Reward models (Ranking learning) Chatbot Arena -竞技场模式 (Battle count of each combination of models, from LMSYS) (Fraction of Model A wins for all non-tied A vs. B battles, from LMSYS) LLM指令攻防 指令诱导 (诱导模型输出目标答案,from SuperCLUE) * 有害指令注入 (将真实有害意图注入到...
we had prior information on the likely ranking based on our benchmarks and chose to pair models according to this ranking. We gave preference to what we believed would be strong pairings based on this ranking. However, we later switched to uniform sampling to get better ...
reviews_gen: 评估结果生成,目前默认使用GPT-4作为Auto-reviewer,可通过enable参数控制是否开启该步骤 elo_rating: ELO rating 算法,可通过enable参数控制是否开启该步骤,注意该步骤依赖review_file必须存在 执行脚本 #Usage:cd llmuses#dry-run模式 (模型answer正常生成,但专家模型不会被触发,评估结果会随机生成)python...
它不仅能够准确预测 LLM 的 Elo Ranking,还与 LMSYS Arena 有高度一致性,同时更是拥有 40 倍于 LMSYS Arena 的效率。 通过Arena Learning 生成的合成数据进行多轮迭代训练,在各种训练策略下模型展现出显著的性能改进。实验结果证明了 WizardArena 的可靠性、合理性以及整个 Arena Learning 数据飞轮的高效率和强大性...
Reward models (Ranking learning) Chatbot Arena -竞技场模式 (Battle count of each combination of models, from LMSYS) (Fraction of Model A wins for all non-tied A vs. B battles, from LMSYS) LLM指令攻防 指令诱导 (诱导模型输出目标答案,from SuperCLUE) 有害指令注入 (将真实有害意图注入...
Reward models (Ranking learning) Chatbot Arena -竞技场模式 (Battle count of each combination of models, from LMSYS) (Fraction of Model A wins for all non-tied A vs. B battles, from LMSYS) LLM指令攻防 指令诱导 (诱导模型输出目标答案,from SuperCLUE) ...
Reward models (Ranking learning) Chatbot Arena -竞技场模式 (Battle count of each combination of models, from LMSYS) (Fraction of Model A wins for all non-tied A vs. B battles, from LMSYS) LLM指令攻防 指令诱导 (诱导模型输出目标答案,from SuperCLUE) ...
Reward models (Ranking learning) Chatbot Arena -竞技场模式 (Battle count of each combination of models, from LMSYS) (Fraction of Model A wins for all non-tied A vs. B battles, from LMSYS) LLM指令攻防 指令诱导 (诱导模型输出目标答案,from SuperCLUE) 有害指令注入 (将真实有害意图注入...
Figure 2 shows the battles count of each combination of models. When we initially launched the tournament, we had prior information on the likely ranking based on our benchmarks and chose to pair models according to this ranking. We gave preference to what we believed would be strong pairings...
questions_file: question data的路径 answers_gen: 候选模型预测结果生成,支持多个模型,可通过enable参数控制是否开启该模型 reviews_gen: 评估结果生成,目前默认使用GPT-4作为Auto-reviewer,可通过enable参数控制是否开启该步骤 elo_rating: ELO rating 算法,可通过enable参数控制是否开启该步骤,注意该步骤依赖review_file...