arXiv AI By Junhao Zhang, Richong Zhang, Fanshuang Kong, Ziyang Miao, Yanhan Ye, Yaowei Zheng

Lost-in-the-Middle in Long-Text Generation: Synthetic Dataset, Evaluation Framework, and Mitigation

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arXiv Machine Learning
Aug 27

Unfolding Scientific Papers into Multi-Turn Generation Trajectories for Continued Pre-Training

arXiv:2608. 25826v1 Announce Type: cross Abstract: A recent line of synthetic-data work reconstructs the thinking behind existing text rather than rewriting the text itself, but it operates on short web passages, recovers only local thoughts, and leaves the structure of whole documents untouched.

By Qiankai Xu, Qiguang Chen, Zixin Su, Wenhao Huang, Yue Gao, Jiaheng Liu, Ge Zhang
arXiv AI
Aug 28

A Multi-Framework Comparison of Outline Stages in Long-Form Generation with LLMs

The paper presents a benchmark that compares seven long‑form generation frameworks across three granularities—single chapter, multi‑chapter, and whole book—using an anchor‑based LLM‑as‑a‑judge protocol to evaluate outlines directly. Results show no single framework dominates across all settings; performance depends on how well a framework’s output form matches the target granularity, with SuperWriter excelling in length‑constrained single‑chapter mode but losing advantage in whole‑book mode. The study finds only moderate correlation between outline and writing quality, supporting the idea that these two stages should be evaluated separately.

By Yifan Song
arXiv Machine Learning
Sep 10

Retrieval-augmented Decoding for Improving Truthfulness in Open-ended Generation

The paper introduces Retrieval-Augmented Decoding (RAD), a decoding-time method that improves the truthfulness of large language models without retraining. RAD uses a small reference set of up to ten annotated examples to build a grounding space of context embeddings and next-token logits, which it retrieves and aggregates during inference to shape the model’s output. Experiments on four open-ended generation benchmarks and four different LLMs show that RAD consistently outperforms strong baselines and generalizes well across tasks.

By Manh Nguyen, Sunil Gupta, Hung Le
arXiv Machine Learning
Sep 11

REVA: Reusable Evidence View Aggregation for Context-Efficient RAG Serving

The paper introduces REVA, a method for compressing retrieval-augmented generation (RAG) prompts by aggregating historical query–document–model interactions into reusable evidence views. REVA mines attention traces from the target generator, maps token-level attention to readable words, aggregates importance across repeated document accesses, and produces budget‑specific plain‑text views that maintain document order and the standard RAG interface. Experiments on four benchmarks with modern LLMs show that REVA improves generation quality by 1.0–5.8 points over existing compressors while reducing compression overhead by 5.3 to 15.6 times and adding less than 40 ms of latency.

By Tuan Nguyen, Qiran Hu, Banruo Liu, Khoa D. Doan, Kok-Seng Wong, Fan Lai