aiqbench analysis
This research addresses critical limitations in large language models for long-form content, specifically length collapse and attribute drift in multi-chapter narratives. By decoupling the evaluation of outlines from final writing, the study benchmarks seven frameworks across single-chapter to whole-book granularities, revealing that no single tool dominates all contexts. The findings highlight the importance of matching a framework's intrinsic output form to the specific generation task rather than relying on a universal leader.
A Multi-Framework Comparison of Outline Stages in Long-Form Generation with LLMs
arXiv:2608.26177v1 Announce Type: cross Abstract: Long-form generation exposes fundamental limitations of large language models. Even 70B-parameter models exhibit length collapse at 16k-token outputs, and multi-chapter stories frequently trigger the attribute drift characteristic of the ``lost-in-the-middle'' effect. The ``outline-first, write-later'' paradigm has gained wide adoption, yet existing research evaluates the final writing rather than the outline itself, conflating two evaluation objects that should be decoupled. We construct a unified head-to-head benchmark covering 7 representative long-form generation frameworks across 3 generation granularities -- single-chapter, multi-chapter, and whole-book -- and propose an anchor-based LLM-as-a-judge protocol that directly assesses outlines against the source text on a 5-point anchored scale. Across 21 framework-granularity cells, no single framework dominates; performance depends on the match between a framework's intrinsic output form and the target granularity. SuperWriter ranks first in the length-constrained single-chapter mode, but this advantage degrades in whole-book mode. The outline-side ranking correlates only moderately with the writing-side ranking, supporting the outline--writing decoupling principle. Compute constraints limit the writing-side evaluation to a subset of cases; follow-up experiments will expand the sample size and add cross-model evaluators to enable stronger statistical inference.
Abstract:Long-form generation exposes fundamental limitations of large language models. Even 70B-parameter models exhibit length collapse at 16k-token outputs, and multi-chapter stories frequently trigger the attribute drift characteristic of the ``lost-in-the-middle'' effect. The ``outline-first, write-later'' paradigm has gained wide adoption, yet existing research evaluates the final writing rather than the outline itself, conflating two evaluation objects that should be decoupled. We construct a unified head-to-head benchmark covering 7 representative long-form generation frameworks across 3 generation granularities -- single-chapter, multi-chapter, and whole-book -- and propose an anchor-based LLM-as-a-judge protocol that directly assesses outlines against the source text on a 5-point anchored scale. Across 21 framework-granularity cells, no single framework dominates; performance depends on the match between a framework's intrinsic output form and the target granularity. SuperWriter ranks first in the length-constrained single-chapter mode, but this advantage degrades in whole-book mode. The outline-side ranking correlates only moderately with the writing-side ranking, supporting the outline--writing decoupling principle. Compute constraints limit the writing-side evaluation to a subset of cases; follow-up experiments will expand the sample size and add cross-model evaluators to enable stronger statistical inference.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2608.26177 [cs.CL]
(or arXiv:2608.26177v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.26177 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 276 words
