Parallel LLM Reasoning for Bias-Resilient, Robust Conceptual Abstraction
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 i...
| Authors | Aisvarya Adeseye, Jouni Isoaho, Adeyemi Adeseye |
|---|---|
| Primary category | cs.CL |
| Published | 4 April 2026 |
| Last updated at source | 4 April 2026 |
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