mlsanityData to 14 Oct 2026

Asking a Language Model for Diverse Responses

arXiv 2509.17570 ↗First posted 22 Sep 2025cs.CLcited by 9

On the map: LLM reasoning → Test-time compute and efficient thinking

Abstract (arXiv)

Large language models increasingly rely on explicit reasoning chains and can produce multiple plausible responses for a given context. We study the candidate sampler that produces the set of plausible responses contrasting the ancestral (parallel) sampling against two alternatives: enumeration, which asks the model to produce n candidates in one pass, and iterative sampling, which proposes candidates sequentially while conditioning on the currently generated response set. Under matched budgets, we compare these samplers on quality, lexical and computation flow diversity, and efficiency. Our empirical results demonstrate that enumeration and iterative strategies result in higher diversity at comparable quality. Our findings highlight the potential of simple non-independent sampling strategies to improve response diversity without sacrificing generation quality.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning. Builds on 18 of its 29 references that are in the map. First posted on arXiv 22 Sep 2025.

Builds on

18 of 29 references.

References · 18

  1. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  2. 2019The Curious Case of Neural Text Degenerationcited by 2,916
  3. 2020Learning to summarize from human feedbackcited by 2,588
  4. 2024MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmarkcited by 1,848
  5. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,745
  6. 2015A Diversity-Promoting Objective Function for Neural Conversation Modelscited by 1,077
  7. 2019Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacementcited by 211
  8. 2024Turning Up the Heat: Min-p Sampling for Creative and Coherent LLM Outputscited by 150
  9. 2022Truncation Sampling as Language Model Desmoothingcited by 123
  10. 2024A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discoverycited by 96
  11. 2023Reward-Augmented Decoding: Efficient Controlled Text Generation With a Unidirectional Reward Modelcited by 79
  12. 2019Comparison of Diverse Decoding Methods from Conditional Language Modelscited by 78
6 more
  1. 2024AMBROSIA: A Benchmark for Parsing Ambiguous Questions into Database Queriescited by 35
  2. 2021Determinantal Beam Searchcited by 18
  3. 2025Disambiguate First, Parse Later: Generating Interpretations for Ambiguity Resolution in Semantic Parsingcited by 13
  4. 2024Conformal Generative Modeling with Improved Sample Efficiency through Sequential Greedy Filteringcited by 10
  5. 2024Predict the Next Word: Humans exhibit uncertainty in this task and language models _____cited by 4
  6. 2024On the Low-Rank Parametrization of Reward Models for Controlled Language Generationcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 8.

9 papers.

  1. 2026Reaching Beyond the Mode: RL for Distributional Reasoning in Language Modelscited by 6
  2. 2025Reasoning about Intent for Ambiguous Requestscited by 4
  3. 2026Exploring Diverse Generation Paths via Inference-time Stiefel Activation Steeringcited by 2
  4. 2026InfiniteScienceGym: An Unbounded, Procedurally-Generated Benchmark for Scientific Analysiscited by 1
  5. 2026PLURAL: A Global Dataset for Value Alignmentcited by 1
  6. 2026Automated IEP Generation from Traditional Chinese Parent-Teacher Interviews via Corpus-Grounded Feature Diffusioncited by 0
  7. 2026Enabling Creative Exploration for Vibe Design Agentscited by 0
  8. 2026Improving the Diversity of LLM Outputs without a Trade-offcited by 0
  9. 2026Initialization Improves LLM-Driven Discoverycited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025On the Effect of Sampling Diversity in Scaling LLM Inference
  2. 2025Intent Factored Generation: Unleashing the Diversity in Your Language Model
  3. 2025Diverse, not Short: A Length-Controlled Data Selection Strategy for Improving Response Diversity of Language Models
  4. 2024Keep Guessing? When Considering Inference Scaling, Mind the Baselines
  5. 2024Refining Answer Distributions for Improved Large Language Model Reasoning
  6. 2024Quasi-random Multi-Sample Inference for Large Language Models
  7. 2023Improving Diversity of Demographic Representation in Large Language Models via Collective-Critiques and Self-Voting
  8. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debate