| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| pretty_name: Temporal Jitter |
| task_categories: |
| - question-answering |
| - text-generation |
| tags: |
| - long-context |
| - temporal-reasoning |
| - memory |
| - synthetic |
| - benchmark |
| size_categories: |
| - 1K<n<10K |
| configs: |
| - config_name: raw |
| data_files: raw/chains.jsonl |
| - config_name: grounded |
| data_files: grounded/chains_grounded.jsonl |
| - config_name: narrated |
| data_files: narrated/chains_narrated.jsonl |
| - config_name: qa |
| data_files: qa/qa_pairs.jsonl |
| - config_name: packaged_manifest |
| data_files: packaged/manifest.jsonl |
| --- |
| |
| # Temporal Jitter |
|
|
| **Temporal Jitter** is a long-context benchmark for testing whether a language model can |
| track a *time-varying fact* about an entity when that fact is buried among many unrelated, |
| similarly-phrased **distractor** facts about other entities — i.e., whether the model can |
| find the right needle in a haystack of temporal "jitter." |
|
|
| Each example places one or more **target** facts (e.g. *"X became Y's position holder on |
| date D"*) inside a long context built mostly from **distractor** facts about *other* |
| real-world entities, at controlled context lengths (6K / 32K / 64K tokens) and difficulty |
| levels (single-hop vs. multi-hop, 1–5 revisions per entity). Questions probe the model's |
| ability to recall the *current* value of a fact, the *historical* value at a specific past |
| date, or to *cite the evidence* (which chunk of context stated it). |
|
|
| ## Why this dataset exists |
|
|
| Most long-context QA benchmarks test retrieval of a single static fact. Real-world facts |
| change over time (a person's job title, a country's head of state, an organization's |
| leadership), and a model with working memory needs to track *which value was true when* — |
| not just find *a* mention of the entity. Temporal Jitter isolates this specific failure |
| mode: can the model resist interference from distractor facts that look structurally |
| identical to the target, and still answer correctly about *time*? |
|
|
| ## Dataset structure |
|
|
| The dataset is organized as a 4-stage pipeline, with the final `packaged/` and `qa/` |
| directories being the ones most consumers will want: |
|
|
| ``` |
| raw/ Time-indexed fact "chains" extracted from Wikidata (entity, relation, |
| a sequence of values each with a start/end date), plus extraction |
| statistics (chain_stats.json) and a wikidata_raw/ cache. |
| grounded/ Chains grounded against cached Wikipedia text (wikipedia_cache/). |
| narrated/ Each chain rendered into natural-language narration sentences |
| (e.g. "Gwangjong of Goryeo assumed the position of monarch on |
| January 1, 950."). |
| packaged/ Final long-context examples: one target chain's narration sentences |
| are mixed into a context built mostly from *other* chains' narrations |
| as distractors, packed to a target token budget. Subdirectories are |
| named `{N}_{hop}_{context_variant}`, e.g. `3_multi_32k` = |
| N=3 revisions, multi-hop, ~32K-token context. A manifest.jsonl |
| indexes every packaged file. |
| qa/ Question-answer pairs over the packaged contexts (qa_pairs.jsonl), |
| with three probe types: |
| - current_value: "What position does X hold today?" |
| - historical_value: "What position did X hold on <date>?" |
| - evidence_citation: "Which chunk first stated that X became <value>?" |
| ``` |
|
|
| ### Example packaged context chunk |
|
|
| ```json |
| { |
| "role": "document", |
| "content": "Daniel Cohn-Bendit became chairperson of The Greens-European Free Alliance on January 1, 2004.", |
| "timestamp": "2004-01-01T00:00:00Z", |
| "is_target": false, |
| "is_distractor": true, |
| "source_chain_id": "wd_P488_Q751935", |
| "chunk_id": 0 |
| } |
| ``` |
|
|
| ### Example QA pair |
|
|
| ```json |
| { |
| "qa_id": "wd_P39_Q469387_6k_historical_value_0_wd_P39_Q469387", |
| "chain_id": "wd_P39_Q469387", |
| "hop": "single", |
| "context_variant": "6k", |
| "N": 1, |
| "probe_type": "historical_value", |
| "gold_answer": "monarch", |
| "evidence_chunk_id": 111, |
| "question": "What position did Gwangjong of Goryeo hold on January 1, 950?" |
| } |
| ``` |
|
|
| ## Scale |
|
|
| | Stage | File | Rows | |
| |---|---|---| |
| | raw | `chains.jsonl` | 1,732 | |
| | narrated | `chains_narrated.jsonl` | 1,647 | |
| | packaged | `manifest.jsonl` | 5,829 packaged contexts | |
| | qa | `qa_pairs.jsonl` | 25,692 question-answer pairs | |
|
|
| Packaged contexts span 3 context-length variants (6K / 32K / 64K tokens), single- and |
| multi-hop variants, and 1–7 revision-count groups (`N`), covering 9 Wikidata relations |
| (political/organizational positions, team membership, employer, etc.). |
|
|
| ## Source and licensing |
|
|
| The underlying facts (entity, relation, value, and date ranges) are extracted from |
| [Wikidata](https://www.wikidata.org/), which is released under |
| [CC0](https://creativecommons.org/publicdomain/zero/1.0/). The natural-language |
| narrations, distractor packaging, and QA pairs in this dataset are derived/synthetic |
| content generated for this project and are released under |
| [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). If you use this dataset, |
| please cite Wikidata as the ultimate source of the underlying facts, and this repository |
| for the derived benchmark construction. |
|
|
| ## Loading |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # QA pairs (the usual entry point for evaluation) |
| qa = load_dataset("Tjayush/temporal-jitter", "qa") |
| |
| # Raw time-indexed fact chains |
| raw = load_dataset("Tjayush/temporal-jitter", "raw") |
| |
| # Grounded chains |
| grounded = load_dataset("Tjayush/temporal-jitter", "grounded") |
| |
| # Narrated chains |
| narrated = load_dataset("Tjayush/temporal-jitter", "narrated") |
| |
| # Index of every packaged long-context file (use with hf_hub_download to fetch each one) |
| manifest = load_dataset("Tjayush/temporal-jitter", "packaged_manifest") |
| ``` |
|
|
| Packaged long-context files are per-example JSON files under `packaged/{N}_{hop}_{context_variant}/`, |
| indexed by `packaged/manifest.jsonl`; load the manifest first to iterate over them: |
|
|
| ```python |
| import json |
| |
| with open("packaged/manifest.jsonl") as f: |
| manifest = [json.loads(line) for line in f] |
| |
| # each entry: {"chain_id": ..., "N": ..., "hop": ..., "context_variant": ..., "path": ...} |
| ``` |
|
|
| ## Known caveats |
|
|
| - `.pre_label_repair.bak` files are backups kept from an earlier label-repair pass over |
| `raw/chains.jsonl`, `narrated/chains_narrated.jsonl`, and `qa/qa_pairs.jsonl` — safe to |
| ignore for normal use; kept for provenance/reproducibility. |
| - `chain_stats.json` documents the extraction/filtering funnel (e.g. chains dropped for |
| missing labels, ambiguous dates, or wrong chain length) for transparency about how the |
| raw chains were curated from the full Wikidata extraction. |
|
|
| ## Author |
|
|
| Ayushman Sarkar ([@Tjayush](https://huggingface.co/Tjayush)) |
|
|
