Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation

• URL: http://arxiv.org/abs/2607.14109v1
• Date: Thu, 07 May 2026 21:11:51 GMT
• Title: Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation
• Abstract summary: Several studies claim better performance with more sophisticated prompting techniques, but do not provide a comprehensive evaluation.<n>We address this gap through a comprehensive empirical study of 8 prompting techniques across 10 multiple-choice question answering (MCQA) datasets.<n>Our findings reveal a striking paradox that baseline prompting consistently outperforms complex reasoning techniques on various benchmarks.
• Score: 2.221515121005437
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Probing the capabilities of Large Language Models (LLMs) and building robust solutions for Multiple-Choice Question Answering (MCQA) remain central challenges in natural language understanding. Furthermore, the rapid proliferation of LLMs has created the implicit assumption that more sophisticated prompting techniques yield better performance. Several studies claim better performance with more sophisticated prompting techniques, but do not provide a comprehensive evaluation. We address this gap through a comprehensive empirical study of 8 prompting techniques across 10 multiple-choice question answering (MCQA) datasets, encompassing 27 model configurations and roughly 4,300 unique questions evaluated more than 430,000 times. Our findings reveal a striking paradox that baseline prompting consistently outperforms complex reasoning techniques on various benchmarks. Only minimal expert and inductive role framing (CoT-Expert and CoT-Inductive) yields a small but statistically significant $\sim$3 percentage-point (pp) gain over baseline whereas every other elaborate technique we tested matches or under-performs it, often by large margins (up to 31~pp for Self-Analogical). We further investigate three critical phenomena: (1) the unexpected victory of Qwen3-30B-A3B-Thinking-2507 in Elo ratings, (2) the performance-efficiency trade-offs across model variants with different thinking budgets, revealing model-dependent optimal configurations, and (3) the substantial variation in dataset difficulty, with 60% of benchmarks below 70% accuracy and a 47.5~pp spread from easiest to hardest, indicating considerable room for model improvement. These results suggest that the LLM evaluation community may be overcomplicating prompt engineering and that substantial performance gaps remain across diverse benchmarks, offering opportunities for genuine model improvements rather than prompt optimization.

Related papers

• Brevity Constraints Reverse Performance Hierarchies in Language Models [0.0]
  On 7.7% of benchmark problems, larger language models underperform smaller ones by 28.4 percentage points despite 10-100x more parameters.<n>We identify the mechanism as spontaneous scale-dependent verbosity that introduces errors through overelaboration.<n>Our results establish that maximizing large model performance requires scale-aware prompt engineering.
  arXiv  Detail & Related papers  (2026-03-11T06:47:41Z)
• Task Complexity Matters: An Empirical Study of Reasoning in LLMs for Sentiment Analysis [2.1036545320600095]
  Large language models (LLMs) with reasoning capabilities have fueled a compelling narrative that reasoning universally improves performance across language tasks.<n>We test this claim through a comprehensive evaluation of 504 configurations across seven model families.<n>Our findings reveal that reasoning effectiveness is strongly task-dependent, challenging prevailing assumptions.
  arXiv  Detail & Related papers  (2026-02-27T14:49:05Z)
• RankLLM: Weighted Ranking of LLMs by Quantifying Question Difficulty [102.02839046225468]
  RankLLM is a novel framework designed to quantify both question difficulty and model competency.<n>We evaluate 30 models on 35,550 questions across multiple domains.
  arXiv  Detail & Related papers  (2026-02-12T21:28:46Z)
• Scaling Trends for Multi-Hop Contextual Reasoning in Mid-Scale Language Models [0.0]
  We present a controlled study of multi-hop contextual reasoning in large language models.<n>We show that multi-agent systems show the inverse pattern, achieving up to 80% on reasoning tasks where rule-based methods fail.
  arXiv  Detail & Related papers  (2026-01-06T20:18:55Z)
• AutoLogi: Automated Generation of Logic Puzzles for Evaluating Reasoning Abilities of Large Language Models [86.83875864328984]
  We propose an automated method for synthesizing open-ended logic puzzles, and use it to develop a bilingual benchmark, AutoLogi.<n>Our approach features program-based verification and controllable difficulty levels, enabling more reliable evaluation that better distinguishes models' reasoning abilities.
  arXiv  Detail & Related papers  (2025-02-24T07:02:31Z)
• Recursive Decomposition of Logical Thoughts: Framework for Superior Reasoning and Knowledge Propagation in Large Language Models [1.4956870931936515]
  We introduce RDoLT, a novel framework that significantly boosts Large Language Models reasoning performance.<n>RDoLT is built on three key innovations: (1) breaking down complex reasoning tasks into sub-tasks of progressive complexity; (2) employing an advanced selection and scoring mechanism to identify the most promising reasoning thoughts; and (3) integrating a knowledge propagation module that mimics human learning.<n>Our approach was evaluated across multiple benchmarks, including GSM8K, SVAMP, MultiArithm, LastLetterConcatenation, and Gaokao2023 Math.
  arXiv  Detail & Related papers  (2025-01-03T02:55:44Z)
• MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale [66.73529246309033]
  multimodal large language models (MLLMs) have shown significant potential in a broad range of multimodal tasks.<n>Existing instruction-tuning datasets only provide phrase-level answers without any intermediate rationales.<n>We introduce a scalable and cost-effective method to construct a large-scale multimodal instruction-tuning dataset with rich intermediate rationales.
  arXiv  Detail & Related papers  (2024-12-06T18:14:24Z)
• On the Worst Prompt Performance of Large Language Models [93.13542053835542]
  Performance of large language models (LLMs) is acutely sensitive to the phrasing of prompts. We introduce RobustAlpacaEval, a new benchmark that consists of semantically equivalent case-level queries. Experiments on RobustAlpacaEval with ChatGPT and six open-source LLMs from the Llama, Mistral, and Gemma families uncover substantial variability in model performance.
  arXiv  Detail & Related papers  (2024-06-08T13:40:38Z)
• Advancing LLM Reasoning Generalists with Preference Trees [119.57169648859707]
  We introduce Eurus, a suite of large language models (LLMs) optimized for reasoning. Eurus models achieve state-of-the-art results among open-source models on a diverse set of benchmarks.
  arXiv  Detail & Related papers  (2024-04-02T16:25:30Z)
• How Easy is It to Fool Your Multimodal LLMs? An Empirical Analysis on Deceptive Prompts [54.07541591018305]
  We present MAD-Bench, a benchmark that contains 1000 test samples divided into 5 categories, such as non-existent objects, count of objects, and spatial relationship. We provide a comprehensive analysis of popular MLLMs, ranging from GPT-4v, Reka, Gemini-Pro, to open-sourced models, such as LLaVA-NeXT and MiniCPM-Llama3. While GPT-4o achieves 82.82% accuracy on MAD-Bench, the accuracy of any other model in our experiments ranges from 9% to 50%.
  arXiv  Detail & Related papers  (2024-02-20T18:31:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.