A Dialectic Pipeline for Improving LLM Robustness

Jan 28, 2026·
Sara Candussio
Sara Candussio
· 2 min read
Abstract
We propose a dialectic pipeline that preserves LLMs’ generalization abilities while improving answer quality via self-dialogue, enabling the model to reflect upon and correct tentative wrong answers. The pipeline is tested across different datasets and model families, with all stages enriched with relevant context in an oracle-RAG setting.
Type
Publication
MSc Thesis, University of Trieste

Can LLMs improve their accuracy without further training, just through a dialectic way of questioning themselves — as Hegel suggested?

This was the core question behind my Master’s thesis. The short answer: yes, and by a lot.

The Idea

Inspired by Hegelian dialectics, the pipeline structures reasoning into three stages:

The thesis–antithesis–synthesis pipeline.

The thesis–antithesis–synthesis pipeline.

  1. Thesis — the model produces an initial answer given the question, context, and options.
  2. Antithesis — the model challenges its own answer, now also seeing the thesis.
  3. Synthesis — the model produces a final answer, having seen both thesis and antithesis.

No fine-tuning. No domain-specific verifiers. Just structured self-dialogue.

Results

The pipeline was tested on multi-hop QA benchmarks (HotpotQA, WikiHop) across five open-source models under 20B parameters (Phi-mini, Phi-medium, Gemma-2B, Gemma-9B, LLaMA-8B).

Accuracy improvements across models on HotpotQA.

Accuracy improvements across models on HotpotQA.

From 53.4% to 80.7% on HotpotQA with Phi-mini (+27.3%).

From 53.4% to 80.7% on HotpotQA with Phi-mini (+27.3%).

Improvements of up to 30% on complex multi-hop questions — beating standard Chain-of-Thought prompting.

CoT vs. pipeline on WikiHop across all models.

CoT vs. pipeline on WikiHop across all models.

Key Takeaways

  • Self-debating is the main driver: letting models reflect on and contrast their own reasoning significantly boosts performance, especially as question complexity increases.
  • Instruction following matters: models that strictly follow instructions (Llama, Phi) benefit more than those that get “too creative” (Gemma-2).
  • Smart filtering > summarization: when dealing with long contexts, filtering for relevant information beats summarization, which can hurt deductive reasoning.
  • Avoid overthinking: for simpler tasks, too much deliberation can introduce errors. A touch of “impulsivity” sometimes helps.
Original vs. summarized vs. filtered context on WikiHop.

Original vs. summarized vs. filtered context on WikiHop.

This work also received an Honorable Mention at the Emanuele Pianta Award (AILC) for the best Italian NLP Master’s thesis at CLiC-it 2025. 🏆

CLiC-it 2025, Cagliari.

CLiC-it 2025, Cagliari.

If you’re interested in agentic reasoning, small language models, or multi-hop QA — feel free to reach out!

Large Language Models Hallucination Multi-Agent Systems Question Answering
Sara Candussio
Authors
Sara Candussio
PhD student