dataset_upload/README.md

---
license: cc-by-4.0
task_categories:
  - question-answering
  - text-generation
language:
  - en
  - zh
  - fr
  - es
  - de
  - ru
size_categories:
  - 10K<n<100K
pretty_name: Fact Knowledge Stability Benchmark
tags:
  - factual-knowledge
  - robustness
  - consistency
  - interpretability
  - multilingual
configs:
  - config_name: queries
    default: true
    data_files: data/evaluation_queries_44416.jsonl
  - config_name: facts
    data_files: data/benchmark_facts_2592.jsonl
---

# Fact Knowledge Stability Benchmark

**2,592 facts × 44,416 queries.** Does a model that knows a fact still know it
when you rephrase the question, change the answer format, add distracting
context, or ask in another language?

The benchmark pairs **behavioural** stability (what the model *says* across
perturbations) with **internal** stability (what its residual stream *does*
across the same perturbations), so the two can be compared on identical inputs.

```python
from datasets import load_dataset

queries = load_dataset("LucasLoading/stable", "queries", split="train")
facts   = load_dataset("LucasLoading/stable", "facts",   split="train")
```

---

## Read this before you report a number

Four properties of this benchmark will silently distort results if you do not
account for them. They are design decisions, not defects, and each is flagged
per-row in the data.

**1. The facts were selected to be easy.** Candidates were kept only if **all
five** filter models (Gemma-2-2B-it, Qwen2.5-7B, Mistral-7B-v0.3, Llama-3.1-8B,
Gemma-2-9B-it) produced the correct first token. That is **10,601 of 1,783,541
cloze prompts — 0.59%**. This is deliberate: to attribute instability to
*expression* rather than to *ignorance*, the model has to know the fact in the
first place. The consequence is that absolute stability scores run high, and
that the four filter models present in a comparison enjoy a selection advantage
over models that had no say in what was kept.

**2. 750 of 2,592 facts (28.9%) are answerable by copying the subject string.**
`Airbus A318 → manufacturer → Airbus`. Every fact and every query carries
`answer_in_subject_surface`; report those two subsets separately.

**3. Only four of the eight condition families belong in the main average.**
Use each row's own boolean, never a hard-coded family list:

| flag | rows | meaning |
|---|---:|---|
| `use_for_main_forward` | 39,260 | anchor + paraphrase + format + context + multilingual |
| `use_for_reverse_analysis` | 634 | target slot is the **subject**; report separately |
| `use_for_recognition_analysis` | 3,412 | diagnostic only |
| *(none of the above)* | 1,110 | `reverse_illposed`; diagnostic only |

**4. `anchor` is the unperturbed baseline, and it holds exactly one query per
fact.** Its within-family agreement is therefore 1.0 by construction. Including
it as a fifth equally-weighted family puts a floor under any family-balanced
consistency score. `configs/metrics.yaml:main_families` controls this; drop
`anchor` from that list to score the four perturbation families only.

---

## Contents

```
data/
  benchmark_facts_2592.jsonl        2,592 facts / 21 relations
  evaluation_queries_44416.jsonl    44,416 queries / 8 condition families / 6 languages
configs/
  metrics.yaml         metric hyper-parameters — layer window, whitening, tau_b, bootstrap
  models.yaml          the 20 evaluated models + decoding config + judge
  relations.yaml       21 relations, with the two optional exclusion switches
  eval_conditions.yaml condition registry the three booleans are derived from
protocol/
  evaluation_protocol.md          BCS/BES (§4-6), ISS (§7), KTS (§8-11)   [Chinese]
  jlens_spec.md                   Jacobian-transported ISS                [Chinese]
  dataset_construction.md         how the benchmark was built (archival)  [Chinese]
  reconstruction_differences.md   documented deviations D1, C1, F1, F2, N1, N2
runner/
  eval_run.py          generation  -> outputs/evaluation/<model>.jsonl
  scoring_full.py      five-label scorer (correct/incorrect/ambiguous/abstain/unparseable)
  test_scoring.py      31 unit tests for the scorer
  common.py
metrics/
  extract_hidden.py    query-end residual states -> outputs/hidden/<model>/
  iss.py               Internal State Stability
  kts.py               Knowledge Topology Stability (KTS-Geo + KTS-ID)
  judge_run.py         semantic clustering of answers (needs a judge model)
  bcs_bes.py           BCS / BES / behaviour classification
  joint.py             internal x external correlation
  make_table.py        main table
  jlens.py states.py mcommon.py
OUTPUT_SCHEMA.md       what your model must emit to be scorable
```

**Not included:** the upstream corpora, the dataset build scripts, and the
hidden-state tensors (1.6–15 GB per model, regenerate with `extract_hidden.py`).

---

## Condition families

| family | queries | facts | role |
|---|---:|---:|---|
| anchor | 2,592 | 2,592 | unperturbed baseline (1 per fact) |
| paraphrase | 10,053 | 2,592 | main |
| format | 7,776 | 2,592 | main |
| context | 6,829 | 2,591 | main |
| multilingual | 12,010 | 2,402 | main (zh/fr/es/de/ru) |
| reverse | 634 | 253 | subject slot; reported separately |
| recognition | 3,412 | 2,585 | diagnostic |
| reverse_illposed | 1,110 | 1,110 | diagnostic |

Coverage is ragged on purpose — multilingual covers 2,402 facts, context 2,591 —
so both readings are mandatory (`coverage_modes` in `configs/metrics.yaml`):
`complete_family` (facts carrying all five) and `full_set` (each fact averaged
over the families it actually has).

---

## Evaluating your own model

Everything below is CPU-only except `eval_run.py`, `extract_hidden.py` and
`judge_run.py`.

```bash
pip install -r requirements.txt

# optional: keep large outputs off the checkout
export FKS_OUTPUTS=/path/to/large/disk/fks_work

# 1. generate  (44,416 queries)
python runner/eval_run.py --model my-model --model-path org/my-model

# 2. score to five labels  ->  outputs/evaluation/my-model.scored.jsonl
python runner/scoring_full.py --gen outputs/evaluation/my-model.jsonl

# 3. your own metrics on top of the labels, or the ones shipped here:
python metrics/judge_run.py    --model my-model      # semantic clustering [GPU]
python metrics/bcs_bes.py      --model my-model      # BCS / BES / behaviour
python metrics/extract_hidden.py --model my-model    # residual states     [GPU]
python metrics/iss.py          --model my-model --transport raw --device cpu
python metrics/kts.py          --model my-model --transport raw --device cpu
```

A model not listed in `configs/models.yaml` needs either an entry there or
`--model-path`; `metrics/*.py` read `n_layers`/`d_model` from that file, so add
an entry before running the internal metrics.

### The decoding protocol is part of the benchmark

Numbers are not comparable unless generation matches
`configs/models.yaml:generation` exactly:

```yaml
do_sample: false          # greedy
num_beams: 1
temperature: 0.0
max_new_tokens: 24
max_prompt_len: 192       # effective value; the 96 in models.yaml is raised by eval_run.py
dtype: bfloat16
use_chat_template: false  # <-- base and instruct get the IDENTICAL raw string
seed: 20260101
```

`use_chat_template: false` is not an oversight. Four of the twenty models are
instruction-tuned; applying a chat template to only those would confound
post-training with prompt format. The prompt itself is built by
`runner/eval_run.py:build_prompt` — anchor gets the instruction wrapper,
perturbed queries carry their own surface form (that *is* the perturbation) and
receive only a bare `Answer:` cue. `extract_hidden.py` imports that same
function rather than re-deriving it, so ISS and BCS are always measured on the
identical prompt.

### Share the scorer, not just the data

`runner/scoring_full.py` is shipped so that label boundaries — negation
handling, year granularity, alias matching — are identical across submissions.
Re-implementing the five-label extractor makes numbers incomparable in ways that
are very hard to see. Run `python runner/test_scoring.py` (31 tests) to confirm
it behaves the same on your machine.

---

## Metrics

**External** (from `runner/` + `metrics/judge_run.py` + `metrics/bcs_bes.py`):

- **BCS** — family-balanced maximum probability over answer clusters. Graded,
  and note it has a high floor: a model choosing at random between two answers
  scores ≈0.61, not 0.5.
- **BES** — entropy-based, `1 - H(p)/log A`. Chance floor ≈0.06.
- **Behaviour classes** — Stable Correct / Stable Wrong / Stable Abstention /
  Unstable, at a pre-registered `tau_b = 0.8` with 0.7/0.9 sensitivity reported.

**Internal** (from `metrics/extract_hidden.py` + `iss.py` + `kts.py`):

- **ISS** — same-fact cross-condition similarity, corrected against
  same-relation background: `(S⁺ − S⁻)/(1 − S⁻)`. 0 means "no better than
  background".
- **KTS-Geo** — Spearman between within-relation pairwise distance matrices
  under two conditions; invariant to rotation, translation and isotropic
  scaling. Reported as `(ρ+1)/2` in the summary files, so **its floor is 0.5** —
  use the `kts_geo_raw_spearman` field for anything scale-sensitive.
- **KTS-ID** — chance-corrected cross-condition nearest-neighbour retrieval of
  the fact itself among same-relation facts.

Layer window: `{l : l/(L−1) ≥ 0.4}` (protocol §7.10). Layer `l` is the *output*
of decoder block `l`, i.e. `hidden_states[l+1]` in HuggingFace indexing. The
`jlens_window` integers in `models.yaml` are a redundant assertion checked at
extraction time, not the definition.

`metrics/judge_run.py` clusters answers with an instruction-tuned judge
(default `Qwen/Qwen2.5-32B-Instruct`, ~65 GB). It shares pretraining lineage
with the four Qwen models in the evaluated set and may favour their phrasing;
`configs/models.yaml:auxiliary_models.judge.known_bias` records the mitigation.
The judge is never itself evaluated.

---

## Data schema

`benchmark_facts_2592.jsonl`

| field | type | notes |
|---|---|---|
| `fact_id` | str | `fact_000602` |
| `subject`, `object` | obj | `canonical`, `entity_id` (Wikidata Q-id), `aliases[]` |
| `relation` | obj | `relation_id`, `source_relations[]`, `direction` |
| `qualification_question` | str | canonical question; identical to the anchor query |
| `sources`, `source_records` | list | provenance |
| `is_functional` | bool | single-valued; true for all benchmark facts |
| `answer_type`, `answer_granularity` | str | drives the scorer's year/entity handling |
| `answer_in_subject_surface` | bool | **750 true** — see caveat 2 |
| `spec_6_2_flag` | bool | 846 true (`developer of` / `manufacturer of`) |

`evaluation_queries_44416.jsonl`

| field | type | notes |
|---|---|---|
| `query_id`, `fact_id`, `relation` | str | |
| `condition_family`, `variant_id`, `language` | str | |
| `query` | str | the surface form actually shown to the model |
| `target_slot` | str | `object`, or `subject` for reverse |
| `gold_canonical`, `gold_aliases[]` | str/list | what the scorer accepts |
| `is_assisted`, `is_well_posed` | bool | |
| `use_for_main_forward` / `_reverse_analysis` / `_recognition_analysis` | bool | see caveat 3 |
| `answer_in_subject_surface` | bool | inherited from the fact |

---

## Known deviations

Recorded in full in `protocol/reconstruction_differences.md`:

- **D1** — two `context` queries whose distractor literally contained the gold
  string were removed by a rule-based guard: 44,418 → **44,416**. The spec
  counts in `configs/eval_conditions.yaml` still read 44,418 and are reconciled
  in that file's `accepted_deviations` block.
- **C1** — `developer of` / `manufacturer of` (846 facts) are flagged rather
  than dropped; `relations.yaml:drop_spec_6_2_flagged` produces the compliant
  1,746-fact / 19-relation variant.
- **F1** — the 28.9% copy-answerable subset (caveat 2).
- **F2** — 808 exactly duplicated triples among the 8,107 candidates, absorbed
  during entity-aware grouping.
- **N2** — `capital_of` direction is not normalised; annotated explicitly with
  `direction: inverse` + `inverse_of: capital`.

## Licence and attribution

Released under **CC BY 4.0**, inheriting the terms of its sources. Facts derive
from **CounterFact** (MIT), **LAMA / T-REx** (CC BY 4.0), **LAMA / Google-RE**,
**LAMA / ConceptNet**, and **PopQA** (MIT); entity ids and aliases come from
**Wikidata** (CC0). Please cite the original datasets alongside this one.