---
license: mit
task_categories:
  - text-generation
language:
  - en
tags:
  - long-context
  - data-selection
  - attention
size_categories:
  - 10K<n<100K
---

# Long-Context Data-Selection Bake-off — Shared Candidate Pool

The shared 16K candidate pool for comparing long-context **data-selection methods** on equal
footing. Every method (AttentionSpan, LongAttn, LongProc, ProLong, perplexity, ...) scores the
**same** 14,300 documents, picks its own **top-800** under the **same** split, then trains
Llama-2-7B + 16K LoRA and evaluates on HELMET.

## Files

| File | Description |
|------|-------------|
| `candidate_pool_16k_scored.parquet` | **The shared pool** — 14,300 docs, each ≥16,384 Llama-2 tokens, 7 domains. |
| `selections/as_positive_800.txt` | Reference: AttentionSpan global-top-800 (`doc_id` list). |
| `selections/as_balanced_800.txt` | Reference: AttentionSpan domain-balanced top-800. |
| `selections/random_800_seed42.txt` | Shared random-800 baseline (seed 42). |
| `scripts/longattn_score_full_pool.py` | LongAttn scorer/selector that reads this pool. |
| `pool_manifest.json` | Provenance, score definition, split spec. |

## Pool schema

| Column | Description |
|--------|-------------|
| `doc_id` | `sha1(text)` — stable join key across all selectors |
| `text` | Full document text (score this) |
| `source` | Domain: code / web / arxiv / encyclopedia / books / government / legal |
| `token_length` | Original (Qwen) token length |
| `llama2_token_length` | Llama-2 token length (all ≥16,384) |
| `sequence_avg_median_lookback` | Raw attention lookback (Qwen2.5-Coder-7B, layer 27) |
| `long_token_ratio` | **AttentionSpan score** = `sequence_avg_median_lookback / 16384` |

## Split (identical for every selector)

- Score all 14,300 docs, take the **top-800** by the selector's own score.
- `min_tokens` = 16,384 (Llama-2 tokenizer); seed = 42 for any random draw.
- Reuse `random_800_seed42.txt` as the common baseline row across all methods.

## Training + eval recipe (must match)

Llama-2-7B, 16K via linear RoPE factor 4; LoRA r=16 α=32 on q/k/v/o; 1 epoch, effective batch 8;
HELMET 16K, 22 subtasks (Recall 8 + RAG 8 + LongQA 6), 100 samples/subtask; paired bootstrap B=5,000.