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HFEPX · Eval paper review

Which Long Documents Matter? A Controlled Study of Data Selection for Context Extension

Junlong Jia, Jiang Zhou, Xing Wu, Chaochen Gao Tinghao Yu +2 more

Published

Oct 17, 2026

Citations

0

Trust level

Low

Usefulness score

5/100 (Low)

Extraction confidence

45% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Oct 17, 2026

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Tags

Automatic Metrics

Should you rely on this paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

Use if you need

A secondary eval reference to pair with stronger protocol papers.

What to verify

Validate the evaluation procedure and quality controls in the full paper before operational use.

Main weakness

This paper looks adjacent to evaluation work, but not like a strong protocol reference.

Human feedback signal
Not explicit
Not explicit in abstract metadata
Evaluation signal
Detected
Eval setup described
Usefulness for eval research
5/100
Adjacent candidate

Treat as adjacent context, not a core eval-method reference.

If you are doing eval pipeline work, start here

Human Eval Papers LLM-as-Judge Papers Pairwise Preference Papers Tool-Use Evaluation Search

Abstract

Natural long documents are widely used for context extension, but which selection pipelines are effective remains unclear. We conduct a controlled comparison on Qwen2.5-3B at 128K under matched 6B-token continued-training budgets. Random sampling of natural long documents (random-long) outperforms repacking at longer evaluation lengths. Within the same candidate pool, selecting documents with high LongPPL key-token density yields further gains, unlike the evaluated generic-quality and LLM-judge selectors. We introduce truncLongPPL to reduce scoring cost by computing key-token density over a truncated prefix. At 128K, scoring the first half of the reference span yields downstream performance close to full-span scoring with a measured $2.28\times$ end-to-end scoring speedup. Across three training seeds, truncLongPPL achieves the highest mean scores among evaluated pipelines, exceeding random-long by 4.54 points on validation HELMET and 3.28 on held-out RULER, with gains most consistent in retrieval. At a 256K target context, it gains 2.82 average RULER points over random-long on Qwen2.5-3B. In short/long mixed training across Qwen2.5-3B, Qwen3-8B, and OLMo~3-7B, it gains 1.47--7.31 average RULER points over random-long mixing, with seven-task short-context averages within 0.23 points of the corresponding baselines. After supervised fine-tuning (SFT), Qwen3-8B and OLMo~3-7B models trained with selected long documents outperform their random-long counterparts by 1.13--1.67 points on LongBench v2 and 0.62--1.44 points on MRCR.

What we could verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"Natural long documents are widely used for context extension, but which selection pipelines are effective remains unclear."

Evaluation Modes

partial

Automatic Metrics

Includes extracted eval setup.

"Natural long documents are widely used for context extension, but which selection pipelines are effective remains unclear."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Natural long documents are widely used for context extension, but which selection pipelines are effective remains unclear."

Benchmarks / Datasets

partial

LongBench

Useful for quick benchmark comparison.

"After supervised fine-tuning (SFT), Qwen3-8B and OLMo~3-7B models trained with selected long documents outperform their random-long counterparts by 1.13--1.67 points on LongBench v2 and 0.62--1.44 points on MRCR."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"Natural long documents are widely used for context extension, but which selection pipelines are effective remains unclear."

Benchmarks and datasets

LongBench

Reported metrics

No metric terms were extracted from the available abstract.

Human feedback details
Uses human feedback
No
Feedback types
None
Rater population
Not reported
Expertise required
General
Evaluation details
Evaluation modes
Automatic Metrics
Agentic eval
None
Quality controls
Not reported
Evidence quality
Low
Use this page as
Background context only

Research brief

Metadata summary

Natural long documents are widely used for context extension, but which selection pipelines are effective remains unclear.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key takeaways

  • Natural long documents are widely used for context extension, but which selection pipelines are effective remains unclear.
  • We conduct a controlled comparison on Qwen2.5-3B at 128K under matched 6B-token continued-training budgets.
  • Random sampling of natural long documents (random-long) outperforms repacking at longer evaluation lengths.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended queries

Contribution summary

  • Random sampling of natural long documents (random-long) outperforms repacking at longer evaluation lengths.
  • Within the same candidate pool, selecting documents with high LongPPL key-token density yields further gains, unlike the evaluated generic-quality and LLM-judge selectors.
  • We introduce truncLongPPL to reduce scoring cost by computing key-token density over a truncated prefix.

Why it matters for eval

  • Random sampling of natural long documents (random-long) outperforms repacking at longer evaluation lengths.
  • Within the same candidate pool, selecting documents with high LongPPL key-token density yields further gains, unlike the evaluated generic-quality and LLM-judge selectors.

Researcher checklist

  • Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Evaluation mode is explicit

    Detected: Automatic Metrics

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    Detected: LongBench

  • Metric reporting is present

    No metric terms extracted.