为了填补这一空白,提出了DARG框架,一种通过自适应推理图演化来动态评估LLMs的方法。与先前通过模板或设计提示生成测试数据的方法不同“DyVal: Graph-informed dynamic evaluation of large language models”和“Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM Evaluation”,基于表示解决问题所需基本...
《ZSC-Eval: An Evaluation Toolkit and Benchmark for Multi-agent Zero-shot Coordination 》 这篇论文提出了一个名为ZSC-Eval的评估工具包和基准测试,旨在解决多智能体强化学习(MARL)中的零样本协调(Zero-shot Coordination, ZSC)问题。ZSC问题的核心挑战在于训练一个自我智能体(ego agent),使其能够在部署时与...
Molecule Generation with Fragment Retrieval Augmentation WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia Beyond Prompts: Dynamic Conversational Benchmarking of Large Language Models ConflictBank: A Benchmark for Evaluating the Influence of Knowledge Conflicts i...
Consequently, the support information is better utilized, leading to better performance. Extensive experiments have been conducted on two public benchmarks, showing the superiority of HMNet.DependenciesPython 3.10 PyTorch 1.12.0 cuda 11.6 torchvision 0.13.0...
| [MiMoTable: A Multi-scale Spreadsheet Benchmark with Meta Operations for Table Reasoning](https://arxiv.org/abs/2412.11711) | COLING 2024 | 2024-12-16 | TQA,T2T,Table manipulation, Data analysis | 1,719 (spreadsheet, question, answer) triplets from 428 different spreadsheets | Multiple d...
Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems【Tenrec:推荐系统的大规模多用途基准数据集】 APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction【APG:点击率预测的自适应参数生成网络】 因果效应
而且,它继承了GPT架构的灵活性,无需额外模块设计,就能在多任务benchmark中超越当前最先进的扩散策略,并提升超10倍的推理速度!🚀🤖这项研究首次将类VAR架构引入机器人领域,为未来基于该架构的进一步创新奠定了基础,有望推动机器人策略学习的新发展方向。🌟...
港中文&商汤等团队的工作:Visual CoT: Advancing Multi-Modal Language Models with a Comprehensive Dataset and Benchmark for Chain-of-Thought Reasoning 论文链接:arxiv.org/abs/2403.1699 https://github.com/deepcs233/Visual-CoT (二维码自动识别) 写在最后 重磅!国内首个具身智能技术社区来啦!近20+学习体系...
Benchmark Datasets Organic anomalies.GADBench中的数据集只包含在现实场景中自然出现的异常,这与以前使用合成异常评估GAD的研究不同。这些早期的工作通常将人工节点属性和结构注入到像Cora这样的普通图中,导致相对容易识别的异常,并且与不同于现实世界的异常明显不同。