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 ...
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...
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...
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...
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) ...
特别地,本文还提出了一个新的离线测试集— WizardArena —用于新模型的性能评估及选择,它可以准确地预测模型的 Elo 排名。从其给出的排名结果来看,WizardArena 与 LMSYS Chatbot Arena 的 ranking 结果一致性高达 98.79%,但该模拟竞技场的对战评判效率(等效 16 H100 算力情况下)却实现了高达 40 倍的提升,如下...
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) ...
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 overall coverage of ...