Parallel LLM Reasoning for Bias-Resilient, Robust Conceptual Abstraction
A new framework using parallel chunk-level processing and evidence-anchored consolidation significantly reduces omission errors and unsupported claims in LLM analysis of long documents, achieving an 84% reduction in omission error, up to 130% improvement in evidence traceability, and up to 91% reduction in unsupported claims. Smaller models benefit the most.
[2605.20194] Parallel LLM Reasoning for Bias-Resilient, Robust Conceptual Abstraction
[Submitted on 4 Apr 2026]
Title:Parallel LLM Reasoning for Bias-Resilient, Robust Conceptual Abstraction
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Abstract:Large language models (LLMs) have been increasingly used to analyze text. However, they are often plagued with contextual reasoning limitations when analyzing long documents. When long documents are processed sequentially, early or dominant concepts can overshadow less visible but meaningful interpretations, leading to cumulative analytical bias, omission error, and over-generalization. Additionally, independently generated outputs are often merged without systematic grounding, introducing redundancy, conceptual drift, and unsupported claims. This study proposes a structured framework combining parallel chunk-level processing with evidence-anchored consolidation. Texts are first divided into semantically coherent chunks and processed independently in parallel to remove influence from earlier processing. The independently generated interpretations are then consolidated using explicit evidence anchoring and prioritization that reduces dominance and over-generalization while improving traceability. Experiments with multiple model types and sizes indicate that parallel processing significantly reduces omission error by approximately 84%, increases evidence traceability by up to 130%, and reduces unsupported claims by up to 91%. Smaller models benefited most, suggesting that efficient parallel chunking and consolidation play a critical role in achieving reliable and scalable textual analysis.
Comments: Accepted to be Published in 12th Intelligent Systems Conference 2026, 3-4 September 2026 in Amsterdam, The Netherlands
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2605.20194 [cs.CL]
(or arXiv:2605.20194v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.20194
arXiv-issued DOI via DataCite
Submission history
From: Aisvarya Adeseye Mrs [view email] [v1] Sat, 4 Apr 2026 05:11:20 UTC (7,189 KB)
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