| # Output schema |
|
|
| What your model must emit for the shipped scorer and metrics to accept it. If |
| you use `runner/eval_run.py` you get this for free; this document exists so you |
| can plug in your own inference stack (vLLM, an API, a custom harness) instead. |
|
|
| ## Generations — `outputs/evaluation/<model>.jsonl` |
|
|
| One JSON object per line, **44,416 lines**, one per `query_id`. |
|
|
| ```json |
| { |
| "query_id": "query_00000001", |
| "fact_id": "fact_000602", |
| "condition_family": "anchor", |
| "model": "my-model", |
| "prompt": "Question: Which jurisdiction does Agriculture and Agri-Food Canada have legal force in?\nAnswer with only the shortest correct answer.\nAnswer:", |
| "raw_response": " Canada\nExplanation: Agriculture and Agri-Food Canada (AAFC) is a federal department of the Government of Canada", |
| "generated_tokens": 24, |
| "finish_reason": "length" |
| } |
| ``` |
|
|
| | field | required | notes | |
| |---|---|---| |
| | `query_id` | **yes** | the join key; must match the query bank exactly | |
| | `raw_response` | **yes** | continuation only, *not* including the prompt. Do not strip, lowercase, or truncate it — the scorer needs the raw span, and `finish_reason: "length"` mid-sentence output is normal and handled | |
| | `fact_id`, `condition_family` | recommended | `judge_run.py` groups by these; it can recover them from the query bank but the files are easier to audit with them present | |
| | `model` | recommended | copied onto scored rows | |
| | `prompt` | recommended | keeps each file self-documenting about the template used — the cheapest way for a reader to catch a protocol mismatch | |
| | `generated_tokens`, `finish_reason` | optional | diagnostics | |
|
|
| Order does not matter; the scorer joins on `query_id`. Extra fields are ignored. |
|
|
| ### Prompt construction |
|
|
| Reproduce `runner/eval_run.py:build_prompt` exactly: |
|
|
| ```python |
| PROMPT = "Question: {q}\nAnswer with only the shortest correct answer.\nAnswer:" |
| |
| def build_prompt(row): |
| if row["condition_family"] == "anchor": |
| return PROMPT.format(q=row["query"]) |
| return f"{row['query']}\nAnswer:" |
| ``` |
|
|
| Anchor is the canonical question and gets the instruction wrapper. Every other |
| family already carries its own surface form — that *is* the perturbation — so |
| wrapping it would erase the manipulation; it gets only a bare `Answer:` cue. |
|
|
| **No chat template, for base and instruct models alike.** See the README. |
|
|
| ### Decoding |
|
|
| Greedy, `num_beams=1`, `temperature=0.0`, `max_new_tokens=24`, |
| `max_prompt_len=192` (left truncation, left padding), `dtype=bfloat16`, |
| `seed=20260101`. From `configs/models.yaml:generation`. |
|
|
| ## Scored output — `<model>.scored.jsonl` |
|
|
| Produced by `runner/scoring_full.py`. One line per generation: |
|
|
| ```json |
| { |
| "query_id": "query_00000001", |
| "model": "my-model", |
| "fact_id": "fact_000602", |
| "relation": "applies_to_jurisdiction", |
| "condition_family": "anchor", |
| "language": "en", |
| "target_slot": "object", |
| "answer_type": "place", |
| "answer_granularity": "entity", |
| "answer_in_subject_surface": true, |
| "use_for_main_forward": true, |
| "use_for_reverse_analysis": false, |
| "use_for_recognition_analysis": false, |
| "raw_response": " Canada\nExplanation: ...", |
| "span": "Canada", |
| "flags": [], |
| "label": "correct", |
| "matched_alias": "Canada", |
| "scorer": "exact_alias", |
| "needs_manual_review": false |
| } |
| ``` |
|
|
| `label` is one of `correct` / `incorrect` / `ambiguous` / `abstain` / |
| `unparseable`. `scorer` names the rule that fired, which is what you inspect |
| when a label looks wrong. The per-query booleans are carried through so metric |
| code can filter without rejoining the query bank. |
|
|
| ## Hidden states — `outputs/hidden/<model>/` |
|
|
| Only needed for ISS and KTS. Produced by `metrics/extract_hidden.py`. |
|
|
| ``` |
| L018.npy … L045.npy float16 [n_main_forward_queries, d_model] |
| index.json query order, layer list, checksums, `complete` flag |
| ``` |
|
|
| Rows are in the order given by `index.json`, aligned with the 39,260 queries |
| carrying `use_for_main_forward`. Layers stored are `{l : l/(L−1) ≥ 0.4}`, where |
| `l` indexes decoder blocks and the stored state is the **output** of block `l` |
| (`hidden_states[l+1]` in HuggingFace terms). |
|
|
| The probe position is the **last valid input token** — the model has read the |
| question but has not emitted an answer token. With left padding this is |
| position `-1` for every row in a batch. |
|
|
