Parallel LLM Reasoning for Bias-Resilient, Robust Conceptual Abstraction
The paper presents a framework for improving the performance of large language models (LLMs) in analyzing long documents. By utilizing parallel chunk-level processing and evidence-anchored consolidation, the study aims to reduce analytical biases and improve traceability. Experimental results indicate significant reductions in omission errors and unsupported claims, particularly benefiting smaller models.
- ▪Large language models often struggle with contextual reasoning when processing long documents sequentially.
- ▪The proposed framework processes text in semantically coherent chunks independently to minimize bias and over-generalization.
- ▪Experiments show that this method reduces omission errors by approximately 84% and increases evidence traceability by up to 130%.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Computation and Language arXiv:2605.20194 (cs) [Submitted on 4 Apr 2026] Title:Parallel LLM Reasoning for Bias-Resilient, Robust Conceptual Abstraction Authors:Aisvarya Adeseye, Jouni Isoaho, Adeyemi Adeseye View a PDF of the paper titled Parallel LLM Reasoning for Bias-Resilient, Robust Conceptual Abstraction, by Aisvarya Adeseye and 2 other authors View PDF HTML (experimental) 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.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
