gu, Lei Shi, Alexandra I. Cristea, Sarah Kiden, Sebastian Stein
Date: 2025-01-20 00:00:00
Tasks: Knowledge Tracing, RAG, Retrieval, Retrieval-augmented Generation

The integration of AI in education offers significant potential to enhance learning efficiency. Large Language Models (LLMs), such as ChatGPT, Gemini, and Llama, allow students to query a wide range of topics, providing unprecedented flexibility. However, LLMs face challenges, such as handling varying content relevance and lack of personalization. To address these challenges, we propose TutorLLM, a personalized learning recommender LLM system based on Knowledge Tracing (KT) and Retrieval-Augmented Generation (RAG). The novelty of TutorLLM lies in its unique combination of KT and RAG techniques with LLMs, which enables dynamic retrieval of context-specific knowledge and provides personalized learning recommendations based on the student's personal learning state. Specifically, this integration allows TutorLLM to tailor responses based on individual learning states predicted by the Multi-Features with Latent Relations BERT-based KT (MLFBK) model and to enhance response accuracy with a Scraper model. The evaluation includes user assessment questionnaires and performance metrics, demonstrating a 10% improvement in user satisfaction and a 5\% increase in quiz scores compared to using general LLMs alone.
GPUs, CPUs, and... NICs: Rethinking the Network's Role in Serving Complex AI Pipelines
Authors: Mike Wong, Ulysses Butler, Emma Farkash, Praveen Tammana, Anirudh Sivaraman, Ravi Netravali
Date: 2025-01-22 00:00:00
Tasks: Management

The increasing prominence of AI necessitates the deployment of inference platforms for efficient and effective management of AI pipelines and compute resources. As these pipelines grow in complexity, the demand for distributed serving rises and introduces much-dreaded network delays. In this paper, we investigate how the network can instead be a boon to the excessively high resource overheads of AI pipelines. To alleviate these overheads, we discuss how resource-intensive data processing tasks -- a key facet of growing AI pipeline complexity -- are well-matched for the computational characteristics of packet processing pipelines and how they can be offloaded onto SmartNICs. We explore the challenges and opportunities of offloading, and propose a research agenda for integrating network hardware into AI pipelines, unlocking new opportunities for optimization.
UAV-assisted Internet of Vehicles: A Framework Empowered by Reinforcement Learning and Blockchain
Authors: Ahmed Alagha, Maha Kadadha, Rabeb Mizouni, Shakti Singh, Jamal Bentahar, Hadi Otrok
Date: 2025-01-22 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning

This paper addresses the challenges of selecting relay nodes and coordinating among them in UAV-assisted Internet-of-Vehicles (IoV). The selection of UAV relay nodes in IoV employs mechanisms executed either at centralized servers or decentralized nodes, which have two main limitations: 1) the traceability of the selection mechanism execution and 2) the coordination among the selected UAVs, which is currently offered in a centralized manner and is not coupled with the relay selection. Existing UAV coordination methods often rely on optimization methods, which are not adaptable to different environment complexities, or on centralized deep reinforcement learning, which lacks scalability in multi-UAV settings. Overall, there is a need for a comprehensive framework where relay selection and coordination are coupled and executed in a transparent and trusted manner. This work proposes a framework empowered by reinforcement learning and Blockchain for UAV-assisted IoV networks. It consists of three main components: a two-sided UAV relay selection mechanism for UAV-assisted IoV, a decentralized Multi-Agent Deep Reinforcement Learning (MDRL) model for autonomous UAV coordination, and a Blockchain implementation for transparency and traceability in the interactions between vehicles and UAVs. The relay selection considers the two-sided preferences of vehicles and UAVs based on the Quality-of-UAV (QoU) and the Quality-of-Vehicle (QoV). Upon selection of relay UAVs, the decentralized coordination between them is enabled through an MDRL model trained to control their mobility and maintain the network coverage and connectivity using Proximal Policy Optimization (PPO). The evaluation results demonstrate that the proposed selection and coordination mechanisms improve the stability of the selected relays and maximize the coverage and connectivity achieved by the UAVs.
TrustDataFilter:Leveraging Trusted Knowledge Base Data for More Effective Filtering of Unknown Information
Authors: Jinghong Zhang, Yidong Cui, Weiling Wang, Xianyou Cheng
Date: 2025-01-25 00:00:00
Tasks: Natural Language Inference

With the advancement of technology and changes in the market, the demand for the construction of domain-specific knowledge bases has been increasing, either to improve model performance or to promote enterprise innovation and competitiveness. The construction of domain-specific knowledge bases typically relies on web crawlers or existing industry databases, leading to problems with accuracy and consistency of the data. To address these challenges, we considered the characteristics of domain data, where internal knowledge is interconnected, and proposed the Self-Natural Language Inference Data Filtering (self-nli-TDF) framework. This framework compares trusted filtered knowledge with the data to be filtered, deducing the reasoning relationship between them, thus improving filtering performance. The framework uses plug-and-play large language models for trustworthiness assessment and employs the RoBERTa-MNLI model from the NLI domain for reasoning. We constructed three datasets in the domains of biology, radiation, and science, and conducted experiments using RoBERTa, GPT3.5, and the local Qwen2 model. The experimental results show that this framework improves filter quality, producing more consistent and reliable filtering results.
Regulating Multifunctionality
Authors: Cary Coglianese, Colton R. Crum
Date: 2025-01-26 00:00:00
Tasks: Management

Foundation models and generative artificial intelligence (AI) exacerbate a core regulatory challenge associated with AI: its heterogeneity. By their very nature, foundation models and generative AI can perform multiple functions for their users, thus presenting a vast array of different risks. This multifunctionality means that prescriptive, one-size-fits-all regulation will not be a viable option. Even performance standards and ex post liability - regulatory approaches that usually afford flexibility - are unlikely to be strong candidates for responding to multifunctional AI's risks, given challenges in monitoring and enforcement. Regulators will do well instead to promote proactive risk management on the part of developers and users by using management-based regulation, an approach that has proven effective in other contexts of heterogeneity. Regulators will also need to maintain ongoing vigilance and agility. More than in other contexts, regulators of multifunctional AI will need sufficient resources, top human talent and leadership, and organizational cultures committed to regulatory excellence.