LLMEval3 vs chat2doc.cn：怎么选？

关于 LLMEval3

LLMEval-Logic is a Chinese logical reasoning benchmark built through a three-stage audit pipeline: (a) annotators authored items forward from real-world stories rather than templating backward from formulas, (b) a hand-written rubric checklist together with the Z3 SMT solver double-audited every natural-language → first-order-logic translation, and (c) a closed-loop adversarial hardening agent workflow discarded items that turned out to be too easy. The dataset has two paired splits — LLMEval-Logic-Base (single-question PL & FOL items with Z3-verified answers, gold formalisations and atom-level NL→FL rubrics) and LLMEval-Logic-Hard (multi-question / sub-question items covering enumeration / counting / uniqueness / alternative-solution / counterfactual reasoning). Three independent runs of 14 frontier LLMs under thinking / no-thinking configurations show the strongest model reaches only 37.5% Item Accuracy on Hard, leaving substantial headroom for frontier reasoning research. Following the contamination-resistant tradition of LLMEval-Fair, only 80% of the corpus is released publicly; the remaining 20% is held out as a private contamination-resistant test set maintained by Fudan NLP Lab.

LLMEval-Fair addresses robustness and fairness concerns in LLM evaluation through a 30-month longitudinal study. Built on a proprietary bank of 220,000 graduate-level questions across 13 academic disciplines, it dynamically samples unseen test sets for each evaluation run. Its automated pipeline ensures integrity via contamination-resistant data curation, a novel anti-cheating architecture, and a calibrated LLM-as-a-judge process achieving 90% agreement with human experts. A study of nearly 60 leading models reveals performance ceilings and exposes data contamination vulnerabilities undetectable by static benchmarks.

LLMEval-Med is a physician-validated benchmark for evaluating LLMs on real-world clinical tasks. It covers five core medical areas (Medical Knowledge, Language Understanding, Reasoning, Ethics & Safety, Text Generation) with 2,996 questions from real electronic health records and expert-designed clinical scenarios. An automated evaluation pipeline with expert-developed checklists is validated through human-machine agreement analysis. 13 LLMs across specialized, open-source, and closed-source categories are evaluated.

Chat2Doc.cn是一款专注于文档交互的AI聊天工具，用户可以通过自然语言与上传的PDF、Word、Excel等文件进行对话，快速获取信息。其核心功能包括智能问答，能够理解文档内容并直接回答用户提出的问题，例如从合同或报告中提取关键数据；还支持多文档对比分析，帮助用户同时处理多个文件，找出差异或汇总要点。此外，它具备上下文记忆能力，可基于连续对话深入挖掘文档细节。这款工具适合需要频繁处理文档的办公人员、学生和研究者，尤其适用于合同审查、学术文献解读、数据分析报告梳理等场景，能显著提升信息检索和整理效率。