Datasets:
Tasks:
Text Classification
Formats:
parquet
Languages:
English
Size:
1K - 10K
Tags:
minimal-pairs
interpretability
mechanistic-interpretability
linguistics
value-embeddings
causal-intervention
License:
| 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}} | |
| } | |
| ``` | |