| --- |
| 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. |
|
|
