Unverified50% confidenceBenchmarkExact time
On five classifier-guided tasks, Diffusion-LM's control success rate comprehensively surpasses the PPLM and FUDGE baselines while maintaining good fluency.
1
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50%
Confidence
Long-term
Relevance
9/2/2026
First Seen
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Related Claims
UnverifiedControllable generation in Diffusion-LM balances fluency and control strength by introducing the hyperparameter λ, and performs multiple gradient updates at each diffusion step to improve control effectiveness.68% similarUnverifiedLLM 的判别能力（判断输出质量）通常优于其生成能力（直接产出完美结果），因此多轮迭代可逐步缩小两者差距67% similarUnverifiedPlug-and-play methods such as PPLM and FUDGE keep the parameters of the pre-trained language model unchanged and, during the decoding stage, adjust the generation probability distribution through additionally trained lightweight classifiers to guide the output to satisfy specific attributes.64% similarUnverifiedLLM-Blender 通过训练专用排序模型 PairRanker 来评估多个 LLM 的候选输出并融合最优结果63% similarUnverifiedLMDeploy has greater advantages in adapting to domestically developed models.62% similar
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