mlsanityData to 14 Oct 2026

PARALLELPROMPT: Extracting Parallelism from Large Language Model Queries

arXiv 2506.18728 ↗First posted 23 Jun 2025cs.LGcited by 3

On the map: Efficient LLM inference and serving → Prompt and context compression

Abstract (arXiv)

LLM serving systems typically treat user prompts as monolithic inputs, optimizing inference through decoding tricks or inter-query batching. However, many real-world prompts contain latent semantic parallelism--decomposable structures where subtasks can be executed independently to reduce latency while preserving meaning. We introduce PARALLELPROMPT, the first benchmark for measuring intra-query parallelism in natural user prompts. Our dataset comprises over 37,000 real-world prompts from public LLM chat logs, each annotated with a structured schema capturing task templates, shared context, and iteration inputs. These schemas are extracted using LLM-assisted prompting with rule-based multilingual validation. To evaluate the benefits of decomposition, we provide an execution suite that benchmarks serial vs. parallel strategies, measuring latency, structural adherence, and semantic fidelity. Our results show that intra-query parallelism can be successfully parsed in over 75% of curated datasets, unlocking up to 5x speedups on tasks like translation, comprehension, and comparative analysis, with minimal quality degradation. By releasing this benchmark, curation pipeline, and evaluation suite, we provide the first standardized testbed for studying structure-aware execution in LLM serving pipelines.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Prompt and context compression, one of 12 directions in Efficient LLM inference and serving. Builds on 25 of its 32 references that are in the map. First posted on arXiv 23 Jun 2025.

Builds on

25 of 32 references.

References · 25

  1. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  2. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  3. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  4. 2023Efficient Streaming Language Models with Attention Sinkscited by 2,186
  5. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,998
  6. 2022Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Themcited by 1,807
  7. 2022Fast Inference from Transformers via Speculative Decodingcited by 1,653
  8. 2020Beyond Accuracy: Behavioral Testing of NLP models with CheckListcited by 1,006
  9. 2023Accelerating Large Language Model Decoding with Speculative Samplingcited by 1,006
  10. 2024Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Headscited by 843
  11. 2024WildChat: 1M ChatGPT Interaction Logs in the Wildcited by 629
  12. 2023LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Datasetcited by 461
13 more
  1. 2021Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarkscited by 395
  2. 2020Are we done with ImageNet?cited by 333
  3. 2018Blockwise Parallel Decoding for Deep Autoregressive Modelscited by 323
  4. 2022Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLPcited by 318
  5. 2024Break the Sequential Dependency of LLM Inference Using Lookahead Decodingcited by 298
  6. 2020Data and its (dis)contents: A survey of dataset development and use in machine learning researchcited by 260
  7. 2023An LLM Compiler for Parallel Function Callingcited by 179
  8. 2023Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generationcited by 105
  9. 2023Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipelinecited by 86
  10. 2023Batch Prompting: Efficient Inference with Large Language Model APIscited by 85
  11. 2024Multi-Task Inference: Can Large Language Models Follow Multiple Instructions at Once?cited by 30
  12. 2024An LLM-Tool Compiler for Fused Parallel Function Callingcited by 15
  13. 2024Tree of Problems: Improving structured problem solving with compositionalitycited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025A Survey on Parallel Text Generation: From Parallel Decoding to Diffusion Language Modelscited by 30
  2. 2025Show or Tell? Modeling the evolution of request-making in Human-LLM conversationscited by 3
  3. 2026Regulating Branch Parallelism in LLM Servingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference
  2. 2025Cross-Lingual Prompt Steerability: Towards Accurate and Robust LLM Behavior across Languages
  3. 2025Hogwild! Inference: Parallel LLM Generation via Concurrent Attention
  4. 2026Parason: Revealing Subtask and Trial Parallelism in LLM Reasoning
  5. 2026Optimizing Prompts for Large Language Models: A Causal Approach
  6. 2025DeepQuestion: Systematic Generation of Real-World Challenges for Evaluating LLMs Performance
  7. 2025Recursive Language Models
  8. 2025TokenPowerBench: Benchmarking the Power Consumption of LLM Inference