title: SimpleStrat: Diversifying Language Model Generation with Stratification
summary: Generating diverse responses from large language models (LLMs) is crucial for
  applications such as planning/search and synthetic data generation, where
  diversity provides distinct answers across generations. Prior approaches rely
  on increasing temperature to increase diversity. However, contrary to popular
  belief, we show not only does this approach produce lower quality individual
  generations as temperature increases, but it depends on model's next-token
  probabilities being similar to the true distribution of answers. We propose
  , an alternative approach that uses the language model itself to
  partition the space into strata. At inference, a random stratum is selected and
  a sample drawn from within the strata. To measure diversity, we introduce
  CoverageQA, a dataset of underspecified questions with multiple equally
  plausible answers, and assess diversity by measuring KL Divergence between the
  output distribution and uniform distribution over valid ground truth answers.
  As computing probability per response/solution for proprietary models is
  infeasible, we measure recall on ground truth solutions. Our evaluation show
  using SimpleStrat achieves higher recall by 0.05 compared to GPT-4o and 0.36
  average reduction in KL Divergence compared to Llama 3.
paper_authors._id: 670d5d3db0c427993f8748b8
paper_authors.name: Justin Wong
paper_authors._id: 670d5d3db0c427993f8748b9
paper_authors.name: Yury Orlovskiy
paper_authors._id: 670d5d3db0c427993f8748ba
paper_authors.name: Michael Luo
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-24T09:27:19.292Z
paper_authors._id: 654037be97949fd2304aab7f
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/654037be97949fd2304aab7f/2cSME81gcwYa2OTeVlq5Q.jpeg
paper_authors.fullname: Michael Luo
paper_authors.type: user
paper_authors.user: michaelzhiluo
paper_authors._id: 670d5d3db0c427993f8748bb
paper_authors.name: Sanjit A. Seshia
paper_authors._id: 670d5d3db0c427993f8748bc
paper_authors.name: Joseph E. Gonzalez
paper_title: SimpleStrat: Diversifying Language Model Generation with Stratification
paper_summary: Generating diverse responses from large language models (LLMs) is crucial for
  applications such as planning/search and synthetic data generation, where
  diversity provides distinct answers across generations. Prior approaches rely
  on increasing temperature to increase diversity. However, contrary to popular
  belief, we show not only does this approach produce lower quality individual
  generations as temperature increases, but it depends on model's next-token
  probabilities being similar to the true distribution of answers. We propose
  , an alternative approach that uses the language model itself to
  partition the space into strata. At inference, a random stratum is selected and
  a sample drawn from within the strata. To measure diversity, we introduce
  CoverageQA, a dataset of underspecified questions with multiple equally
  plausible answers, and assess diversity by measuring KL Divergence between the
  output distribution and uniform distribution over valid ground truth answers.
  As computing probability per response/solution for proprietary models is
  infeasible, we measure recall on ground truth solutions. Our evaluation show
  using SimpleStrat achieves higher recall by 0.05 compared to GPT-4o and 0.36
  average reduction in KL Divergence compared to Llama 3.
paper_ai_summary: A method using language models to partition the response space increases diversity and quality by selecting random strata, outperforming prior methods based on temperature adjustment and existing models.
paper_ai_keywords: language models strata inference CoverageQA KL Divergence recall SimpleStrat GPT-4o Llama 3