--- license: unknown pretty_name: AetherSearch Eval-1400 language: - en task_categories: - question-answering size_categories: - 1K AetherSearch monogram # 🔭 AetherSearch Eval-1400 ### One frozen benchmark for training-time evaluation and final checkpoint assessment

1,400 questions 7 source datasets Frozen v1 release Sampling seed 42 Zero normalized training-question overlap

[🏠 Project](https://github.com/Muradil-mamat-211/AetherSearch) · [🎓 SFT Data](https://huggingface.co/datasets/muradil211/AetherSearch_SFT) · [🤖 SFT Model](https://huggingface.co/muradil211/AetherSearch_SFT) · [⚖️ DPO Data](https://huggingface.co/datasets/muradil211/AetherSearch_DPO) · [🧠 DPO Model](https://huggingface.co/muradil211/AetherSearch_DPO) --- ## Dataset overview **AetherSearch Eval-1400** is a frozen, 1,400-question evaluation suite for agentic search. It combines seven official held-out QA sources and isolates their questions from the audited AetherSearch SFT, DPO, and RL training inputs. **This is the dataset for `SFT_eval`: the same 1,400 questions are used both during SFT training and after training finishes.** The frozen release also provides a common benchmark for subsequent Base, SFT, DPO, and RL checkpoint comparisons. The Hub exposes one split, **`validation`**, backed by `eval_1400.jsonl`. Training-time and final evaluation reuse that exact split, question order, and gold aliases. A final score on this suite is therefore a score on the shared evaluation benchmark; this release does not supply a separate final holdout. ## Evaluation contract | When | Use | |---|---| | During SFT training | Periodic `SFT_eval` of intermediate checkpoints on the frozen `validation` split | | After SFT training | Evaluate the completed SFT checkpoint on the same full split | | Base / SFT / DPO / RL comparison | Reuse the same revision, 1,400 question IDs, gold aliases, and evaluation settings | | Agent rollout | Supply only `id` and `question` from the question-only file or projection | | Scoring | Keep `answers` in the evaluator and join predictions by `id` | | Evidence diagnostics | Read supporting facts from the separate diagnostic sidecar when auditing | All 1,400 questions remain evaluation data and are excluded from gradient training, preference-pair construction, and RL optimization. Gold aliases and official supporting evidence must stay outside normal agent prompts. The release fixes questions and reference answers. For checkpoint comparisons, also keep the scoring implementation, retrieval corpus, tool budget, prompt, and decoding settings consistent. No model performance results are included. ## Release at a glance | Item | Value | |---|---:| | Evaluation questions | **1,400** | | Source datasets | **7** | | Unique raw / normalized questions | **1,400 / 1,400** | | Hub split | `validation` | | Sampling seed | `42` | | Training-question exclusion union | **186,326** unique normalized questions | | Exact SFT / DPO / RL question overlap | **0 / 0 / 0** | | Internal / cross-dataset duplicates | **0 / 0** | | Records with non-empty gold aliases | **1,400** | | Records with verified provenance | **1,400** | | Known high-confidence training near-duplicates after audit | **0** | | Release revision | `frozen-v1` | ## Source composition | Source dataset | Questions | Official source split | Release variant | |---|---:|---|---| | [NQ](https://github.com/google-research-datasets/natural-questions/tree/fb26a3073b1fe636c97302890a27b491d6530130/nq_open) | 213 | `dev` | NQ-Open | | [TriviaQA](https://huggingface.co/datasets/mandarjoshi/trivia_qa/tree/0f7faf33a3908546c6fd5b73a660e0f8ff173c2f) | 213 | `dev` | `unfiltered.nocontext`; author release calls this `validation` | | [PopQA](https://huggingface.co/datasets/akariasai/PopQA/tree/098765c79ea10a2cb19c828324e33281b8336ec0) | 213 | `test` | Official `test.tsv` | | [HotpotQA](https://huggingface.co/datasets/hotpotqa/hotpot_qa/tree/1908d6afbbead072334abe2965f91bd2709910ab) | 212 | `dev` | Distractor v1; author release calls this `validation` | | [2WikiMultiHopQA](https://github.com/Alab-NII/2wikimultihop/tree/13800e5be57df1b4040b9b1588c6c811779e69e9) | 212 | `dev` | Official April 2021 release and entity aliases | | [MuSiQue](https://github.com/StonyBrookNLP/musique/tree/922ac98f19a201998dbdae6d7f2887a5258dbdeb) | 212 | `dev` | MuSiQue-Ans v1.0, answerable questions | | [Bamboogle](https://github.com/ofirpress/self-ask/blob/559e56ec1ba8dc93cf63c4148ec1f4717caf4b60/datasets/bamboogle.md) | 125 | `eval` | Complete official 125-question evaluation release | | **Total** | **1,400** | **Official held-out partitions only** | | Bamboogle supplies 125 questions. Its 75-question shortfall from the initial 200-per-source proposal is distributed evenly across the other six sources: 12 extra questions each, with the remaining three assigned to NQ, TriviaQA, and PopQA in the fixed source order. The resulting composition is frozen. Each record preserves its original `source_split`; the combined Hub split name `validation` describes how AetherSearch consumes this benchmark. Source revisions, download locations, and source-file SHA-256 values are recorded in `eval_1400_manifest.json`. ## Public schema | Field | Type | Meaning | |---|---|---| | `id` | string | Frozen benchmark ID, `eval1400_0001` through `eval1400_1400` | | `question` | string | Original held-out question text | | `answers` | list[string] | Non-empty official answer aliases for the scorer | | `source_dataset` | string | One of the seven source names above | | `source_split` | string | Original official partition: `dev`, `test`, or `eval` | | `source_id` | string or null | Original example ID when supplied by the source | | `source_row_number` | integer | One-based row location in the pinned source file | | `source_file` | string | Source-file identity within the provenance snapshot | NQ-Open and Bamboogle do not provide original example IDs. Their `source_id` is `null`; row numbers, filenames, and the source hashes in the manifest provide their original identity. The main evaluation file contains questions, gold aliases, and provenance. `eval_1400_rollout_questions.jsonl` contains exactly `id` and `question` for the same 1,400 rows in the same order. The diagnostic file contains supporting metadata only for sources that provide it, joined through the benchmark ID. ## Load with Datasets ```python from datasets import load_dataset benchmark = load_dataset( "muradil211/AetherSearch_Eval_1400", revision="frozen-v1", split="validation", ) assert len(benchmark) == 1400 # Use this same benchmark during SFT training and after training completes. agent_inputs = benchmark.select_columns(["id", "question"]) gold_by_id = {row["id"]: row["answers"] for row in benchmark} # Pass agent_inputs to the rollout runner; keep gold_by_id in the scorer. print(agent_inputs[0]) ``` For a runner that consumes JSONL directly, download the question-only file: ```python from huggingface_hub import hf_hub_download rollout_path = hf_hub_download( repo_id="muradil211/AetherSearch_Eval_1400", repo_type="dataset", revision="frozen-v1", filename="eval_1400_rollout_questions.jsonl", ) ``` ## Curation and integrity The frozen build follows this sequence: 1. Register the current canonical SFT, DPO, and RL training releases and historical versions supported by actual training logs or release evidence. 2. Normalize every training and candidate question with Unicode NFKC, casefold, whitespace collapse, strip, and removal of trailing ASCII or full-width question marks. 3. Remove exact training overlaps, conservative punctuation/template equivalents, and internal or cross-source normalized duplicates. 4. Sample deterministically with seed 42. Candidates are sorted by source ID, normalized question, and original row number before a separate seeded shuffle for each source. Reviewed exclusions trigger the same deterministic selection again; a shortage fails the build. 5. Audit the selected questions against the complete training-question union with exhaustive character-similarity retrieval and rare-token retrieval. Resolve each flagged pair through a conservative question-specific review. 6. Independently reconstruct the exclusion sets from original training files, verify selected questions and aliases against original held-out rows, replay sampling, and verify that training-file hashes stayed unchanged. | Training stage | Audited input rows | Unique normalized exclusion questions | Final exact overlap | |---|---:|---:|---:| | SFT | 60,206 | 30,093 | 0 | | DPO | 18,584 | 15,269 | 0 | | RL | 169,615 | 169,604 | 0 | Stage exclusion sets overlap with one another; their union contains 186,326 questions. The counts include evidenced historical training inputs alongside the canonical releases of 2,600 SFT rows, 2,126 DPO rows, and 169,615 RL rows. Across the 51,713 official candidate rows, isolation removed 877 exact training overlaps, 12 automatic near-duplicates, and 1,216 internal duplicates. Review removed 32 additional near-duplicate candidates. The final 1,400-question scan flagged 33 pairs involving 16 questions; all were resolved with explicit reasons for retaining distinct factual questions. No known high-confidence training near-duplicate remained. Similarity retrieves pairs for review; it is not an automatic semantic deletion rule. Changed entities, years, answer slots, or reasoning targets are retained when they ask different questions. This audit does not claim detection of every possible arbitrary semantic paraphrase. Every exclusion and reviewed decision is recorded in `eval_1400_exclusion_audit.json`. ## Files | File | Purpose | |---|---| | `eval_1400.jsonl` | Canonical 1,400-row evaluation split, with evaluator gold aliases | | `eval_1400_rollout_questions.jsonl` | Same IDs and questions, with exactly two fields for agent input | | `eval_1400_manifest.json` | Frozen composition, pinned official sources, isolation checks, and hashes | | `eval_1400_exclusion_audit.json` | Training-source registry, exclusions, and near-duplicate review decisions | | `eval_1400_diagnostics.jsonl` | Official supporting facts and diagnostic metadata; audit use only | | `independent_validation.json` | Independent source, gold, training-isolation, and replay verification report | | `verify_release.py` | Standalone release-file, row-integrity, and rollout-projection verifier | | `checksums.sha256` | SHA-256 values for all published content files | | `ATTRIBUTION.md` | Upstream attribution and rights-status record | | `assets/aethersearch-mark.svg` | AetherSearch release mark | The benchmark and its five accompanying frozen JSON/JSONL artifacts are published byte-for-byte from the verified local release. The exclusion audit preserves the build-time training-source registry. Raw training files and historical training logs remain outside this repository. ## Checksums and versioning The canonical data SHA-256 is: ```text ecd634aac24013213ceca3523dc024d82544c5479546fe205da24d8e8ffea678 ``` After downloading the repository, verify it without loading a model: ```bash sha256sum -c checksums.sha256 python verify_release.py ``` Pin `revision="frozen-v1"` when comparing checkpoints. Changes to question membership, gold aliases, composition, or sampling require a new benchmark version. This repository publishes the newly curated Eval-1400 suite; the existing [AetherSearch_Eval](https://huggingface.co/datasets/muradil211/AetherSearch_Eval) repository documents the earlier full 51,713-row evaluation release. ## Attribution and rights The upstream QA datasets retain their applicable terms and attribution requirements. This combined release does not assert a blanket license over all source questions, aliases, or supporting metadata; the card therefore uses `license: unknown`, consistent with the AetherSearch SFT/DPO dataset cards. See [ATTRIBUTION.md](ATTRIBUTION.md) and the pinned source references in the manifest.