# Dipper: Diversity in Prompts for Producing Large Language Model Ensembles in Reasoning tasks

arXiv: https://arxiv.org/abs/2412.15238 · HTML: https://mlsanity.com/paper/2412.15238/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 24 of its 38 references that are in the map.
- First posted on arXiv 12 Dec 2024.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 24 of 24)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](https://mlsanity.com/paper/2408.03314/) (2024)
- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors](https://mlsanity.com/paper/2308.10848/) (2023)
- [Towards Reasoning in Large Language Models: A Survey](https://mlsanity.com/paper/2212.10403/) (2022)
- [LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion](https://mlsanity.com/paper/2306.02561/) (2023)
- [Ensemble deep learning: A review](https://mlsanity.com/paper/2104.02395/) (2021)
- [Prompt Cache: Modular Attention Reuse for Low-Latency Inference](https://mlsanity.com/paper/2311.04934/) (2023)
- [Reasoning with Language Model Prompting: A Survey](https://mlsanity.com/paper/2212.09597/) (2022)
- [Progressive-Hint Prompting Improves Reasoning in Large Language Models](https://mlsanity.com/paper/2304.09797/) (2023)
- [Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves](https://mlsanity.com/paper/2311.04205/) (2023)
- [Ensemble Learning for Heterogeneous Large Language Models with Deep Parallel Collaboration](https://mlsanity.com/paper/2404.12715/) (2024)
- [Neural Ensemble Search for Uncertainty Estimation and Dataset Shift](https://mlsanity.com/paper/2006.08573/) (2020)
- [Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars](https://mlsanity.com/paper/2405.16122/) (2024)
- [Efficient Prompt Caching via Embedding Similarity](https://mlsanity.com/paper/2402.01173/) (2024)

## Built on by (most cited first, 22 of 22)

- [A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?](https://mlsanity.com/paper/2503.24235/) (2025)
- [Roll the dice & look before you leap: Going beyond the creative limits of next-token prediction](https://mlsanity.com/paper/2504.15266/) (2025)
- [On the Effect of Sampling Diversity in Scaling LLM Inference](https://mlsanity.com/paper/2502.11027/) (2025)
- [LLM-Based Social Simulations Require a Boundary](https://mlsanity.com/paper/2506.19806/) (2025)
- [Uncertainty Quantification for Multimodal Large Language Models with Incoherence-adjusted Semantic Volume](https://mlsanity.com/paper/2602.24195/) (2026)
- [Multi-Novelty: Improve the Diversity and Novelty of Contents Generated by Large Language Models via inference-time Multi-Views Brainstorming](https://mlsanity.com/paper/2502.12700/) (2025)
- [ConlangCrafter: Constructing Languages with a Multi-Hop LLM Pipeline](https://mlsanity.com/paper/2508.06094/) (2025)
- [Hear Both Sides: Efficient Multi-Agent Debate via Diversity-Aware Message Retention](https://mlsanity.com/paper/2603.20640/) (2026)
- [Beyond Quality: Unlocking Diversity in Ad Headline Generation with Large Language Models](https://mlsanity.com/paper/2508.18739/) (2025)
- [Within-Model vs Between-Prompt Variability in Large Language Models for Creative Tasks](https://mlsanity.com/paper/2601.21339/) (2026)
- [Real-Time Trustworthiness Scoring for LLM Structured Outputs and Data Extraction](https://mlsanity.com/paper/2603.18014/) (2026)
- [Mixture of Complementary Agents for Robust LLM Ensemble](https://mlsanity.com/paper/2605.24048/) (2026)
- [PIED: Physics-Informed Experimental Design for Inverse Problems](https://mlsanity.com/paper/2503.07070/) (2025)
- [Broaden your SCOPE! Efficient Multi-turn Conversation Planning for LLMs with Semantic Space](https://mlsanity.com/paper/2503.11586/) (2025)
- [CIEGAD: Cluster-Conditioned Interpolative and Extrapolative Framework for Geometry-Aware and Domain-Aligned Data Augmentation](https://mlsanity.com/paper/2512.10178/) (2025)
- [Forty Shades of Blue: Quality-Diversity Alignment via Mode-Conditioned Reinforcement Learning](https://mlsanity.com/paper/2609.14896/) (2026)
- [Uncovering Scaling Laws for Large Language Models via Inverse Problems](https://mlsanity.com/paper/2509.07909/) (2025)
- [Small Language Model Helps Resolve Semantic Ambiguity of LLM Prompt](https://mlsanity.com/paper/2604.23263/) (2026)
- [KACE: Knowledge-Adaptive Context Engineering for Mathematical Reasoning](https://mlsanity.com/paper/2606.00532/) (2026)
- [MOSCOPT: Mixture-of-Skills Collective Optimization for LLM Agents](https://mlsanity.com/paper/2609.14399/) (2026)
- [Efficient Reasoning Exploration via State-Conditioned Latent Steering with Progress Guidance](https://mlsanity.com/paper/2609.24066/) (2026)
- [Two Heads Are Better Than One: Aggregating Weaker LLMs for Better Forecasts](https://mlsanity.com/paper/2609.33257/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs), particularly smaller variants, still struggle with complex reasoning tasks. While inference-time prompting can guide reasoning, existing methods often rely on sequential queries. Ensemble approaches offer a promising path to performance gains, especially given recent batch inference speed-ups. This work introduces DIPPER, a novel, training-free framework that transforms a single LLM into an effective inference-time ensemble. By feeding the model an optimized and diverse set of prompts in parallel, DIPPER elicits varied reasoning paths, leading to performance gains. We empirically demonstrate significant improvements on reasoning benchmarks, such as MATH, where a DIPPER ensemble of three Qwen2-MATH-1.5B instances (via parallel prompting of a single model) outperforms a larger 7B model.