Input Order Dramatically Affects LLM Summarization Quality

A new research paper from arXiv examines a critical yet understudied aspect of large language model behavior: how the order of input documents fundamentally shapes the semantic alignment and quality of generated summaries. The findings have significant implications for AI systems that synthesize information from multiple sources, including content generation tools and automated reporting systems.

The Input Order Problem

When language models process multiple documents to create a unified summary, researchers have discovered that the sequence in which documents are presented to the model dramatically influences the final output. This phenomenon, explored in the paper "Input Order Shapes LLM Semantic Alignment in Multi-Document Summarization," reveals a fundamental challenge in how AI systems prioritize and integrate information from diverse sources.

The research demonstrates that LLMs exhibit what researchers call "positional bias" — a tendency to weight information differently based on its position in the input sequence. Documents presented earlier or later in the sequence may receive disproportionate influence on the final summary, regardless of their actual relevance or importance to the topic at hand.

Semantic Alignment Challenges

The study focuses specifically on semantic alignment, measuring how well the generated summaries capture the core meaning and key information across all input documents. When input order varies, the semantic alignment scores show significant fluctuation, indicating that the model's understanding and synthesis of the material is not robust to simple reordering of the same content.

This finding is particularly relevant for AI-generated content systems, including video script generation, automated news aggregation, and synthetic media production workflows that rely on multi-source information synthesis. If the order of training data or reference materials can substantially alter the output, it raises questions about the consistency and reliability of AI-generated content.

Implications for Content Generation

For systems generating video scripts, articles, or other synthetic media from multiple sources, this research highlights a critical consideration: the architecture of information input directly affects output quality. Content generation pipelines that process research papers, news articles, or reference documents must account for positional bias to ensure comprehensive and balanced synthesis.

The findings also connect to broader concerns about digital authenticity and AI-generated content verification. If subtle changes in input ordering can produce different summaries from the same source material, detection systems may need to account for this variability when analyzing potentially AI-generated content for consistency and source fidelity.

Technical Methodology

The research employs quantitative analysis to measure semantic alignment across different input orderings. By systematically permuting document sequences and evaluating the resulting summaries, the study provides empirical evidence of how LLMs handle multi-document contexts. The methodology offers a framework for testing other models and could inform the development of more robust summarization systems.

Future Directions

Understanding input order effects opens several avenues for improving LLM-based content generation. Potential solutions include attention mechanism modifications that reduce positional bias, ensemble methods that generate summaries from multiple input orderings, and training procedures that explicitly teach models to be invariant to document sequence.

For the synthetic media and AI video generation community, these insights underscore the importance of rigorous testing across varied input configurations. As AI systems become more sophisticated at generating video scripts, voiceovers, and multimedia content from text sources, ensuring consistent semantic alignment regardless of input order becomes essential for reliable, authentic-seeming output.

The research contributes to ongoing efforts to understand and improve LLM behavior in complex tasks, particularly those involving information synthesis from multiple sources — a capability central to many AI content generation applications.

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