mlsanityData to 14 Oct 2026

DiversiGATE: A Comprehensive Framework for Reliable Large Language Models

arXiv 2306.13230 ↗First posted 22 Jun 2023cs.CL cs.AIcited by 1

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Abstract (arXiv)

In this paper, we introduce DiversiGATE, a unified framework that consolidates diverse methodologies for LLM verification. The proposed framework comprises two main components: Diversification and Aggregation which provide a holistic perspective on existing verification approaches, such as Self-Consistency, Math Prompter and WebGPT. Furthermore, we propose a novel `SelfLearner' model that conforms to the DiversiGATE framework which can learn from its own outputs and refine its performance over time, leading to improved accuracy. To evaluate the effectiveness of SelfLearner, we conducted a rigorous series of experiments, including tests on synthetic data as well as on popular arithmetic reasoning benchmarks such as GSM8K. Our results demonstrate that our approach outperforms traditional LLMs, achieving a considerable 54.8% -> 61.8% improvement on the GSM8K benchmark.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 23 of its 27 references that are in the map. First posted on arXiv 22 Jun 2023.

Builds on

23 of 27 references.

References · 23

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020Language Models are Few-Shot Learnerscited by 32,419
  4. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  5. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  6. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  7. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  8. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  9. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  10. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
  11. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,998
  12. 2018CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledgecited by 1,822
11 more
  1. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,745
  2. 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,256
  3. 2022LaMDA: Language Models for Dialog Applicationscited by 1,220
  4. 2021Scaling Language Models: Methods, Analysis & Insights from Training Gophercited by 1,202
  5. 2021What Makes Good In-Context Examples for GPT-3?cited by 1,195
  6. 2021Are NLP Models really able to Solve Simple Math Word Problems?cited by 878
  7. 2021Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategiescited by 743
  8. 2020It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learnerscited by 669
  9. 2021Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigmcited by 606
  10. 2016Solving General Arithmetic Word Problemscited by 360
  11. 2023MathPrompter: Mathematical Reasoning using Large Language Modelscited by 236

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Citing papers per year: 2023: 1.

1 paper.

  1. 2023How (not) to ensemble LVLMs for VQAcited by 4

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  1. 2025Do LLMs Overthink Basic Math Reasoning? Benchmarking the Accuracy-Efficiency Tradeoff in Language Models
  2. 2026VERGE: Formal Refinement and Guidance Engine for Verifiable LLM Reasoning
  3. 2025Mathematical Reasoning in Large Language Models: Assessing Logical and Arithmetic Errors across Wide Numerical Ranges
  4. 2024Are Your LLMs Capable of Stable Reasoning?
  5. 2025GSM-Infinite: How Do Your LLMs Behave over Infinitely Increasing Context Length and Reasoning Complexity?
  6. 2024Improving LLM Reasoning through Scaling Inference Computation with Collaborative Verification
  7. 2024Evaluating Mathematical Reasoning of Large Language Models: A Focus on Error Identification and Correction
  8. 2026Grading the Graders: Verification Autonomy Levels (L0-L5) for LLM Reasoning