The Wisdom of Partisan Crowds: Comparing Collective Intelligence in Humans and LLM-based Agents
专题命中 指令微调 :LLM(title,abstract);large language model(abstract);language model(abstract);分类 cs.CL
AI 大模型
大语言模型、预训练、指令微调、后训练和语言模型应用。
专题命中 指令微调 :LLM(title,abstract);large language model(abstract);language model(abstract);分类 cs.CL
专题命中 指令微调 :instruction tuning(title,abstract);large language model(abstract);language model(abstract);分类 cs.CL
专题命中 指令微调 :language model(title,abstract);LLM(abstract);large language model(abstract);分类 cs.CL
Comments To be published in the main proceedings of the Association for Computational Linguistics, European Chapter (EACL 2024)
专题命中 指令微调 :LLM(title,abstract);large language model(abstract);language model(abstract);分类 cs.CL
专题命中 指令微调 :foundation model(title,abstract);large language model(abstract);language model(abstract);分类 cs.CL
专题命中 指令微调 :instruction tuning(title,abstract);large language model(abstract);language model(abstract);分类 cs.CL
Comments Project page: https://github.com/JayZhang42/FederatedGPT-Shepherd
专题命中 指令微调 :language model(title,abstract);LLM(abstract);large language model(abstract);分类 cs.LG
专题命中 指令微调 :LLM(title,abstract);large language model(abstract);language model(abstract);分类 cs.AI
专题命中 指令微调 :instruction tuning(title,abstract);large language model(abstract);language model(abstract);分类 cs.AI
专题命中 指令微调 :language model(title,abstract);large language model(abstract);prompting(abstract);分类 cs.CL
Comments 4 pages, 1 figure, 2 tables
专题命中 指令微调 :instruction tuning(title);LLM(abstract);large language model(abstract);language model(abstract)
Comments In AAAI-24
专题命中 指令微调 :foundation model(title,abstract);large language model(abstract);language model(abstract);分类 cs.AI
专题命中 指令微调 :LLM(title,abstract);large language model(abstract);language model(abstract);分类 cs.CL
专题命中 指令微调 :LLM(title,abstract);large language model(abstract);language model(abstract);分类 cs.CL
专题命中 指令微调 :language model(title,abstract);large language model(abstract);pretraining(abstract);分类 cs.CL
Comments EMNLP Findings 2023
专题命中 指令微调 :foundation model(title,abstract);language model(abstract);instruction tuning(abstract);分类 cs.LG
Comments 18 pages, 9 figures
专题命中 指令微调 :instruction tuning(title,abstract);large language model(abstract);language model(abstract);分类 cs.CL
Comments Accepted to EMNLP 2023
专题命中 指令微调 :language model(title,abstract);large language model(abstract);instruction tuning(abstract);分类 cs.CL
专题命中 指令微调 :language model(title,abstract);LLM(abstract);large language model(abstract);分类 cs.CL
Comments Accepted to EMNLP 2023 Findings. Work was done before July 2023
专题命中 指令微调 :LLM(title,abstract);large language model(abstract);language model(abstract);分类 cs.CL
专题命中 指令微调 :foundation model(title);large language model(abstract);language model(abstract);instruction tuning(abstract)
专题命中 指令微调 :language model(title,abstract);LLM(abstract);foundation model(abstract);分类 cs.CL
专题命中 指令微调 :language model(title,abstract);LLM(abstract);large language model(abstract);分类 cs.CL
Comments Accepted at RuleML 2023
专题命中 指令微调 :LLM(title,abstract);language model(abstract);prompting(abstract);分类 cs.AI
专题命中 指令微调 :language model(title,abstract);large language model(abstract);instruction tuning(abstract);分类 cs.CL
专题命中 指令微调 :language model(title,abstract);LLM(abstract);large language model(abstract);分类 cs.CL
Comments Work done up till December 2022
专题命中 指令微调 :prompting(title,abstract);large language model(abstract);language model(abstract);分类 cs.CL
Comments Accepted at EACL 2023 main conference. (Camera-ready version)
专题命中 指令微调 :instruction tuning(title,abstract);language model(abstract);small language model(abstract);分类 cs.CL
Comments EMNLP 2022
专题命中 指令微调 :language model(title,abstract);large language model(abstract);pretraining(abstract);分类 cs.CL
Comments Accepted to ACL 2021
TF1-EN-3M:三百万合成道德寓言用于训练小型开放语言模型
机构 * Babeș-Bolyai University(巴纳德-波耶亚大学) ; KlusAI Labs(KlusAI实验室)
专题命中 指令微调 :language model(title);LLM(abstract,abstract_cn);分类 cs.CL、cs.AI、cs.LG
AI总结 本文提出TF1-EN-3M数据集,包含三百万英文寓言,用于训练小型开放语言模型,展示通过指令微调模型生成高质量寓言的方法,验证了无需大模型即可实现大规模道德叙事的可能性。
Comments 18 pages, 6 tables, 1 figure. v2: revised evaluation with open-weight LLM judge panel, expanded citations