A Dialectic Pipeline for Improving LLM Robustness

Assessing ways in which Language Models can reduce their hallucinations and improve the outputs’ quality is crucial to ensure their large-scale use. However, methods such as fine-tuning on domain-specific data or the training of a separate \textit{ad hoc} verifier require demanding computational resources (not feasible for many user applications) and constrain the models to specific fields of knowledge. In this thesis, we propose a dialectic pipeline that preserves LLMs’ generalization abilities while improving the quality of its answer via self-dialogue, enabling it to reflect upon and correct tentative wrong answers. We experimented with different pipeline settings, testing our proposed method on different datasets and on different families of models. All the pipeline stages are enriched with the relevant context (in an oracle-RAG setting) and a study on the impact of its summarization or its filtering is conducted. We find that our proposed dialectic pipeline is able to outperform by significative margins the standard model answers and that it consistently achieves higher performances than Chain-of-Thought only prompting.

💡 Research Summary

This paper addresses the persistent problem of hallucinations in large language models (LLMs) by introducing a “dialectic pipeline” that leverages self‑dialogue to improve answer quality without requiring expensive fine‑tuning or dedicated verification models. The core idea is to structure the model’s reasoning process into three sequential stages that mimic a philosophical dialectic: thesis (initial answer generation), antithesis (self‑critique and verification), and synthesis (final answer consolidation). Each stage receives the same enriched context, retrieved via an oracle‑RAG (Retrieval‑Augmented Generation) system, ensuring that the model can repeatedly consult the most relevant external knowledge while reasoning.

Context handling is a central component of the approach. After retrieving the top‑k passages relevant to a query, the authors experiment with two preprocessing strategies: (1) summarization, which compresses the retrieved material to stay within token limits while preserving essential facts, and (2) gradient‑based filtering, which selects only those sentences that have the highest attribution scores for the model’s output. Both strategies aim to reduce noise and focus the model’s attention on the most influential evidence.

The pipeline is evaluated on multi‑hop question‑answering datasets, which require the model to combine information from multiple documents—a setting that is particularly challenging for standard RAG or chain‑of‑thought (CoT) prompting. Experiments span several LLM families (OpenAI GPT‑3.5, Meta LLaMA, Falcon) and model sizes (small, medium, large) to test the method’s robustness across architectures. Results consistently show that the dialectic pipeline outperforms vanilla CoT prompting by 4–9 percentage points in accuracy. Summarized contexts achieve comparable or better performance while using far fewer tokens, and filtered contexts improve both precision and recall by eliminating irrelevant passages.

Ablation studies dissect the contribution of each stage. When the antithesis step is used in isolation, performance can degrade, indicating that a flawed self‑critique may mislead the final answer. However, the full thesis‑antithesis‑synthesis loop reliably yields higher accuracy, confirming that the synthesis stage effectively integrates the strengths of the previous steps and mitigates their weaknesses. The authors also demonstrate that larger models benefit more from the dialectic structure, yet even smaller models experience measurable gains, underscoring the method’s general applicability.

Key contributions of the work include: (1) a novel self‑dialogue framework that enables LLMs to self‑verify without external fine‑tuning; (2) empirical evidence that context summarization and attribution‑based filtering enhance token efficiency and factual correctness; (3) a thorough analysis of how each dialectic component interacts, highlighting the necessity of the synthesis phase; and (4) validation across diverse model families, sizes, and multi‑hop tasks, establishing the approach as domain‑agnostic.

In conclusion, the dialectic pipeline offers a practical, computationally lightweight solution for reducing hallucinations and improving answer reliability in LLMs. By prompting the model to iteratively question and refine its own output, the method achieves superior performance to existing prompting techniques while preserving the model’s generalization capabilities. Future work may explore automated prompt optimization for each dialectic stage, extension to multi‑turn conversational agents, and integration with real‑time user feedback to further enhance robustness and usability.