mlsanityData to 14 Oct 2026

CARO: Chain-of-Analogy Reasoning Optimization for Robust Content Moderation

arXiv 2604.10504 ↗First posted 12 Apr 2026cs.AIcited by 0

On the map: LLM reasoning → Reasoning benchmarks and evaluation

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Abstract (arXiv)

Current large language models (LLMs), even those explicitly trained for reasoning, often struggle with ambiguous content moderation cases due to misleading "decision shortcuts" embedded in context. Inspired by cognitive psychology insights into expert moderation, we introduce caro (Chain-of-Analogy Reasoning Optimization), a novel two-stage training framework to induce robust analogical reasoning in LLMs. First, caro bootstraps analogical reasoning chains via retrieval-augmented generation (RAG) on moderation data and performs supervised fine-tuning (SFT). Second, we propose a customized direct preference optimization (DPO) approach to reinforce analogical reasoning behaviors explicitly. Unlike static retrieval methods, caro dynamically generates tailored analogical references during inference, effectively mitigating harmful decision shortcuts. Extensive experiments demonstrate that caro substantially outperforms state-of-the-art reasoning models (DeepSeek R1, QwQ), specialized moderation models (LLaMA Guard), and advanced fine-tuning and retrieval-augmented methods, achieving an average F1 score improvement of 24.9% on challenging ambiguous moderation benchmarks.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 30 of its 39 references that are in the map. First posted on arXiv 12 Apr 2026.

Builds on

30 of 39 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Policy-as-Prompt: Rethinking Content Moderation in the Age of Large Language Models25 Feb
  2. 2023Moderating New Waves of Online Hate with Chain-of-Thought Reasoning in Large Language Models22 Dec

Other references · 28

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2022Training language models to follow instructions with human feedbackcited by 15,971
  4. 2024The Llama 3 Herd of Modelscited by 14,715
  5. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  6. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  7. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
  8. 2024Qwen2.5 Technical Reportcited by 4,942
  9. 2019The Curious Case of Neural Text Degenerationcited by 2,916
  10. 2024LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Modelscited by 1,464
  11. 2025Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learningcited by 1,444
  12. 2019ZeRO: Memory Optimizations Toward Training Trillion Parameter Modelscited by 1,301
16 more
  1. 2023Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversationscited by 1,207
  2. 2018Hierarchical Neural Story Generationcited by 1,193
  3. 2017Mixed Precision Trainingcited by 1,156
  4. 2024WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMscited by 496
  5. 2022A Holistic Approach to Undesired Content Detection in the Real Worldcited by 356
  6. 2024ShieldGemma: Generative AI Content Moderation Based on Gemmacited by 243
  7. 2023ToxicChat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User-AI Conversationcited by 231
  8. 2024SALAD-Bench: A Hierarchical and Comprehensive Safety Benchmark for Large Language Modelscited by 228
  9. 2024AEGIS: Online Adaptive AI Content Safety Moderation with Ensemble of LLM Expertscited by 136
  10. 2025GuardReasoner: Towards Reasoning-based LLM Safeguardscited by 100
  11. 2022COLD: A Benchmark for Chinese Offensive Language Detectioncited by 76
  12. 2024RigorLLM: Resilient Guardrails for Large Language Models against Undesired Contentcited by 68
  13. 2022Entropy-based Attention Regularization Frees Unintended Bias Mitigation from Listscited by 55
  14. 2023You Only Prompt Once: On the Capabilities of Prompt Learning on Large Language Models to Tackle Toxic Contentcited by 39
  15. 2023Adapting Large Language Models for Content Moderation: Pitfalls in Data Engineering and Supervised Fine-tuningcited by 29
  16. 2024RAGAR, Your Falsehood Radar: RAG-Augmented Reasoning for Political Fact-Checking using Multimodal Large Language Modelscited by 27

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  1. 2026CHAIRO: Contextual Hierarchical Analogical Induction and Reasoning Optimization for LLMs
  2. 2026Learning to Reason by Analogy via Retrieval-Augmented Reinforcement Fine-Tuning
  3. 2023ANALOGYKB: Unlocking Analogical Reasoning of Language Models with A Million-scale Knowledge Base
  4. 2022E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning
  5. 2025SIGMA: Refining Large Language Model Reasoning via Sibling-Guided Monte Carlo Augmentation
  6. 2026HiPO: Hierarchical Preference Optimization for Adaptive Reasoning in LLMs
  7. 2026Hit-RAG: Learning to Reason with Long Contexts via Preference Alignment
  8. 2025Deliberation on Priors: Trustworthy Reasoning of Large Language Models on Knowledge Graphs