---
license: cc-by-4.0
language:
- en
pretty_name: LexVE
size_categories:
- 1K<n<10K
task_categories:
- text-classification
tags:
- minimal-pairs
- interpretability
- mechanistic-interpretability
- linguistics
- value-embeddings
- causal-intervention
configs:
- config_name: default
  default: true
  data_files:
  - split: discovery
    path: data/default/discovery.parquet
  - split: evaluation
    path: data/default/evaluation.parquet
- config_name: graded_distance
  data_files:
  - split: discovery
    path: data/graded_distance/discovery.parquet
  - split: evaluation
    path: data/graded_distance/evaluation.parquet
dataset_info:
- config_name: default
  features:
    - name: anchor
      dtype: string
    - name: base_class
      dtype: string
    - name: base_label
      dtype: string
    - name: base_label_id
      dtype: int64
    - name: base_text
      dtype: string
    - name: base_token_id
      dtype: int64
    - name: base_word
      dtype: string
    - name: calibration_status
      dtype: string
    - name: class_labels
      dtype: bool
    - name: cluster_id
      dtype: string
    - name: cue_gap
      dtype: int64
    - name: direction
      dtype: string
    - name: frame
      dtype: string
    - name: id
      dtype: string
    - name: linguistic_level
      dtype: string
    - name: pair_id
      dtype: string
    - name: pair_index
      dtype: int64
    - name: pos_tag
      dtype: string
    - name: schema_version
      dtype: string
    - name: source_class
      dtype: string
    - name: source_label
      dtype: string
    - name: source_label_id
      dtype: int64
    - name: source_text
      dtype: string
    - name: source_token_id
      dtype: int64
    - name: source_word
      dtype: string
    - name: split
      dtype: string
    - name: swap_pos
      dtype: int64
    - name: task
      dtype: string
    - name: template_id
      dtype: string
    - name: template_text
      dtype: string
    - name: tier
      dtype: string
    - name: token_count
      dtype: int64
    - name: known_issue
      dtype: bool
    - name: known_issue_note
      dtype: string
  splits:
  - name: discovery
    num_examples: 1376
    num_bytes: 64340
  - name: evaluation
    num_examples: 1294
    num_bytes: 62310
- config_name: graded_distance
  features:
    - name: base_class
      dtype: string
    - name: base_label
      dtype: string
    - name: base_label_id
      dtype: int64
    - name: base_text
      dtype: string
    - name: base_token_id
      dtype: int64
    - name: base_word
      dtype: string
    - name: class_labels
      dtype: bool
    - name: cluster_id
      dtype: string
    - name: cue_gap
      dtype: int64
    - name: direction
      dtype: string
    - name: filler_tokens
      dtype: int64
    - name: frame
      dtype: string
    - name: id
      dtype: string
    - name: linguistic_level
      dtype: string
    - name: pair_id
      dtype: string
    - name: pair_index
      dtype: int64
    - name: pos_tag
      dtype: string
    - name: schema_version
      dtype: string
    - name: source_class
      dtype: string
    - name: source_label
      dtype: string
    - name: source_label_id
      dtype: int64
    - name: source_text
      dtype: string
    - name: source_token_id
      dtype: int64
    - name: source_word
      dtype: string
    - name: split
      dtype: string
    - name: swap_pos
      dtype: int64
    - name: task
      dtype: string
    - name: template_id
      dtype: string
    - name: template_text
      dtype: string
    - name: tier
      dtype: string
    - name: token_count
      dtype: int64
    - name: known_issue
      dtype: bool
    - name: known_issue_note
      dtype: string
  splits:
  - name: discovery
    num_examples: 1120
    num_bytes: 46034
  - name: evaluation
    num_examples: 1070
    num_bytes: 44251
---

# LexVE: lexical minimal pairs for word-level interventions

LexVE is a set of **2,670 English minimal-pair prompts across 38 tasks**. In each pair the two prompts differ in exactly
one word (the *cue*), and that word decides the next token. It was built to study how language models use a single word's
identity, for example by swapping or steering that word's embedding or value-embedding vectors, and it works as a plain
minimal-pair benchmark too.

```python
from datasets import load_dataset

ds = load_dataset("aadityabuilds/lexve")                       # LexVE v2: splits "discovery", "evaluation"
graded = load_dataset("aadityabuilds/lexve", "graded_distance")  # cue-to-prediction distance 0-14 tokens
row = ds["evaluation"][0]
```

Example row (`animacy_relative`):

| | prompt | cue | expected next token |
|---|---|---|---|
| base | We finally met the doctor, | doctor (human) | " who" |
| source | We finally met the statue, | statue (inanimate) | " which" |

A model that has learned the distinction should prefer " who" after the base prompt and " which" after the source prompt.
An intervention on the cue (e.g. replacing the doctor's vector with the statue's) should move the prediction from " who"
toward " which".

## Configurations

| Config | Rows | Discovery / evaluation | What it is |
|---|---|---|---|
| `default` | 2,670 | 1,376 / 1,294 | LexVE v2: 38 tasks, `near` / `far` / `attractor` frames |
| `graded_distance` | 2,190 | 1,120 / 1,070 | 19 tasks; one fixed stem per task with filler phrases so the cue sits 0, 4, 7, 10 or 14 tokens before the prediction |

Both configs reuse the same lexical pairs and the same discovery/evaluation assignment.

## Tasks

The 38 tasks cover cues of 13 parts of speech and eight linguistic levels.

| Task | Level | Cue POS | Tier | Status | Labels (class A / B) | Pairs | Rows | Graded rows | Example (base → label) |
|---|---|---|---|---|---|---|---|---|---|
| `adj_prep_of_with` | lexicalized_syntax | adjective | open | capable | `of` / `with` | 17 | 136 | 170 | Everyone knew she was afraid → `of` (vs *satisfied* → `with`) |
| `adj_prep_to_from` | lexicalized_syntax | adjective | open | capable | `to` / `from` | 17 | 136 | 170 | Everyone knew she was similar → `to` (vs *different* → `from`) |
| `animacy_relative` | lexical_semantics | noun | open | capable | `who` / `which` | 20 | 160 | – | We finally met the doctor, → `who` (vs *statue* → `which`) |
| `article_phonology` | phonology | article | diag | limited | `apple` / `banana` | 1 | 8 | – | For breakfast, I ate an → `apple` (vs *a* → `banana`) |
| `comparative_than` | morphology | adjective | open | capable | `than` / `and` | 18 | 144 | 180 | The new house is bigger → `than` (vs *big* → `and`) |
| `complement_clause` | lexicalized_syntax | verb | open | capable | `whether` / `that` | 16 | 128 | 160 | In the interview, the journalist wondered → `whether` (vs *claimed* → `that`) |
| `container_noun` | lexical_semantics | noun | closed | capable | `water` / `paper` | 5 | 40 | – | She handed me a bottle of → `water` (vs *pile* → `paper`) |
| `correlative` | morphosyntax | conjunction | diag | capable | `or` / `nor` | 1 | 12 | – | You can choose either the red one → `or` (vs *neither* → `nor`) |
| `country_language` | world_knowledge | proper_noun | open | capable | pair-specific | 11 | 88 | 110 | In France, most people speak → `French` (vs *Spain* → `Spanish`) |
| `dative_alternation` | lexicalized_syntax | verb | open | limited | `me` / `the` | 16 | 32 | – | Two days ago, my neighbor gave → `me` (vs *donated* → `the`) |
| `determiner_number` | morphosyntax | determiner | closed | capable | `is` / `are` | 8 | 96 | 80 | The farmer told us that this sheep → `is` (vs *these* → `are`) |
| `entity_type` | lexical_semantics | proper_noun | open | capable | `who` / `where` | 20 | 120 | – | The article mentioned John, → `who` (vs *London* → `where`) |
| `food_selection` | lexical_semantics | verb | diag | capable | `water` / `bread` | 6 | 36 | – | Every morning, my grandmother drank some → `water` (vs *ate* → `bread`) |
| `gender_pronoun` | lexical_semantics | noun | open | capable | `he` / `she` | 11 | 110 | 110 | The man said that → `he` (vs *woman* → `she`) |
| `gerund_vs_infinitive` | lexicalized_syntax | verb | open | capable | `going` / `to` | 8 | 64 | 80 | Last year, she enjoyed → `going` (vs *wanted* → `to`) |
| `hypernym_class` | lexical_semantics | noun | closed | capable | `bird` / `fish` | 8 | 64 | 80 | A eagle is a kind of → `bird` (vs *shark* → `fish`) † |
| `infinitive_vs_object` | lexicalized_syntax | verb | open | capable | `to` / `the` | 16 | 96 | 160 | After a long day, Maria decided → `to` (vs *enjoyed* → `the`) |
| `irregular_number` | morphosyntax | noun | open | capable | `was` / `were` | 16 | 160 | 160 | I think the man → `was` (vs *men* → `were`) |
| `light_verb` | collocation | verb | diag | limited | `decision` / `walk` | 1 | 10 | – | Tomorrow I will make a → `decision` (vs *take* → `walk`) |
| `mass_count` | morphosyntax | quantifier | closed | capable | `money` / `friends` | 4 | 20 | – | To be honest, I do not have much → `money` (vs *many* → `friends`) |
| `modal_bare_vs_to` | morphosyntax | modal | closed | capable | `be` / `to` | 9 | 72 | 90 | Honestly, we should → `be` (vs *ought* → `to`) |
| `noun_prep_for_in` | lexicalized_syntax | noun | open | capable | `for` / `in` | 16 | 96 | 160 | The report highlighted the need → `for` (vs *increase* → `in`) |
| `noun_prep_on_to` | lexicalized_syntax | noun | open | capable | `on` / `to` | 15 | 90 | 150 | They discussed the impact → `on` (vs *access* → `to`) |
| `npi_licensing` | semantics | quantifier_adverb | closed | capable | `any` / `some` | 7 | 40 | – | At the party, nobody had → `any` (vs *somebody* → `some`) |
| `numeral_noun_number` | morphosyntax | determiner | closed | capable | `year` / `years` | 7 | 56 | – | The project took one → `year` (vs *three* → `years`) |
| `particle_up_out` | collocation | verb | open | capable | `up` / `out` | 11 | 88 | – | After a while, she gave → `up` (vs *figured* → `out`) |
| `partitive_agreement` | morphosyntax | quantifier | closed | capable | `is` / `are` | 6 | 48 | – | I think one of the boxes → `is` (vs *two* → `are`) |
| `prep_government_mixed` | lexicalized_syntax | verb | open | capable | pair-specific | 9 | 72 | 90 | At the time, Alex relied → `on` (vs *looked* → `at`) |
| `pronoun_case` | morphosyntax | preposition_vs_subordinator | closed | limited | `him` / `he` | 8 | 16 | – | They stayed in the room with → `him` (vs *because* → `he`) |
| `range_preposition` | lexicalized_syntax | preposition | diag | capable | `to` / `and` | 1 | 12 | – | Prices range from ten → `to` (vs *between* → `and`) |
| `sensory_modality` | lexical_semantics | adjective | closed | capable | `sound` / `light` | 7 | 42 | – | Suddenly there was a loud → `sound` (vs *bright* → `light`) |
| `temporal_tense` | semantics | adverb | closed | capable | `will` / `was` | 5 | 30 | 40 | The coach said that tomorrow the team → `will` (vs *yesterday* → `was`) |
| `time_place_relative` | lexical_semantics | noun | open | capable | `when` / `where` | 11 | 66 | – | I still remember the day → `when` (vs *city* → `where`) |
| `transitivity` | lexicalized_syntax | verb | open | capable | `the` / `.` | 15 | 90 | – | A moment later, Maya inspected → `the` (vs *arrived* → `.`) |
| `unit_ago_away` | lexical_semantics | noun | closed | capable | `ago` / `away` | 4 | 32 | – | That was only a few weeks → `ago` (vs *miles* → `away`) |
| `verb_prep_from_of` | lexicalized_syntax | verb | closed | capable | `from` / `of` | 6 | 48 | 60 | In the end, she suffered → `from` (vs *consisted* → `of`) |
| `verb_prep_in_with` | lexicalized_syntax | verb | closed | capable | `in` / `with` | 6 | 48 | 60 | In the end, she participated → `in` (vs *dealt* → `with`) |
| `verb_prep_on_to` | lexicalized_syntax | verb | open | capable | `on` / `to` | 8 | 64 | 80 | In the end, she focused → `on` (vs *referred* → `to`) |

† This example is one of the flagged ungrammatical rows (see Limitations).

## Fields

| Field | Description |
|---|---|
| `id` | Unique row id: `v2__<task>__p<pair>__<template>__<direction>` (graded: `g__<task>__p<pair>__d<distance>__<direction>`). |
| `task` | Task name (38 in `default`, 19 in `graded_distance`). |
| `base_text` | Prompt with the base cue word. The model is asked for the next token. |
| `source_text` | The same prompt with the cue word swapped for its partner (the only difference). |
| `base_word`, `source_word` | The cue word in each prompt (no leading space). |
| `base_label`, `source_label` | Expected next token after each prompt, as a string with its leading space (e.g. `" who"`). |
| `base_class`, `source_class` | Class of each cue word (e.g. `human` / `inanimate`). |
| `class_labels` | True if labels are shared by the whole class (36 of 38 tasks); False if each pair has its own labels (`country_language`, `prep_government_mixed`). |
| `direction` | `a_to_b` or `b_to_a`. Every pair appears in both directions (base and source exchanged). |
| `pair_id`, `cluster_id` | Lexical pair, e.g. `animacy_relative::doctor__statue`. Use `cluster_id` to group rows for cluster bootstraps. |
| `pair_index` | Index of the pair within its task; even = discovery, odd = evaluation (see Splits). |
| `split` | `discovery` or `evaluation` (also the HF split name). |
| `template_id`, `template_text` | Sentence template; `{w}` marks the cue slot. |
| `frame` | `near` (cue within two tokens of the prediction), `far` (neutral words in between), `attractor` (agreement attraction). Graded: `graded_d0` ... `graded_d14`. |
| `tier` | `open` (8 or more lexical pairs), `closed` (function-word cues, few possible pairs), `diag` (a single lexeme pair; clusters are templates). Tiers should not be pooled. |
| `linguistic_level` | One of eight levels: lexical_semantics, world_knowledge, morphology, morphosyntax, lexicalized_syntax, collocation, semantics, phonology. |
| `pos_tag` | Part of speech of the cue word. |
| `anchor` | (`default` only) Literature that inspired the task (inspiration only; no text is copied). |
| `calibration_status` | (`default` only) `capable` or `limited`: whether the d12/d16/d20 nanochat models passed the discovery-pair baseline rule for this task (see Construction). |
| `filler_tokens` | (`graded_distance` only) Number of filler tokens inserted between cue and prediction: 0, 4, 7, 10 or 14. |
| `schema_version` | `lexve-v2.0` or `lexve-v2.0-graded`. |
| `known_issue`, `known_issue_note` | True for rows whose text has "a" before a vowel-initial word (e.g. "A eagle is a kind of"); kept unchanged, see Limitations. |
| `*_token_id`, `*_label_id`, `swap_pos`, `token_count`, `cue_gap` | **Tokenizer-specific** (nanochat BPE, see below): token ids of the cue and label, cue token position, prompt length in tokens, and tokens between cue and prediction. |

**Model-agnostic vs tokenizer-specific.** Every text, word, label-string, class, task and metadata field is model-agnostic.
The token-id and position fields refer to the **nanochat BPE tokenizer** (Karpathy's nanochat, MIT license; tokenizer
`tokenizer.pkl` sha256 `387cfc082b0bee45467774fd6f1310a922ad170886a58ccddcb468f275e06a6c`). With another tokenizer,
re-tokenize `base_text` / `source_text` and the labels. Cue words and labels were chosen to be single tokens with a leading
space under nanochat, so some may split into several tokens elsewhere.

## Splits

- `discovery` and `evaluation` are assigned by lexical pair: even `pair_index` → discovery, odd → evaluation. Exceptions:
  `article_phonology` splits by template and `food_selection` by lexeme.
- Within a task (excluding `diag` tier), no cue word appears in both splits, so held-out results test generalization to
  new words.
- Use `discovery` to choose anything (heads, directions, hyperparameters) and report on `evaluation`.
- Each pair appears in both directions (`a_to_b`, `b_to_a`). For confidence intervals, resample by `cluster_id`, not by row.

## Construction

- **Hand-written.** Class word lists, pairings and sentence templates were written by hand
  (`specs_v2.py`). Cross-class pairs were matched roughly by corpus frequency. No row was generated by a language model.
  Pairs were never selected or dropped based on any model's intervention results.
- **Tokenizer screen.** Every cue and label had to be a single nanochat token with a leading space and to align in every
  template. Pairs that failed BPE alignment were rejected (listed in `provenance/manifest_*.json`).
- **Template calibration (baseline only, discovery pairs only).** Templates were kept if, averaged over three nanochat
  models (d12, d16, d20 with value embeddings), at least 50% of discovery pairs were predicted correctly in both directions
  and the two labels held at least 2% of probability mass. Evaluation pairs never influenced selection. Tasks with fewer
  than two surviving near templates are kept and marked `calibration_status: limited`. The per-template numbers are in
  `provenance/template_selection.json`, and baseline accuracy and label mass per task and model are in
  `provenance/baseline_competence.json`.
- **Graded distance.** Uses the same pairs with one stem per task plus fixed filler phrases
  (`", it seems,"`, `", as far as we know,"`, ...). The stems and fillers are in `provenance/manifest_graded.json`.
- **Inspirations, not sources.** Task designs draw on CausalGym, BLiMP, RAVEL, COMPS, Ettinger (2020), Warstadt et al.
  (2019) and Chierchia (1998) (field `anchor`). No text was copied from these resources.

## Limitations

- **Ungrammatical article rows.** 22 rows in `default` (11 distinct texts) and
  30 rows in `graded_distance` (15 texts) put "a" before a vowel-initial word: "A eagle /
  eel / owl is a kind of" (`hypernym_class`), "a aircraft" (`determiner_number`), "a audible" (`sensory_modality`). They are
  kept unchanged so the data match published results, and flagged with `known_issue`. Filter them out with
  `ds.filter(lambda r: not r["known_issue"])`.
- **Calibration circularity.** Templates were chosen using baselines of the same nanochat models the dataset was first used
  to analyse (discovery pairs only). Results on other models do not inherit this bias, but results on those three models
  should be read with it in mind.
- **Small tasks.** Four `diag` tasks have a single lexical pair (`article_phonology`, `correlative`, `light_verb`,
  `range_preposition`); their clusters are templates. Several `closed` tasks have only 4-9 pairs.
- **Weak baselines.** Four tasks are `limited` (`article_phonology`, `dative_alternation`, `light_verb`, `pronoun_case`).
  In the calibration models, label mass is low for some tasks (`pronoun_case` about 2%, `article_phonology` 1-2%,
  `light_verb` 3-5%).
- **Cue reuse.** Some cue words appear in more than one task (e.g. "actor").
- **Scope.** English only. Gender pairs are binary (masculine/feminine nouns). Labels are a single next token, so this is
  a two-way forced choice, not a test of fluent generation.

## Provenance and integrity

The Parquet files are a lossless conversion of the frozen JSONL files plus the two `known_issue` columns, verified value by
value. The source hashes are:

| Config | Source file | sha256 |
|---|---|---|
| `default` | `lexve_v2.jsonl` | `3b8a30a8f989a900026bfaf0c480a7e00a516a400270db6b2c79db94b7d957e2` |
| `graded_distance` | `lexve_v2_graded.jsonl` | `72028c0b5fdc7067f568722b25c0d39c549d598be3af9011891d9a592fcff6b9` |

`SHA256SUMS` covers every file in this repository.

## License

[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).

## Citation

```bibtex
@misc{lexve2026,
  title  = {LexVE: Lexical Minimal Pairs for Word-Level Interventions},
  author = {AUTHORS_TBD},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/datasets/aadityabuilds/lexve}}
}
```