This guide introduces CEL-PHP, a high‑performance, non‑Turing‑complete expression engine for PHP 8+, showing how to install it, evaluate simple and contextual expressions, handle parsing and optimization, integrate caching, register custom functions, and avoid common pitfalls for robust backend rule evaluation.
This article revisits nanochat's core components, detailing the preparation of diverse training datasets, the scaling calculations for tokens and parameters, the model's MQA and KV‑cache design, the full training pipeline with gradient accumulation and mixed‑precision, cost breakdown, inference optimizations, evaluation tasks, and identified limitations with suggested improvements.
Large language models are evolving from free‑form text generators to reliable data providers by mastering structured output through prompt engineering, validation frameworks, constrained decoding, supervised fine‑tuning, reinforcement learning, and API‑level capabilities, enabling seamless integration with software systems while addressing hallucinations and format reliability.
OCRBench v2, introduced at NeurIPS 2025, evaluates 58 multimodal models on 23 OCR‑related tasks in Chinese and English, revealing that even top models like Gemini‑2.5‑Pro barely exceed the passing threshold and that most models struggle with fine‑grained text localization and multilingual performance.
This article explains why many procurement processes fail despite formal procedures and provides a step‑by‑step, data‑driven approach—clarifying requirements, using SRM templates, screening suppliers with performance data, scoring comprehensively, ensuring traceability, and conducting post‑award reviews—to select the right suppliers and turn procurement into a strategic advantage.
The article explains how to assess fine‑tuned large models using both human judgment and dataset‑driven metrics, outlines common pitfalls, introduces benchmark datasets and evaluation frameworks, and provides concise answers to related interview questions.
OpenAI’s latest research reveals that language model hallucinations stem from training and evaluation incentives that favor confident guesses over acknowledging uncertainty, and proposes revised scoring methods that reward modesty, highlighting statistical mechanisms behind false answers and offering pathways to reduce hallucinations.
This article explains why large language models often produce confident but incorrect answers, detailing statistical inevitability, data scarcity, and model capacity limits, and proposes concrete solutions such as confidence thresholds and allowing abstention to reduce hallucinations.
This article surveys the latest research on role‑playing AI agents, covering their definition, core components, application scenarios, three main challenges—role fidelity, long‑term memory, and evaluation—and presents four technical approaches for each challenge along with future research directions and references.
The article presents MiroMind‑M1, an open‑source math‑reasoning language model that combines a 719K high‑quality SFT dataset, a novel CAMPO reinforcement‑learning algorithm, and extensive evaluations on AIME24, AIME25, and MATH‑500, demonstrating state‑of‑the‑art performance while reducing token usage.
This article presents a thorough evaluation of the open‑source JoyAgent‑JDGenie multi‑agent AI framework, covering its background, test cases for restaurant recommendation and travel planning, deployment steps, performance metrics, and concluding recommendations, highlighting its efficiency, ease of deployment, and result quality.
This article introduces the open‑source Zhi‑Create‑Qwen3‑32B model, detailing its fine‑tuned training on creative‑writing data, the multi‑domain dataset strategy, curriculum‑learning based SFT, evaluation on WritingBench, and practical deployment options across various hardware and inference frameworks.
This article explains the design of the edit_file tool, the fast‑apply model that rewrites whole files instead of diffs, its training and evaluation methodology, speculative decoding speed gains, and future research directions for large‑scale code‑editing AI systems.
In an interview with Vivo AI engineer Liang Tianan, the article explores the challenges of post‑Q&A recommendation, the integration of large language models into recall, ranking and evaluation pipelines, and the engineering trade‑offs required to deliver high‑quality, diverse suggestions on mobile devices.
In a detailed interview, ByteDance AI specialist Cai Conghuai explains how large‑model techniques such as SFT, DPO and RAG address Douyin’s multimodal user‑experience challenges, improve signal detection, root‑cause analysis, and outline future AI‑agent breakthroughs for content platforms.
This article presents SCALE, a community‑driven, open‑source benchmark that expands beyond simple Text‑to‑SQL accuracy to evaluate large language models on performance, dialect conversion, and deep SQL understanding, offering developers, researchers, and CTOs a realistic measure of AI‑assisted database tasks.
This comprehensive guide walks you through the fundamentals of Retrieval‑Augmented Generation (RAG) and AI agents, explains their inner workings, shares optimization tricks, provides ready‑to‑run code snippets, and demonstrates how to evaluate performance with metrics such as recall, faithfulness, and answer relevance.
This article compares two recent papers that investigate why large reasoning models such as Llama and Qwen show degraded instruction‑following performance when using chain‑of‑thought prompting, analyzing attention patterns, training effects, and proposed mitigation strategies.
The article explores how people manipulate evaluation systems by redefining variables, adjusting weights, and shifting perspectives, turning losses into perceived wins, and reveals the psychological and statistical biases that create this illusion, urging more honest, multi‑dimensional, transparent modeling for genuine assessment.
This article presents a comprehensive overview of Zhihu Direct Answer, describing its AI‑driven search architecture, RAG framework, query understanding, retrieval, chunking, reranking, generation, evaluation mechanisms, engineering optimizations, and the professional edition, while sharing concrete performance‑boosting practices and future development plans.
The article argues that artificial intelligence has entered a new phase where reinforcement learning finally generalizes, evaluation becomes more important than pure model performance, and researchers must redesign benchmarks and utility‑focused tasks to drive truly transformative progress.
This article reviews AutoGLM, the first "think‑while‑doing" AI agent released by Zhipu AI, detailing its core capabilities, full‑stack architecture, user experience, identified limitations, and the outcomes of three hands‑on tests using both the client application and a Chrome extension.
The Q‑Eval‑100K dataset, comprising 100 k AIGC images and videos with separate visual‑quality and textual‑consistency annotations, powers the open‑source Q‑Eval‑Score framework that fine‑tunes multimodal models to deliver state‑of‑the‑art, scalable, and objective evaluation—including a “vague‑to‑specific” strategy for long prompts—surpassing existing benchmarks.
This article explains how semantic enrichment, vector and hybrid search, and clustering techniques can be applied to large language model logs to evaluate inputs and outputs, improve compliance auditing, and enhance model iteration across various business scenarios.
This article analyses the shortcomings of large‑model internet search—such as unverifiable sources, fabricated content, and poor instruction compliance—by comparing Qwen‑max, Doubao‑1.5‑pro‑256k, and DeepSeek‑v3, and proposes prompt engineering, post‑processing, and custom tool improvements to boost reliability.
Retrieval‑augmented generation (RAG) enhances large language models by integrating a preprocessing pipeline—cleaning, chunking, embedding, and vector storage—with a query‑driven retrieval and prompt‑injection workflow, leveraging vector databases, multi‑stage recall, advanced prompting, and comprehensive evaluation metrics to mitigate knowledge cut‑off, hallucinations, and security issues.
This article explains the governmental push for digital transformation in financial institutions, introduces the BizDevOps integration model and its domestic and international standards, outlines the evaluation framework and process, showcases case studies, and announces the open registration for the 2025 BizDevOps assessment.
The article explains perplexity as a metric for evaluating large language models, walks through a step‑by‑step probability calculation for a sample sentence, shows how to normalize by sentence length using the geometric mean, and demonstrates that lower perplexity indicates a more accurate and less uncertain model.
JD’s Merchant Intelligent Assistant leverages a large‑language‑model‑based multi‑agent architecture to provide 24/7 e‑commerce support, detailing its evolution, planning techniques, online inference, evaluation methods, sample generation, and practical insights for scalable AI‑driven operations.
The JD Merchant Intelligent Assistant employs a large‑language‑model‑driven multi‑agent architecture with dynamic ReAct planning, enabling merchants to query and execute store operations in under a second with over 90 % decision accuracy, while reducing inference cost, hallucinations, and engineering effort across diverse e‑commerce tasks.
This article explores how AI transforms next‑generation sales by detailing project background and goals, core technologies such as efficient sample generation, model training and evaluation, system deployment impact, practical case studies, challenges, solutions, and future directions across multiple industries.
This article summarizes the key technical insights from Zhihu Direct Answer, an AI-powered search product, covering its product overview, RAG framework, query understanding, retrieval strategies, chunking, reranking, generation techniques, evaluation methods, and engineering optimizations for cost and performance.
This comprehensive survey examines the foundations, tokenization techniques, model architectures, training paradigms, evaluation benchmarks, and open challenges of multimodal next‑token prediction (MMNTP), offering researchers a clear roadmap for future advances in multimodal AI.
This article provides a comprehensive overview of LLM-based Text-to-SQL technology, covering its background, evolution, challenges, various LLM-driven methods, benchmark datasets, evaluation metrics, and future research directions to guide researchers and practitioners in advancing natural language interfaces for databases.
The article outlines the rapid progress of LLM agents in 2024 while highlighting key difficulties in planning capabilities, evaluation methods, dataset generation, and metric design, and suggests practical combinations and product‑level enhancements to improve efficiency, accuracy, and usability.
At NeurIPS 2024, Alibaba Cloud's PAI team presented the Spotlight paper PertEval, which introduces knowledge‑invariant perturbations to expose the true knowledge capacity of LLMs, critiques over‑optimistic static benchmarks, and showcases responsible AI solutions and platform demos for enterprise use.
This article presents a detailed collection of product key performance indicators (KPIs) covering user growth, retention, activity, satisfaction, market share, revenue, development cycles, resource utilization, team satisfaction, brand awareness, and strategic goal achievement, along with formulas, weighting, and scoring methods for systematic performance assessment.
The article distills a fireside chat between OpenAI’s CPO Kevin Weil and Anthropic’s CPO Mike Krieger, highlighting how uncertainty, iterative co‑design, evolving product‑manager skills, human‑AI collaboration, non‑deterministic UI, and emerging trends like proactivity, asynchrony, multimodality and personalization shape modern AI product development.
A user study with 21 participants reveals sixteen critical limitations of generative AI search engines, maps them to eight quantitative metrics, proposes sixteen design recommendations, and evaluates You.com, Perplexity and BingChat against this framework to highlight current performance gaps.
Meta’s newly released 92‑page Movie Gen paper introduces a multimodal LLM that unifies text‑to‑image, text‑to‑video, personalized video, precise video editing, and audio generation, detailing its dual‑model architecture, training pipeline, temporal auto‑encoder design, scaling strategies, evaluation benchmark, and ablation studies.
Index-1.9B-32K is a 1.9B-parameter model with a 32K token context window, achieving strong long‑text performance comparable to larger models while using only about 2% of GPT‑4’s compute, trained via long pre‑training and supervised fine‑tuning, with a trade‑off of reduced short‑context ability.
The article explains that evaluation in mathematical modeling is essential for analyzing and comparing solutions, but it must be followed by continuous optimization and actionable improvements to avoid stagnation and achieve real progress.
This article provides a comprehensive, step‑by‑step guide for developing large‑language‑model (LLM) applications, covering prompt design techniques, n‑shot and chain‑of‑thought strategies, retrieval‑augmented generation, structured I/O, workflow optimization, evaluation pipelines, operational best practices, and team organization to create reliable, scalable AI products.
This article presents a comprehensive knowledge map for developing large‑model applications, covering a four‑layer technical architecture, the full development lifecycle, core elements such as prompt engineering and model fine‑tuning, evaluation methods, and practical case studies, offering guidance for both enterprises and startups.
This article explains how to handle complex multimodal PDFs in RAG systems, outlines extraction, indexing, and multimodal model integration, details four query‑rewriting strategies (HyDE, stepwise, sub‑question, backward), and presents key evaluation metrics and tools for assessing RAG performance.
This article examines the practical challenges of using large language models in software development, including handling long contexts, cross‑file editing, bug‑fixing evaluation methods, and presents benchmark results from SWE‑Bench and its Lite subset to assess model capabilities.
This article presents a comprehensive overview of applying reinforcement learning to recommendation systems, covering background challenges, practical exploration, frontier research directions, multi‑agent and inverse RL approaches, evaluation methods, and future outlooks, based on a KDD‑published study and industry experience.
The report presents the open‑source Index‑1.9B family—base, pure, chat, and character variants—detailing benchmark results, pre‑training optimizations such as a normalized LM‑Head and deeper‑slim architectures, the importance of modest instruction data, alignment via SFT/DPO, role‑play enhancements with RAG, and acknowledges remaining safety and factual limitations.
This article presents Xiaomi's work on LLM‑based Agent technology, covering its perception‑thinking‑action pipeline, technical framework, prompt management, executor and API platform, workflow, optimization strategies, evaluation metrics, and future directions for AI assistants.
The article examines the opportunities and risks of applying large language models (LLMs) to cybersecurity, outlines fourteen practical recommendations for assessing their real‑world capabilities, and concludes with an invitation to the upcoming R&D Efficiency Conference covering AI, product management, and related topics.
Researchers propose a black‑box method to expose test‑set leakage in large language models by comparing log‑probability shifts when test items are shuffled, using Monte‑Carlo estimation and a sharded likelihood test, and demonstrate its effectiveness on several models including Mistral‑7B.
This article reviews the rapid progress of large language models, compares them with knowledge graphs, explores how LLMs can aid knowledge extraction and completion, discusses how knowledge graphs can evaluate and enhance LLMs, and outlines future interactive integration between the two technologies.
This article examines whether the rapid growth of large‑language‑model context windows can eliminate the need for retrieval‑augmented generation, presenting experimental needle‑in‑a‑haystack tests, analysis of model performance across token lengths and needle positions, and practical guidance using an open‑source evaluation tool.
The 360 AI Research Institute introduces HiCo, a hierarchical controllable diffusion model that enables fine‑grained layout control across up to eight image regions, integrates seamlessly with existing Stable Diffusion ecosystems, and demonstrates superior performance on the GRIT‑VAL benchmark for layout‑aware image synthesis.
This guide shares years of promotion‑review experience from major tech firms, outlining company‑specific promotion processes and five essential content elements—systematic design, detailed data, derivation reasoning, upstream/downstream context, and comparative analysis—plus practical presentation and logical techniques to help engineers secure promotions and salary raises.
This article details how the financial sector is adopting large language models, describing the shift from generic to finance‑specific models, the technical challenges and cost considerations, the XuanYuan model releases, novel training and evaluation approaches, and a range of practical applications such as marketing, service, operations, office assistance, and risk control.
This article offers a comprehensive mid‑stage analysis of large‑model technology, discussing its rapid development, emerging challenges such as cost and hallucinations, positioning, scenario applications, cost‑value trade‑offs, and strategic pathways for future research and deployment.
This article provides a comprehensive overview of major benchmark datasets—including CMMLU, MMLU, C‑Eval, GSM8K, Gaokao‑Bench, AGIEval, MATH, BBH, HumanEval, and MBPP—used to evaluate large language models' knowledge, reasoning, and coding abilities, and summarizes related leaderboards and evaluation tools.
The proposed unified platform centralizes prompt creation, evaluation, and iteration for large‑model applications, offering one‑stop hosting, metric‑driven testing, seamless resource integration, model switching, fine‑grained traffic control, and an automated data‑flywheel with QEP scoring, cutting optimization cycles from weeks to days while paving the way for advanced fine‑tuning techniques.
RJUA‑QA is a newly released, large‑scale urology question‑answer dataset constructed from virtual patient records based on clinical experience, featuring 2,132 QA pairs with extensive context, designed to benchmark and improve large language models’ medical reasoning, diagnosis, and treatment recommendation capabilities.
This article provides a comprehensive overview and comparative analysis of popular Java expression engines—including AviatorScript, MVEL, OGNL, SpEL, QLExpress, JEXL, JUEL, and Janino—covering their features, community support, size, performance benchmarks, security settings, usage cases, and syntax differences to guide developers in selecting the most suitable engine for their projects.
CodeFuseEval is an enterprise‑grade, multi‑task benchmark that evaluates code‑generation large models across six languages and thousands of real‑world tasks using both objective metrics (pass@k, BLEU, CodeBLEU) and expert human review, with an open‑source framework, continuous dataset expansion, and a focus on correctness, efficiency, robustness, and service‑level quality.
The article recounts a personal test of a multimodal AI agent in Newport Beach and expands into a detailed analysis of current multimodal LLM architectures, memory mechanisms, task planning, tool usage, personality modeling, cost constraints, evaluation challenges, and the broader social and reliability implications of deploying such agents.
Evaluating language models solely with ROUGE or BLEU is insufficient, so comprehensive benchmarks like GLUE, SuperGLUE, MMLU, C‑Eval, and HELM provide diverse tasks and metrics that more accurately assess linguistic understanding, knowledge acquisition, and robustness across English and Chinese NLP systems.
This article explains what a team is, describes its open‑system nature and three‑layer composition, outlines the Tuckman development model and leadership growth stages, and provides practical guidance on direction, leadership, roles, systems, communication, relationships, and evaluation for managing high‑performing technical teams.
This article defines hallucination in LLMs as a failure of faithfulness or factualness, explores data‑level and model‑level origins, reviews reference‑based and reference‑free evaluation metrics, and surveys current research on data‑centric and model‑centric mitigation techniques along with future directions.
mPLUG-Owl is a modular multimodal dialogue large model from Alibaba DAMO Academy that builds on the mPLUG series, offering advanced image, video, OCR, and multilingual capabilities, with extensive evaluations showing superior performance over MiniGPT‑4, LLaVA, and other multimodal LLMs across various tasks.
This article provides a detailed overview of large language models (LLMs), tracing their evolution from statistical and neural language models to modern pre‑trained transformers, discussing scaling, training, adaptation, utilization, evaluation methods, available resources, and outlining current challenges and future research directions.
This article provides a detailed guide to cloud migration, covering evaluation and analysis, pilot projects, assessment strategies, planning and design with cloud services, verification and implementation steps, continuous measurement, and cost optimization through FinOps to ensure successful and secure migration.
A recent study by the LMSYS organization introduces an Elo‑rated, 1v1 battle arena for large language models, ranking open‑source chatbots like Vicuna, Koala, and ChatGLM, while discussing the limitations of traditional benchmarks and the advantages of crowd‑sourced, scalable evaluation.
This study systematically evaluates ChatGPT and other large language models on their ability to answer commonsense questions, assess their knowledge awareness, and utilize generated knowledge for reasoning, revealing strong QA performance but notable gaps in social and temporal commonsense and in leveraging contextual knowledge.
This article reviews Alibaba’s Tongyi Qianwen large‑language model by testing its self‑introduction, code generation, literary creation, mathematical reasoning, Chinese language understanding, and casual chatting abilities, summarizing strengths, weaknesses, and overall performance compared with other LLMs.
The Vicuna-13B open‑source chatbot, fine‑tuned from LLaMA on 70k ShareGPT conversations, matches over 90% of ChatGPT and Google Bard quality while costing only about $300 to train, thanks to memory optimizations, multi‑turn dialogue handling, and cheap spot‑instance training.
This article reviews the rapid progress of large language models (LLMs), covering their historical development, scaling laws, emergent abilities, core technologies such as training and alignment, resource ecosystems, evaluation methods, safety concerns, and prospective research challenges.
This paper presents a large‑scale evaluation of ChatGPT on knowledge‑base complex question answering, introducing a feature‑driven multi‑label annotation framework and CheckList‑based functional, robustness, and controllability tests, and comparing its performance with other LLMs across multiple English and multilingual datasets.
The Entropy Weight Method (EWM) offers an objective, data‑driven way to calculate indicator weights by measuring information entropy, avoiding subjective bias and improving the reliability of multi‑criteria evaluations across fields such as water quality and resource management.
This article surveys automatic text summarization, detailing system classifications, extractive, abstractive and hybrid techniques, notable recent research, multi‑document and cross‑lingual challenges, major datasets, evaluation metrics, and promising future research avenues in the field.
This article provides a comprehensive overview of automatic text summarization, covering extractive, abstractive, and hybrid methods, system classifications, applications, datasets, evaluation metrics, and future research directions within the field of artificial intelligence.
This article presents a systematic, multi‑dimensional benchmark of six leading cloud speech‑recognition platforms—Alibaba Cloud, Tencent Cloud, iFlytek, Baidu Cloud, Huawei Cloud, and Microsoft Azure—using a 22.6‑hour, 81‑file Mandarin dataset, scoring with the CORR metric and SCTK tool, and discusses each provider's workflow, strengths, pitfalls, and cost.
The article reviews the development background of the digital economy‑driven content risk control industry, examines current content moderation technologies and challenges, describes the establishment of a content technology promotion alliance, outlines its research directions and evaluation standards, and includes a Q&A on regulatory collaboration.
This article compiles and categorizes recent model‑related publications, offering a comprehensive list of evaluation‑model resources—including concepts, preprocessing techniques, weighting methods, and various algorithms—and optimization‑model references covering linear and integer programming, graph theory, network flows, and meta‑heuristics.
The article outlines systematic questioning strategies for judges in corporate promotion defenses, detailing how to clarify definitions, probe processes, assess difficulty, evaluate big‑picture thinking, explore methodology, and link technical work to business value, thereby ensuring fair and insightful evaluations.
The article explains why performance assessments are crucial in large tech firms, outlines the KPI/OKR systems and the typical 271 distribution, reveals hidden rules that affect bonuses and promotions, and offers short‑ and long‑term strategies for employees to navigate and improve their ratings.
This article presents a comprehensive guide to deploying machine translation at Vivo, covering business analysis, algorithm choices beyond standard NMT, language detection challenges, data collection and cleaning, scientific evaluation methods, and engineering optimizations to deliver a seamless user experience.
This article examines the trade‑offs of microservice architecture, comparing it with monolithic design, and provides practical guidance on when to transition, how to assess technical and team readiness, and what risks and splitting strategies to consider.
This article outlines a thorough methodology for evaluating software, hardware, and overall system architectures, detailing assessment criteria, a five‑stage evaluation process, quality‑assurance measures, and best‑practice checkpoints to ensure high availability, scalability, security, and cost‑effectiveness of complex engineering projects.
This article reviews the background, key considerations, architectural principles, evolution, performance comparison, and a customized solution for building mini‑programs using frameworks such as WePY, Taro, and UniApp, highlighting cross‑platform support, TypeScript integration, and development experience improvements.
Anxin Securities’ RPA Unified Management Platform earned the highest 3+ maturity rating at China’s inaugural RPA standard assessment, showcasing extensive automation across finance, operations, and disaster recovery, while outlining future SmartRPA initiatives and AI‑driven enhancements for digital transformation.
The article explains why relying on a single metric like click‑through rate is insufficient for recommendation systems, and outlines a comprehensive, multi‑dimensional evaluation framework that combines business indicators, user behavior metrics, and algorithmic performance measures such as recall, precision, and AUC.
This article details how Toutiao's senior algorithm architect designs a transparent recommendation system, covering system overview, three-dimensional feature modeling, real-time training pipelines, recall strategies, content analysis, user tagging, evaluation methods, and content safety measures.
This article explains the fundamentals of recommendation systems, covering data collection, user and content profiling, system architecture, algorithmic pipelines such as recall, filtering, ranking, and evaluation metrics, while also discussing practical challenges like echo chambers and long‑term user value.
This article outlines a comprehensive framework for designing decision‑type products, covering their evolution stages, core elements of model‑data‑strategy, domain modeling techniques, data‑to‑knowledge transformation, business and process value, and a feedback‑driven decision loop with evaluation and simulation.
This article examines the fundamentals of monolithic and microservice architectures, outlines the advantages and drawbacks of each, provides criteria for deciding when to adopt microservices, and offers practical guidance on technical, talent, and organizational considerations for a successful migration.
This article explores the fundamentals of monolithic and microservice architectures, assesses the benefits, costs, and risks of adopting microservices, and provides practical criteria—including business complexity, team size, and technical readiness—to help decide the optimal moment for migration.
The article details the end‑to‑end architecture of Toutiao's massive recommendation platform, covering system overview, content and user feature extraction, model training, recall strategies, evaluation methodology, and content safety mechanisms, while highlighting practical challenges and engineering solutions.
This article reviews the latest advances in open-domain dialogue systems, covering classification, end‑to‑end generation challenges, knowledge‑controlled generation, automated evaluation, large‑scale latent‑space models such as PLATO, and outlines future research directions for building more coherent and controllable conversational AI.
This article introduces China’s inaugural DevOps Capability Maturity Model, outlines its eight-part structure—including system and tool requirements—describes the standardized evaluation methodology, registration details, and provides contact information for organizations seeking certification.
This article explains how to choose and combine appropriate evaluation metrics for recommendation systems by considering the specific scenario, business model, offline versus online testing, ecosystem balance, and user behavior, providing practical methods and a concise summary of common metric types.
This article details Toutiao’s large‑scale recommendation system, explaining how it models content, user, and environment features, the variety of algorithms and real‑time training pipelines used, feature engineering categories, recall strategies, content analysis, user tagging, evaluation methods, and content‑safety mechanisms.
The article discusses how to discover hot topics using algorithms such as TextRank, BERT embeddings, and BM25, outlines the lifecycle of a hot topic, and proposes practical methods for evaluating article deduplication accuracy and recall when no labeled test set is available.
