README.md

---
license: cc0-1.0
language:
- en
size_categories:
- n<1K
task_categories:
- text-generation
tags:
- unlearning
- machine-unlearning
- knowledge-editing
- model-editing
- benchmark
- wikidata
pretty_name: Fine-Grained Knowledge Unlearning (Namesake Benchmark)
configs:
- config_name: default
  data_files:
  - split: train
    path: dataset.csv
- config_name: scored
  data_files:
  - split: train
    path: dataset_scored.csv
---

# Fine-Grained Knowledge Unlearning — Namesake Benchmark

A benchmark for **fine-grained knowledge unlearning**: can a method remove a fact
about entity **X** without damaging the *same fact* on entity **Y**, when X and Y
have (near-)identical names and share exactly that one attribute?

Each sample is a pair of real people who

- share an identical or near-identical name,
- share **one** career element (e.g. both are basketball players) — the fact to
  unlearn on X and retain on Y,
- differ on everything else (birth year, birthplace, teams, works, …) — the
  disambiguators used to point a model at the right namesake,
- are approximately equally famous (Wikipedia pageview ratio ≤ 20, both ≥ 1,500
  annual views, plus a probe-model log-prob balance term).

The point is that a coarse unlearning method will delete "the Michael Jordan who
plays basketball" rather than "*this* Michael Jordan", and the retain side
measures exactly that collateral damage.

**Example.** Unlearn *"James Allen (b. 1864) → writer"*, then check that
*"James Lane Allen, born in 1849, is a ___"* still yields *writer*.

## Files

| file | rows | what it is |
|---|---|---|
| `dataset.csv` | 247 | the benchmark — one row per pair, X = more famous (unlearn target), Y = less famous (retention probe) |
| `dataset_scored.csv` | 1,701 | **every** candidate pair with probe-model scores, before the final filter — re-cut at your own thresholds without re-running the model |
| `unlearning_dataset.json` | 247 | the same 247 pairs in EasyEdit-style records (download directly; not a `configs` entry) |

```python
from datasets import load_dataset

ds = load_dataset("ernlavr/fine_grained_unlearning")            # the 247 pairs
scored = load_dataset("ernlavr/fine_grained_unlearning", "scored")  # all 1,701 scored
```

`unlearning_dataset.json` holds nested records, so fetch it as a file:

```python
import json
from huggingface_hub import hf_hub_download

p = hf_hub_download("ernlavr/fine_grained_unlearning",
                    "unlearning_dataset.json", repo_type="dataset")
records = json.load(open(p))
```

```json
{
  "case_id": 0,
  "pair_id": "Q1371577_Q3297881",
  "unlearn": {"subject", "prompt", "disambiguated_prompt", "paraphrases", "target"},
  "retain":  {"subject", "prompt", "paraphrases", "target"},
  "disambiguation_check": {"x": {"prompt", "target"}, "y": {"prompt", "target"}},
  "metadata": {"qid_x", "qid_y", "name_match_type", "career_category",
               "family_relation", "fame_ratio", "scores": {...}}
}
```

## Schema (`dataset.csv`)

**Identity** — `pair_id`, `qid_x`, `qid_y` (Wikidata QIDs), `name_x`, `name_y`,
`description_x`, `description_y`, `birth_year_x`, `birth_year_y`.

**Pair properties** — `name_match_type` (see below), `shared_occupation` (the
fact to unlearn/retain), `career_category`, `family_relation`.

**Fame** — `pageviews_x`, `pageviews_y` (12-month enwiki, 2025-07 → 2026-07),
`sitelinks_x`, `sitelinks_y`, `fame_ratio` (max/min pageviews).

**Prompts** — `unlearn_prompt` (bare name), `unlearn_prompt_disambiguated`,
`unlearn_target`, `retain_prompt`, `retain_target`, `hint_attr`, `hint_x`,
`hint_y`, `probe_attr`, `probe_{x,y}_prompt`, `probe_{x,y}_target`,
`probe_options`.

**Probe scores** — `know_{x,y}`, `margin_{x,y}`, `shared_{x,y}`, `shared_ambig`,
`shared_margin_{x,y}`, `balance`, `quality`.

Two gotchas:

- `probe_options` is a **JSON-encoded string** — `json.loads` it. It holds every
  candidate `[attr, value_x, value_y]` triple; the pipeline picked the one with
  the best worst-case margin and exposed it as `probe_attr`.
- `family_relation` is an **empty string** when absent (pandas reads it as NaN).
  Filter on specific values. **`different_from` is not a family relation** — it
  is Wikidata's explicit distinctness marker (P1889), i.e. *positive* evidence
  the two items are different people. Of the 247 pairs: 41 are blood/marital
  relatives (`child` 22, `father` 14, `sibling` 4, `spouse` 1), 23 are
  `different_from`, 183 unmarked. Drop the 41 if a shared family context would
  confound your experiment.

## Score definitions

All scores are **mean per-token log-probs** of the gold continuation under the
probe model (`meta-llama/Llama-3.1-8B`, 39,943 continuations scored).

| column | meaning |
|---|---|
| `know_x`, `know_y` | log-prob of the correct differing attribute given the disambiguated prompt. High ⇒ the model knows the entity *and* resolves the right namesake from the hint. |
| `margin_x`, `margin_y` | `know` minus the log-prob of the **partner's** value on the same prompt. Positive both ways ⇒ the two representations are separable pre-edit (otherwise the retention eval is meaningless). |
| `shared_x`, `shared_y` | strength of the shared occupation fact — the unlearn/retain target. |
| `shared_ambig` | occupation log-prob on the **bare-name** prompt (name-level prior, no disambiguation). |
| `shared_margin_x/y` | `shared` minus the best of 8 distractor occupations. |
| `balance` | \|`know_x` − `know_y`\| — representation-level fame balance. |
| `quality` | the pipeline's ranking score (capped margins − balance). |

The published 247 were cut with `margin_{x,y} > 0` **and**
`shared_margin_{x,y} > 0`, ranked by `quality`, capped at 2 pairs per unlearn
entity. **See Limitations — that threshold is too loose, and you probably want
to re-cut.**

## Name closeness (`name_match_type`)

| value | meaning | example | n |
|---|---|---|---|
| `edit2` | edit distance 2 inside one token, shared 2-char prefix | Steven / Stephen | 63 |
| `initial` | added middle name/initial | James Allen / James Lane Allen | 53 |
| `edit1` | edit distance 1 | Isiah Thomas / Isaiah Thomas | 50 |
| `suffix` | Jr/Sr/II/III variants | Larry Nance / Larry Nance Jr. | 33 |
| `exact` | identical labels | Mark Davis / Mark Davis | 31 |
| `middle_variant` | different middle token | George E. Johnson / George L. Johnson | 17 |
| `diacritic` | differ only in diacritics/punctuation | José López / Jose Lopez | 0 |

## Composition

**Career category** — literature 45, football 38, basketball 36, acting 35,
music 32, politics 27, filmmaking 9, visual_art 5, baseball 4, law 3, then a
tail of 11 categories with ≤ 2 pairs each.

**Probe attribute** — birthplace 186, citizenship 35, team 14, birth_year 10,
work 2.

**Fame balance** — `fame_ratio` mean 4.86, median 2.85, max 19.56; the least
viewed entity has 1,505 annual pageviews.

## ⚠️ Limitations — read before using

An audit of the published 247 pairs generated greedily from the probe model and
scored the output with a synonym map (so "jurist" counts for `lawyer`). **The
`usable` figures below are that heuristic, not human-verified ground truth** —
treat them as directional.

**~45% of the 247 pairs fail the benchmark's core premise**, i.e. the model does
not demonstrably hold the fact you are asking a method to unlearn:

- both occupations produced correctly: **150/247 (61%)**
- cross-contamination (X's generation leaks Y's attribute or vice versa): **9.3%** — low; the disambiguation hints do work
- usable (occupation right for both **and** no leak): **136/247 (55%)**
- for 37 pairs (15%) the model gets the occupation wrong for *both* people

**The published `> 0` threshold is the cause, and it is cheap to fix.** A
stricter cut on the *same* log-prob columns raises quality sharply, and because
`dataset_scored.csv` ships all 1,701 scored pairs, re-cutting needs no GPU:

| gate | pairs | usable |
|---|---|---|
| `shared_margin > 0 & margin > 0` (as published) | 247 | 55% |
| `shared_margin > 1 & margin > 0.5` | 104 | 73% |
| **`shared_margin > 2 & margin > 0.5`** (recommended) | **69** | **78%** |
| `shared_margin > 2 & margin > 1` | 50 | 80% |

```python
import pandas as pd
s = pd.read_csv("dataset_scored.csv")
smin = s[["shared_margin_x", "shared_margin_y"]].min(axis=1)
mmin = s[["margin_x", "margin_y"]].min(axis=1)
strict = s[(smin > 2) & (mmin > 0.5)]     # 69 pairs
```

Of the three log-prob signals, `shared_margin` is the best predictor of a usable
pair (correlation 0.352), ahead of `margin` (0.160). A "regret" metric
(`logp(gold) − logp(model's own greedy continuation)`) was also tested and did
**worse** (0.244), so it is not included.

**A residual ~20% is a data problem no threshold can fix.** Wikidata's `P106`
(occupation) includes peripheral tags, so the "shared career" is sometimes one
neither person is actually known for — **John Smith the explorer is tagged
`writer`; William Clark of Lewis & Clark is tagged `politician`**. This is
strongly category-dependent:

| category | pairs | usable |
|---|---|---|
| music | 32 | 84% |
| visual_art | 5 | 80% |
| acting | 35 | 66% |
| basketball | 36 | 56% |
| literature | 45 | 47% |
| football | 38 | 42% |
| politics | 27 | 41% |
| filmmaking | 9 | 33% |

Literature is both the **largest** category (45 pairs) and near the bottom —
`writer` is Wikidata's worst catch-all. A cheap extra filter is to require
`shared_occupation` to appear in *both* Wikidata one-line descriptions
(`description_x`/`description_y`), which splits usability 62% vs 44%.

**Other caveats.** The fame filter is purely *relative* (ratio ≤ 20) with no
absolute ceiling, so 8 pairs have `pageviews_x > 1M` — including Donald Trump /
Donald Trump Jr. under `businessperson`, a career neither is primarily known
for. Scores are specific to Llama-3.1-8B; re-probe for another model. English
Wikipedia only, so fame and coverage are anglophone-biased, and the underlying
Wikidata gender/occupation distribution is inherited unchanged.

## Provenance

Built from [Wikidata](https://www.wikidata.org) (CC0) via WDQS SPARQL, plus the
[Wikimedia Pageviews API](https://wikimedia.org/api/rest_v1/) for fame
estimation (12 months, 2025-07-01 → 2026-07-01). Pipeline: mine humans per
occupation (≥ 5 sitelinks) → pair by name closeness within an occupation →
fetch attributes and family links → filter for fame balance, single shared
career and sufficient disambiguators → score with Llama-3.1-8B and cut.

11,024 candidate pairs → 5,168 after a sitelink prefilter → 1,701 after
attribute/fame filtering → 247 published.

## Licence

CC0-1.0, following Wikidata. Pageview counts are aggregate Wikimedia statistics.
All entities are public figures with English Wikipedia articles; every attribute
is drawn from public Wikidata statements.