aiqbench analysis
This research tackles the "lost-in-the-middle" phenomenon in large language models, where critical information is overlooked during long-context generation. The authors introduce RAL-Writer, a training-free framework that dynamically retrieves and restates essential input segments to maintain coherence and consistency across extended outputs. By establishing a new benchmark dataset and evaluation metrics, this work provides a concrete path forward for improving the reliability of LLMs handling massive context windows.
Lost-in-the-Middle in Long-Text Generation: Synthetic Dataset, Evaluation Framework, and Mitigation
arXiv:2503.06868v2 Announce Type: replace-cross Abstract: Existing long-text generation methods produce lengthy outputs from short inputs, leaving long-input-to-long-output generation underexplored. As input length increases, LLMs increasingly overlook information in the middle of the context--a limitation known as the "lost-in-the-middle" phenomenon--leading to inconsistent and incoherent outputs. To address this problem, we propose Retrieval-Augmented Long-Text Writer (RAL-Writer), a training-free framework consisting of a Planner that generates writing steps and a Writer that produces content according to these steps. The Writer jointly models semantic relevance and positional bias to compute importance scores, dynamically retrieve critical input segments, and strategically restate them. We also construct a benchmark dataset for long-input-to-long-output generation and introduce three evaluation metrics covering length, consistency, and quality. We evaluate RAL-Writer against comparable baselines on this dataset. The results demonstrate the effectiveness of our approach. Code is available at https://github.com/OnlyAR/RAL-Writer.
Abstract:Existing long-text generation methods produce lengthy outputs from short inputs, leaving long-input-to-long-output generation underexplored. As input length increases, LLMs increasingly overlook information in the middle of the context--a limitation known as the "lost-in-the-middle" phenomenon--leading to inconsistent and incoherent outputs. To address this problem, we propose Retrieval-Augmented Long-Text Writer (RAL-Writer), a training-free framework consisting of a Planner that generates writing steps and a Writer that produces content according to these steps. The Writer jointly models semantic relevance and positional bias to compute importance scores, dynamically retrieve critical input segments, and strategically restate them. We also construct a benchmark dataset for long-input-to-long-output generation and introduce three evaluation metrics covering length, consistency, and quality. We evaluate RAL-Writer against comparable baselines on this dataset. The results demonstrate the effectiveness of our approach. Code is available at this https URL.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2503.06868 [cs.CL]
(or arXiv:2503.06868v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2503.06868 Focus to learn more arXiv-issued DOI via DataCite
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