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srb-correction-c-c-0001
srb_correction
c-ć
U razredu je dvadeset jedna vredna ućenica.
[ "U razredu je dvadeset jedna vredna učenica." ]
true
Treba „učenica“, a ne „ućenica“ (č/ć).
latin
hard
derived:srb-probe-broj-pridev-imenica-0009
false
CC-BY-4.0
srb-correction-c-c-0002
srb_correction
c-ć
Na ispitu je palo trideset ćetiri lenja studenta.
[ "Na ispitu je palo trideset četiri lenja studenta." ]
true
Treba „četiri“, a ne „ćetiri“ (č/ć).
latin
hard
derived:srb-probe-broj-pridev-imenica-0011
false
CC-BY-4.0
srb-correction-c-c-0003
srb_correction
c-ć
Ja uzeh kljuć sa stola i izađoh.
[ "Ja uzeh ključ sa stola i izađoh." ]
true
Treba „ključ“, a ne „kljuć“ (č/ć).
latin
hard
derived:srb-probe-aorist-imperfekat-syn-0006
false
CC-BY-SA-4.0
srb-correction-c-c-0006
srb_correction
c-ć
U ućionici su postavljena dva nova stola.
[ "U učionici su postavljena dva nova stola." ]
true
Treba „učionici“, a ne „ućionici“ (č/ć).
latin
hard
derived:srb-probe-broj-pridev-imenica-0001
false
CC-BY-4.0
srb-correction-c-c-0007
srb_correction
c-ć
Po staroj kuči noću su lutali duhovi.
[ "Po staroj kući noću su lutali duhovi." ]
true
Treba „kući“, a ne „kuči“ (č/ć).
latin
hard
derived:srb-probe-sibilarizacija-syn-0007
false
CC-BY-SA-4.0
srb-correction-c-c-0008
srb_correction
c-ć
Mi stigosmo na stanicu u poslednji ćas.
[ "Mi stigosmo na stanicu u poslednji čas." ]
true
Treba „čas“, a ne „ćas“ (č/ć).
latin
hard
derived:srb-probe-aorist-imperfekat-syn-0005
false
CC-BY-SA-4.0
srb-correction-c-c-0009
srb_correction
c-ć
Da li češ mi pomoći oko selidbe?
[ "Da li ćeš mi pomoći oko selidbe?" ]
true
Treba „ćeš“, a ne „češ“ (č/ć).
latin
hard
derived:srb-grammar-enklitike-0004
false
CC-BY-4.0
srb-correction-c-c-0011
srb_correction
c-ć
Ne boj se, mali vuće, ništa ti neće biti.
[ "Ne boj se, mali vuče, ništa ti neće biti." ]
true
Treba „vuče“, a ne „vuće“ (č/ć).
latin
hard
derived:srb-probe-sibilarizacija-syn-0020
false
CC-BY-SA-4.0
srb-correction-c-c-0012
srb_correction
c-ć
Dugo je prićala o svojoj nozi.
[ "Dugo je pričala o svojoj nozi." ]
true
Treba „pričala“, a ne „prićala“ (č/ć).
latin
hard
derived:srb-probe-sibilarizacija-syn-0014
false
CC-BY-SA-4.0
srb-correction-c-c-0015
srb_correction
c-ć
On mi tada reće da više ne dolazim.
[ "On mi tada reče da više ne dolazim." ]
true
Treba „reče“, a ne „reće“ (č/ć).
latin
hard
derived:srb-probe-aorist-imperfekat-syn-0009
false
CC-BY-SA-4.0
srb-correction-c-c-0016
srb_correction
c-ć
Bavi se fudbalom več deset godina.
[ "Bavi se fudbalom već deset godina." ]
true
Treba „već“, a ne „več“ (č/ć).
latin
hard
derived:srb-cloze-padezi-0004
false
CC-BY-4.0
srb-correction-c-c-0017
srb_correction
c-ć
U zoološkom vrtu ima ćetrnaest mladih lavova.
[ "U zoološkom vrtu ima četrnaest mladih lavova." ]
true
Treba „četrnaest“, a ne „ćetrnaest“ (č/ć).
latin
hard
derived:srb-probe-broj-pridev-imenica-0015
false
CC-BY-4.0
srb-correction-c-c-0020
srb_correction
c-ć
Kad je pala noč, deca zaspaše kao zaklana.
[ "Kad je pala noć, deca zaspaše kao zaklana." ]
true
Treba „noć“, a ne „noč“ (č/ć).
latin
hard
derived:srb-probe-aorist-imperfekat-syn-0008
false
CC-BY-SA-4.0
srb-correction-c-c-0022
srb_correction
c-ć
Proćitao je jedanaest debelih knjiga.
[ "Pročitao je jedanaest debelih knjiga." ]
true
Treba „Pročitao“, a ne „Proćitao“ (č/ć).
latin
hard
derived:srb-probe-broj-pridev-imenica-0007
false
CC-BY-4.0
srb-correction-c-c-0023
srb_correction
c-ć
Da li ćeš doči na sastanak sutra ujutru?
[ "Da li ćeš doći na sastanak sutra ujutru?" ]
true
Treba „doći“, a ne „doči“ (č/ć).
latin
hard
derived:srb-grammar-pravopis-0003
false
CC-BY-4.0
srb-correction-c-c-0024
srb_correction
c-ć
Hajde, junaće, pomozi mi da podignem ovo!
[ "Hajde, junače, pomozi mi da podignem ovo!" ]
true
Treba „junače“, a ne „junaće“ (č/ć).
latin
hard
derived:srb-probe-sibilarizacija-syn-0017
false
CC-BY-SA-4.0
srb-correction-c-c-0025
srb_correction
c-ć
Treba mi pomoč oko selidbe.
[ "Treba mi pomoć oko selidbe." ]
true
Treba „pomoć“, a ne „pomoč“ (č/ć).
latin
hard
derived:srb-grammar-standard-0007
false
CC-BY-4.0
srb-correction-c-c-0026
srb_correction
c-ć
Javiču ti se u vezi s tim sutra.
[ "Javiću ti se u vezi s tim sutra." ]
true
Treba „Javiću“, a ne „Javiču“ (č/ć).
latin
hard
derived:srb-grammar-standard-0002
false
CC-BY-4.0
srb-correction-c-c-0027
srb_correction
c-ć
Bože, saćuvaj nas od nevolje!
[ "Bože, sačuvaj nas od nevolje!" ]
true
Treba „sačuvaj“, a ne „saćuvaj“ (č/ć).
latin
hard
derived:srb-probe-sibilarizacija-syn-0015
false
CC-BY-SA-4.0
srb-correction-c-c-0028
srb_correction
c-ć
Ja stigoh kući tek pred ponoč.
[ "Ja stigoh kući tek pred ponoć." ]
true
Treba „ponoć“, a ne „ponoč“ (č/ć).
latin
hard
derived:srb-probe-aorist-imperfekat-syn-0002
false
CC-BY-SA-4.0
srb-correction-c-c-0029
srb_correction
c-ć
Hajde, momće, pomozi mi da podignem ovo!
[ "Hajde, momče, pomozi mi da podignem ovo!" ]
true
Treba „momče“, a ne „momće“ (č/ć).
latin
hard
derived:srb-probe-sibilarizacija-syn-0018
false
CC-BY-SA-4.0

serbeval: a Serbian-language LLM benchmark (official set v1.2)

Author: Djordje Markovic (@djolex999)

Code, task YAMLs, full leaderboard and methodology: https://github.com/djolex999/serbeval

serbeval tests what translated benchmarks cannot carry over into Serbian: case government, verbal aspect, the aorist and imperfect, sibilarization, numeral agreement, the ijekavian jat reflex, pravopis (together/apart, hyphenated compounds, prefixoids), č/ć, and Cyrillic/Latin consistency. It runs on lm-evaluation-harness 0.4.12.

This dataset is the official set v1.2: 351 items in 17 tasks. v1.1 and v1.2 (2026-10-17) fixed gold answers and dropped 3 items; v1.2 checked every changed gold against the text of Pravopis 2010 (tačka and page in the explanation). Both spellings are accepted only where Pravopis 2010 and Klajn disagree. The history is under errata in MANIFEST.json.

Verification

Every item has a status in MANIFEST.json:

Status Items Meaning
human 61 checked by a native speaker (blind baseline 29/30, plus the disputed items)
consensus 214 fewer than 2 of the 4 strongest models disagreed with the gold
derived 76 correction item built mechanically from an official item

Model consensus is not human verification. It is labelled separately on purpose.

Gold answers come from three places:

  • hand-written items;
  • the srLex lexicon, using only forms attested in the corpus;
  • source checks against Pravopis 2010 and Klajn.

Items where the authorities disagree (e.g. foto-aparat vs fotoaparat) were dropped rather than pinned to one side.

Tasks

Config Items What it tests
srb_correction_c_c 21 correction: one č/ć error, no cue
srb_correction_ispravno 27 correction: clean sentences (false-alarm check)
srb_correction_razmak_crtica 28 correction: one spacing/hyphen error, no cue
srb_cyrillic_agreement 20 MCQ: the same question in Cyrillic and Latin (pairs)
srb_grammar 19 MCQ: case government, aspect, agreement, clitics, standard usage
srb_grammar_cloze 18 cloze: produce the inflected form
srb_idioms 19 MCQ: meaning of Serbian idioms
srb_pravopis_poluslozenice 19 cloze: polusloženice
srb_pravopis_prefiksoidi 17 cloze: foreign prefixoids
srb_pravopis_sastavljeno_rastavljeno 24 cloze: together / apart
srb_pravopis_slozeni_pridevi 22 cloze: compound adjectives
srb_probe_aorist_imperfekat 20 cloze: aorist and imperfect
srb_probe_broj_pridev_imenica 20 cloze: numeral + adjective + noun agreement
srb_probe_jat 19 cloze: ijekavian jat reflex
srb_probe_poluslozenice 19 cloze: hyphenated compounds
srb_probe_sastavljeno_rastavljeno 19 cloze: written together or apart
srb_probe_sibilarizacija 20 cloze: sibilarization / palatalization

Three item shapes:

  • MCQ: question, choices (4), answer_index. Gold positions are balanced.
  • Cloze: question with ____, cue, and answers, the list of every accepted form. Scoring folds case, outer punctuation and script, but never diacritics or an intra-word hyphen.
  • Correction: text, which may contain one error; answers (the corrected sentence, or the text itself when has_error is false). There is no cue.

All items also carry id, task, category, explanation, script, difficulty, source, verified and license.

Usage

from datasets import load_dataset
ds = load_dataset("djolex999/serbeval", "srb_probe_aorist_imperfekat", split="test")

To score models with the exact prompts and scoring rules, use the task YAMLs from the GitHub repo:

lm_eval --tasks srb_official --include_path lm_eval_tasks ...

Leaderboard (official set v1.2, 30 models, zero-shot, runs 2026-10-12 to 17)

# Model MCQ Cloze Pravopis Probni Ispravljanje Prosek max unparsed
1 openai_gpt-6-sol 100% 100% 99% 96% 99% 99% 0.00
2 google_gemini-3.8-flash 100% 100% 95% 97% 98% 98% 0.00
3 anthropic_claude-opus-5.5 100% 100% 95% 96% 99% 98% 0.00
4 openai_gpt-6.1-sol 100% 100% 94% 97% 99% 98% 0.00
5 openai_gpt-5.6-sol 100% 100% 92% 97% 98% 97% 0.00
6 deepseek_deepseek-v4-pro 98% 100% 89% 90% 95% 95% 0.00
7 openai_gpt-6-luna 100% 100% 83% 96% 94% 94% 0.00
8 anthropic_claude-sonnet-5.5 96% 94% 92% 92% 96% 94% 0.00
9 x-ai_grok-4.7 100% 89% 89% 95% 99% 94% 0.00
10 google_gemini-3.5-flash-lite 100% 100% 88% 87% 95% 94% 0.00
11 openai_gpt-5.6-luna 100% 100% 82% 89% 96% 93% 0.00
12 moonshotai_kimi-k3 96% 100% 86% 85% 92% 92% 0.00
13 deepseek_deepseek-v4.1-flash 100% 100% 79% 85% 89% 90% 0.00
14 z-ai_glm-5.3 98% 94% 82% 85% 93% 90% 0.05
15 google_gemma-4-31b-it 98% 100% 76% 79% 93% 89% 0.00
16 deepseek_deepseek-v3.2 98% 89% 88% 76% 87% 88% 0.00
17 minimax_minimax-m3 96% 100% 73% 77% 89% 87% 0.00
18 qwen_qwen3.6-27b 96% 89% 76% 60% 94% 83% 0.00
19 mistralai_mistral-large-4-0 98% 89% 69% 70% 86% 82% 0.00
20 qwen_qwen3-235b-a22b-2507 88% 100% 72% 67% 80% 81% 0.05
21 openai_gpt-5-mini 90% 89% 66% 62% 87% 79% 0.00
22 anthropic_claude-haiku-5.5 93% 89% 59% 62% 89% 78% 0.00
23 nvidia_nemotron-3-ultra-550b-a55b 91% 94% 54% 63% 82% 77% 0.00
24 meta-llama_llama-3.3-70b-instruct 88% 94% 57% 52% 75% 73% 0.05
25 google_gemma-3-12b-it 79% 89% 49% 44% 75% 67% 0.00
26 qwen_qwen3.5-9b 86% 83% 47% 37% 76% 66% 0.00
27 nvidia_nemotron-3.5-lightning 81% 39% 12% 14% 54% 40% 0.00
28 mistralai_mistral-nemo 69% 33% 12% 15% 68% 39% 0.00
29 meta-llama_llama-3.1-8b-instruct 48% 50% 23% 21% 54% 39% 0.00
30 cohere_command-a-plus 74% 33% 19% 28% 37% 38% 0.15

The frontier is near ceiling (GPT-6 Sol 99%, three more at 98%); its misses are mostly usage-over-norm spellings. Below it, the set separates models from 38% to 95%, and pravopis separates them most. The best open-weights model is DeepSeek V4 Pro (95%); turning reasoning on does not raise any tested model by more than 2 points. Details and caveats, including a possible small selection bias toward the four consensus models, are in the GitHub repo.

License

Items carry their own license field: 303 are CC BY 4.0 (hand-written for this project) and 48 are CC BY-SA 4.0 (word forms from srLex). The dataset as a whole is distributed under CC BY-SA 4.0.

Attribution:

Citation

@misc{serbeval2026,
  title  = {serbeval: a Serbian-language benchmark for large language models},
  author = {Markovic, Djordje},
  note   = {GitHub: @djolex999},
  year   = {2026},
  url    = {https://github.com/djolex999/serbeval}
}
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