EVALITA-LLM Leaderboard
Open Italian LLM LeaderboardEvalita-LLM is a benchmark designed to evaluate Large Language Models (LLMs) on Italian tasks. The distinguishing features of Evalita-LLM are the following: (i) all tasks are native Italian, avoiding translation issues and potential cultural biases; (ii) the benchmark includes generative tasks, enabling more natural interaction with LLMs; (iii) all tasks are evaluated against multiple prompts, this way mitigating the model sensitivity to specific prompts and allowing a fairer evaluation. To provide a comprehensive evaluation, Evalita-LLM leverages the Combined Performance Score (CPS), which combines both the peak and average performance of models across multiple prompts.
Multiple-choice tasks: ๐TE (Textual Entailment), ๐SA (Sentiment Analysis), โ ๏ธHS (Hate Speech Detection), ๐ฅAT (Admission Test), ๐คWIC (Word in Context), โFAQ (Frequently Asked Questions)
Generative tasks: ๐LS (Lexical Substitution), ๐SU (Summarization), ๐ท๏ธNER (Named Entity Recognition), ๐REL (Relation Extraction)
- "headers": [
- "Rank",
- "Size",
- "FS",
- "IS_FS",
- "Model",
- "Avg. Comb. Perf. โฌ๏ธ",
- "TE",
- "TE Prompt Average",
- "TE Prompt Std",
- "TE Best Prompt",
- "TE Best Prompt Id",
- "SA",
- "SA Prompt Average",
- "SA Prompt Std",
- "SA Best Prompt",
- "SA Best Prompt Id",
- "HS",
- "HS Prompt Average",
- "HS Prompt Std",
- "HS Best Prompt",
- "HS Best Prompt Id",
- "AT",
- "AT Prompt Average",
- "AT Prompt Std",
- "AT Best Prompt",
- "AT Best Prompt Id",
- "WIC",
- "WIC Prompt Average",
- "WIC Prompt Std",
- "WIC Best Prompt",
- "WIC Best Prompt Id",
- "FAQ",
- "FAQ Prompt Average",
- "FAQ Prompt Std",
- "FAQ Best Prompt",
- "FAQ Best Prompt Id",
- "LS",
- "LS Prompt Average",
- "LS Prompt Std",
- "LS Best Prompt",
- "LS Best Prompt Id",
- "SU",
- "SU Prompt Average",
- "SU Prompt Std",
- "SU Best Prompt",
- "SU Best Prompt Id",
- "NER",
- "NER Prompt Average",
- "NER Prompt Std",
- "NER Best Prompt",
- "NER Best Prompt Id",
- "REL",
- "REL Prompt Average",
- "REL Prompt Std",
- "REL Best Prompt",
- "REL Best Prompt Id",
- "#Params (B)"
- "data": [
- [
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- true,
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- "metadata": null
Groups
evalita-mp: All tasks (perplexity and non-perplexity based).evalita-mp_gen: Only generative tasks.evalita-mp_mc: Only multiple-choice tasks.
Tasks
The following Evalita-LLM tasks can also be evaluated in isolation:
evalita-mp_te: Textual Entailment (TE)evalita-mp_sa: Sentiment Analysis (SA)evalita-mp_wic: Word in Context (WIC)evalita-mp_hs: Hate Speech Detection (HS)evalita-mp_at: Admission Tests (AT)evalita-mp_faq: Frequently Asked Questions & Question Answering (FAQ)evalita-mp_sum_fp: Summarization (SU)evalita-mp_ls: Lexical Substitution LS)evalita-mp_ner_group: Named Entity Recognition (NER)evalita-mp_re: Relation Extraction (REL)
Usage
lm_eval --model hf --model_args pretrained=meta-llama/Llama-2-7b-hf --tasks evalita-mp --device cuda:0 --batch_size 1
๐ Model Evaluation Request
Fill out the form below to request evaluation of your model on EVALITA-LLM.
Once submitted, our team will automatically receive a notification. We will evaluate the submissionโs relevance for both research and commercial purposes, as well as assess its feasibility.
What happens after submission:
- Your request is automatically sent to the EVALITA-LLM team
- We verify that the model is accessible on HuggingFace
- We contact you to confirm inclusion in the evaluation
- The model is added to the evaluation queue
Model requirements:
- Model must be publicly accessible on HuggingFace Hub
- Must be compatible with the EleutherAI/lm-evaluation-harness framework
- Must have a license that allows evaluation
Evaluation tasks: Your model will be evaluated on all tasks: TE, SA, HS, AT, WIC, FAQ, LS, SU, NER, REL.
Textual Entailment (TE) --- Multiple-choice task
The input are two sentences: the text (T) and the hypothesis (H). The model has to determine whether the meaning of the hypothesis is logically entailed by the text.
| # | Prompt | Answer Choices |
|---|---|---|
| 1 | La frase: '{{text1}}' implica logicamente che la frase: '{{text2}}' sia vera? | ["Sรฌ", "No"] |
| 2 | Devi risolvere un compito di inferenza semantica. La frase: '{{text1}}' implica logicamente che la frase: '{{text2}}' sia vera? | ["Sรฌ", "No"] |
| 3 | La frase: '{{text1}}' implica logicamente che la frase: '{{text2}}' sia vera?\nA: Sรฌ\nB: No\nRisposta: | ["A", "B"] |
| 4 | Devi risolvere un compito di inferenza semantica. La frase: '{{text1}}' implica logicamente che la frase: '{{text2}}' sia vera?\nA: Sรฌ\nB: No\nRisposta: | ["A", "B"] |
| 5 | Frase 1: '{{text1}}' Frase 2: '{{text2}}' | ["La frase 1 implica logicamente che la frase 2 sia vera", "La frase 1 non implica logicamente che la frase 2 sia vera"] |
| 6 | Devi risolvere un compito di inferenza semantica. Frase 1: '{{text1}}' Frase 2: '{{text2}}' | ["La frase 1 implica logicamente che la frase 2 sia vera", "La frase 1 non implica logicamente che la frase 2 sia vera"] |
Combined Performance = (1 - (Best Prompt - Prompt Average) / 100) * Best Prompt. Prompt Average = accuracy averaged over the 6 prompts. Best Prompt = accuracy of the best prompt. Prompt ID = ID of the best prompt (see legend above).
- "headers": [
- "Rank",
- "Size",
- "FS",
- "IS_FS",
- "Model",
- "Avg. Comb. Perf. โฌ๏ธ",
- "Comb. Perf. โฌ๏ธ",
- "Prompt Average",
- "Prompt Std",
- "Best Prompt",
- "Best Prompt Id",
- "SA",
- "SA Prompt Average",
- "SA Prompt Std",
- "SA Best Prompt",
- "SA Best Prompt Id",
- "HS",
- "HS Prompt Average",
- "HS Prompt Std",
- "HS Best Prompt",
- "HS Best Prompt Id",
- "AT",
- "AT Prompt Average",
- "AT Prompt Std",
- "AT Best Prompt",
- "AT Best Prompt Id",
- "WIC",
- "WIC Prompt Average",
- "WIC Prompt Std",
- "WIC Best Prompt",
- "WIC Best Prompt Id",
- "FAQ",
- "FAQ Prompt Average",
- "FAQ Prompt Std",
- "FAQ Best Prompt",
- "FAQ Best Prompt Id",
- "LS",
- "LS Prompt Average",
- "LS Prompt Std",
- "LS Best Prompt",
- "LS Best Prompt Id",
- "SU",
- "SU Prompt Average",
- "SU Prompt Std",
- "SU Best Prompt",
- "SU Best Prompt Id",
- "NER",
- "NER Prompt Average",
- "NER Prompt Std",
- "NER Best Prompt",
- "NER Best Prompt Id",
- "REL",
- "REL Prompt Average",
- "REL Prompt Std",
- "REL Best Prompt",
- "REL Best Prompt Id",
- "#Params (B)"
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- "metadata": null
Sentiment Analysis (SA) --- Multiple-choice task
The input is a tweet. The model has to determine the sentiment polarity of the text, categorizing it into one of four classes: positive, negative, neutral, or mixed.
| # | Prompt | Answer Choices |
|---|---|---|
| 1 | Qual รจ il sentiment espresso nel seguente tweet: '{{text}}'? | ["Positivo", "Negativo", "Neutro", "Misto"] |
| 2 | Devi svolgere un compito di analisi del sentiment. Qual รจ il sentiment espresso nel seguente tweet: '{{text}}'? | ["Positivo", "Negativo", "Neutro", "Misto"] |
| 3 | Qual รจ il sentiment espresso nel seguente tweet: '{{text}}'?\nA: Positivo\nB: Negativo\nC: Neutro\nD: Misto\nRisposta: | ["A", "B", "C", "D"] |
| 4 | Devi svolgere un compito di analisi del sentiment. Qual รจ il sentiment espresso nel seguente tweet: '{{text}}'?\nA: Positivo\nB: Negativo\nC: Neutro\nD: \nRisposta: | ["A", "B", "C", "D"] |
| 5 | Il seguente tweet: '{{text}}' esprime un sentiment | ["Positivo", "Negativo", "Neutro", "Misto"] |
| 6 | Devi svolgere un compito di analisi del sentiment. Il seguente tweet: '{{text}}' esprime un sentiment | ["Positivo", "Negativo", "Neutro", "Misto"] |
Combined Performance = (1 - (Best Prompt - Prompt Average) / 100) * Best Prompt. Prompt Average = F1-macro averaged over the 6 prompts. Best Prompt = F1-macro of the best prompt. Prompt ID = ID of the best prompt (see legend above).
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- "Model",
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- "TE",
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- "TE Prompt Std",
- "TE Best Prompt",
- "TE Best Prompt Id",
- "Comb. Perf. โฌ๏ธ",
- "Prompt Average",
- "Prompt Std",
- "Best Prompt",
- "Best Prompt Id",
- "HS",
- "HS Prompt Average",
- "HS Prompt Std",
- "HS Best Prompt",
- "HS Best Prompt Id",
- "AT",
- "AT Prompt Average",
- "AT Prompt Std",
- "AT Best Prompt",
- "AT Best Prompt Id",
- "WIC",
- "WIC Prompt Average",
- "WIC Prompt Std",
- "WIC Best Prompt",
- "WIC Best Prompt Id",
- "FAQ",
- "FAQ Prompt Average",
- "FAQ Prompt Std",
- "FAQ Best Prompt",
- "FAQ Best Prompt Id",
- "LS",
- "LS Prompt Average",
- "LS Prompt Std",
- "LS Best Prompt",
- "LS Best Prompt Id",
- "SU",
- "SU Prompt Average",
- "SU Prompt Std",
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- "SU Best Prompt Id",
- "NER",
- "NER Prompt Average",
- "NER Prompt Std",
- "NER Best Prompt",
- "NER Best Prompt Id",
- "REL",
- "REL Prompt Average",
- "REL Prompt Std",
- "REL Best Prompt",
- "REL Best Prompt Id",
- "#Params (B)"
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- "metadata": null
Hate Speech (HS) --- Multiple-choice task
The input is a tweet. The model has to determine whether the text contains hateful content directed towards marginalized or minority groups. The output is a binary classification: hateful or not hateful.
| # | Prompt | Answer Choices |
|---|---|---|
| 1 | C'รจ incitamento all'odio nel seguente tweet: '{{full_text}}'? | ["Falso", "Vero"] |
| 2 | Devi svolgere un compito di identificazione di incitamento all'odio. C'รจ incitamento all'odio nel seguente tweet: '{{full_text}}'? | ["Falso", "Vero"] |
| 3 | C'รจ incitamento all'odio nel seguente tweet: '{{full_text}}'?\nA: Vero\nB: Falso\nRisposta: | ["B", "A"] |
| 4 | Devi svolgere un compito di identificazione di incitamento all'odio. C'รจ incitamento all'odio nel seguente tweet: '{{full_text}}'?\nA: Vero\nB: Falso\nRisposta: | ["B", "A"] |
| 5 | Il tweet: '{{full_text}}' | ["non contiene incitamento all'odio", "contiene incitamento all'odio"] |
| 6 | Devi svolgere un compito di identificazione di incitamento all'odio. Il tweet: '{{full_text}}' | ["non contiene incitamento all'odio", "contiene incitamento all'odio"] |
Combined Performance = (1 - (Best Prompt - Prompt Average) / 100) * Best Prompt. Prompt Average = F1-micro averaged over the 6 prompts. Best Prompt = F1-micro of the best prompt. Prompt ID = ID of the best prompt (see legend above).
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- "SA",
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- "FAQ",
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- "NER",
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- "NER Best Prompt Id",
- "REL",
- "REL Prompt Average",
- "REL Prompt Std",
- "REL Best Prompt",
- "REL Best Prompt Id",
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- "metadata": null
Admission Tests (AT) --- Multiple-choice task
The input is a multiple-choice question with five options (A-E) from Italian medical specialty entrance exams, and the model must identify the correct answer.
| # | Prompt | Answer Choices |
|---|---|---|
| 1 | Dato il seguente quesito di medicina: '{{Question}}' qual รจ la risposta corretta? | ["A", "B", "C", "D", "E"] |
| 2 | Devi risolvere un compito di risposte a domande. Dato il seguente quesito di medicina: '{{Question}}' qual รจ la risposta corretta? | ["A", "B", "C", "D", "E"] |
| 3 | Dato il seguente quesito di medicina: '{{Question}}' qual รจ la risposta corretta?\nA: {{A}}\nB: {{B}}\nC: {{C}}\nD: {{D}}\nE: {{E}}\nRisposta: | ["A", "B", "C", "D", "E"] |
| 4 | Devi risolvere un compito a scelta multipla. Dato il seguente caso clinico: '{{background}}', qual รจ la risposta corretta alla domanda: '{{domanda}}'?\nA: {{A}}\nB: {{B}}\nC: {{C}}\nD: {{D}}\nE: {{E}}\nRisposta:Devi risolvere un compito a scelta multipla. Dato il seguente quesito di medicina: '{{Question}}' qual รจ la risposta corretta?\nA: {{A}}\nB: {{B}}\nC: {{C}}\nD: {{D}}\nE: {{E}}\nRisposta: | ["A", "B", "C", "D", "E"] |
| 5 | Dato il seguente quesito di medicina '{{Question}}' la risposta corretta รจ: | ["A", "B", "C", "D", "E"] |
| 6 | Devi risolvere un compito di risposte a domande. Dato il seguente quesito di medicina '{{Question}}' la risposta corretta รจ: | ["A", "B", "C", "D", "E"] |
Combined Performance = (1 - (Best Prompt - Prompt Average) / 100) * Best Prompt. Prompt Average = accuracy averaged over the 6 prompts. Best Prompt = accuracy of the best prompt. Prompt ID = ID of the best prompt (see legend above).
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- "TE Prompt Std",
- "TE Best Prompt",
- "TE Best Prompt Id",
- "SA",
- "SA Prompt Average",
- "SA Prompt Std",
- "SA Best Prompt",
- "SA Best Prompt Id",
- "HS",
- "HS Prompt Average",
- "HS Prompt Std",
- "HS Best Prompt",
- "HS Best Prompt Id",
- "Comb. Perf. โฌ๏ธ",
- "Prompt Average",
- "Prompt Std",
- "Best Prompt",
- "Best Prompt Id",
- "WIC",
- "WIC Prompt Average",
- "WIC Prompt Std",
- "WIC Best Prompt",
- "WIC Best Prompt Id",
- "FAQ",
- "FAQ Prompt Average",
- "FAQ Prompt Std",
- "FAQ Best Prompt",
- "FAQ Best Prompt Id",
- "LS",
- "LS Prompt Average",
- "LS Prompt Std",
- "LS Best Prompt",
- "LS Best Prompt Id",
- "SU",
- "SU Prompt Average",
- "SU Prompt Std",
- "SU Best Prompt",
- "SU Best Prompt Id",
- "NER",
- "NER Prompt Average",
- "NER Prompt Std",
- "NER Best Prompt",
- "NER Best Prompt Id",
- "REL",
- "REL Prompt Average",
- "REL Prompt Std",
- "REL Best Prompt",
- "REL Best Prompt Id",
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- "metadata": null
Word in Context (WIC) --- Multiple-choice task
The input consists of a word (w) and two sentences. The model has to determine whether the word w has the same meaning in both sentences. The output is a binary classification: 1 (same meaning) or 0 (different meaning).
| # | Prompt | Answer Choices |
|---|---|---|
| 1 | La parola: '{{sentence1[start1:end1]}}' nella frase: '{{sentence1}}' ha lo stesso significato della parola: '{{sentence2[start2:end2]}}' nella frase: '{{sentence2}}'? | ["No", "Sรฌ"] |
| 2 | Devi determinare se una stessa parola usata in due frasi differenti ha lo stesso significato in entrambi i contesti. La parola: '{{sentence1[start1:end1]}}' nella frase: '{{sentence1}}' ha lo stesso significato della parola: '{{sentence2[start2:end2]}}' nella frase: '{{sentence2}}'? | ["No", "Sรฌ"] |
| 3 | La parola: '{{sentence1[start1:end1]}}' nella frase: '{{sentence1}}' ha lo stesso significato della parola: '{{sentence2[start2:end2]}}' nella frase: '{{sentence2}}'?\nA: Sรฌ\nB: No\nRisposta: | ["B", "A"] |
| 4 | Devi determinare se una stessa parola usata in due frasi differenti ha lo stesso significato in entrambi i contesti. La parola: '{{sentence1[start1:end1]}}' nella frase: '{{sentence1}}' ha lo stesso significato della parola: '{{sentence2[start2:end2]}}' nella frase: '{{sentence2}}'?\nA: \nB: No\nRisposta: | ["B", "A"] |
| 5 | La parola: '{{sentence1[start1:end1]}}' nella frase: '{{sentence1}}' e la parola: '{{sentence2[start2:end2]}}' nella frase: '{{sentence2}}' | ["non hanno lo stesso significato", "hanno lo stesso significato"] |
| 6 | Devi determinare se una stessa parola usata in due frasi differenti ha lo stesso significato in entrambi i contesti. La parola: '{{sentence1[start1:end1]}}' nella frase: '{{sentence1}}' e la parola: '{{sentence2[start2:end2]}}' nella frase: '{{sentence2}}' | ["non hanno lo stesso significato", "hanno lo stesso significato"] |
Combined Performance = (1 - (Best Prompt - Prompt Average) / 100) * Best Prompt. Prompt Average = F1-macro averaged over the 6 prompts. Best Prompt = F1-macro of the best prompt. Prompt ID = ID of the best prompt (see legend above).
- "headers": [
- "Rank",
- "Size",
- "FS",
- "IS_FS",
- "Model",
- "Avg. Comb. Perf. โฌ๏ธ",
- "TE",
- "TE Prompt Average",
- "TE Prompt Std",
- "TE Best Prompt",
- "TE Best Prompt Id",
- "SA",
- "SA Prompt Average",
- "SA Prompt Std",
- "SA Best Prompt",
- "SA Best Prompt Id",
- "HS",
- "HS Prompt Average",
- "HS Prompt Std",
- "HS Best Prompt",
- "HS Best Prompt Id",
- "AT",
- "AT Prompt Average",
- "AT Prompt Std",
- "AT Best Prompt",
- "AT Best Prompt Id",
- "Comb. Perf. โฌ๏ธ",
- "Prompt Average",
- "Prompt Std",
- "Best Prompt",
- "Best Prompt Id",
- "FAQ",
- "FAQ Prompt Average",
- "FAQ Prompt Std",
- "FAQ Best Prompt",
- "FAQ Best Prompt Id",
- "LS",
- "LS Prompt Average",
- "LS Prompt Std",
- "LS Best Prompt",
- "LS Best Prompt Id",
- "SU",
- "SU Prompt Average",
- "SU Prompt Std",
- "SU Best Prompt",
- "SU Best Prompt Id",
- "NER",
- "NER Prompt Average",
- "NER Prompt Std",
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- "metadata": null
Frequently Asked Questions & Question Answering (FAQ) --- Multiple-choice task
The input is a user query regarding the water supply service. The model must identify the correct answer from the 4 available options.
| # | Prompt | Answer Choices |
|---|---|---|
| 1 | Rispondi alla seguente domanda: '{{question}}' | {{[A, B, C, D]}} |
| 2 | Devi risolvere un compito di risposte a domande. Rispondi alla seguente domanda: '{{question}}' | {{[A, B, C, D]}} |
| 3 | Rispondi alla seguente domanda: '{{question}}'\nA: {{A}}\nB: {{B}}\nC: {{C}}\nD: {{D}}\nRisposta: | ["A", "B", "C", "D"] |
| 4 | Devi risolvere un compito a scelta multipla. Rispondi alla seguente domanda: '{{question}}'\nA: {{A}}\nB: {{B}}\nC: {{C}}\nD: {{D}}\nRisposta: | ["A", "B", "C", "D"] |
| 5 | La risposta alla domanda: '{{question}}' รจ: | {{[A, B, C, D]}} |
| 6 | Devi risolvere un compito di risposte a domande. La risposta alla domanda: '{{question}}' รจ: | {{[A, B, C, D]}} |
Combined Performance = (1 - (Best Prompt - Prompt Average) / 100) * Best Prompt. Prompt Average = accuracy averaged over the 6 prompts. Best Prompt = accuracy of the best prompt. Prompt ID = ID of the best prompt (see legend above).
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- "TE Prompt Std",
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- "SA",
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- "SA Prompt Std",
- "SA Best Prompt",
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- "HS",
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- "HS Prompt Std",
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- "AT",
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- "AT Prompt Std",
- "AT Best Prompt",
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- "WIC",
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- "WIC Best Prompt Id",
- "Comb. Perf. โฌ๏ธ",
- "Prompt Average",
- "Prompt Std",
- "Best Prompt",
- "Best Prompt Id",
- "LS",
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- "LS Prompt Std",
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- "SU",
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- "NER",
- "NER Prompt Average",
- "NER Prompt Std",
- "NER Best Prompt",
- "NER Best Prompt Id",
- "REL",
- "REL Prompt Average",
- "REL Prompt Std",
- "REL Best Prompt",
- "REL Best Prompt Id",
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- "metadata": null
Lexical Substitution (LS) --- Generative task
The input is a sentence containing a target word (w). The model has to replace the target word w with its most suitable synonyms that are contextually relevant.
| # | Prompt |
|---|---|
| 7 | Trova 10 parole che possono sostituire la parola racchiusa tra i marcatori <head> nella seguente frase: '{{context}}', mantenendo lo stesso significato. Elenca i lemmi (forme base) di queste parole, separandoli con una virgola, ad esempio: lemma1, lemma2, lemma3, lemma4, lemma5. Non aggiungere commenti o altro testo. Risposta: |
| 8 | Devi risolvere un compito di sostituzione lessicale. Trova 10 parole che possono sostituire la parola racchiusa tra i marcatori <head> nella seguente frase: '{{context}}', mantenendo lo stesso significato. Elenca i lemmi (forme base) di queste parole, separandoli con una virgola, ad esempio: lemma1, lemma2, lemma3, lemma4, lemma5. Non aggiungere commenti o altro testo. Risposta: |
Combined Performance = (1 - (Best Prompt - Prompt Average) / 100) * Best Prompt. Prompt Average = F1 averaged over the 2 prompts. Best Prompt = F1 of the best prompt. Prompt ID = ID of the best prompt (see legend above).
- "headers": [
- "Rank",
- "Size",
- "FS",
- "IS_FS",
- "Model",
- "Avg. Comb. Perf. โฌ๏ธ",
- "TE",
- "TE Prompt Average",
- "TE Prompt Std",
- "TE Best Prompt",
- "TE Best Prompt Id",
- "SA",
- "SA Prompt Average",
- "SA Prompt Std",
- "SA Best Prompt",
- "SA Best Prompt Id",
- "HS",
- "HS Prompt Average",
- "HS Prompt Std",
- "HS Best Prompt",
- "HS Best Prompt Id",
- "AT",
- "AT Prompt Average",
- "AT Prompt Std",
- "AT Best Prompt",
- "AT Best Prompt Id",
- "WIC",
- "WIC Prompt Average",
- "WIC Prompt Std",
- "WIC Best Prompt",
- "WIC Best Prompt Id",
- "FAQ",
- "FAQ Prompt Average",
- "FAQ Prompt Std",
- "FAQ Best Prompt",
- "FAQ Best Prompt Id",
- "Comb. Perf. โฌ๏ธ",
- "Prompt Average",
- "Prompt Std",
- "Best Prompt",
- "Best Prompt Id",
- "SU",
- "SU Prompt Average",
- "SU Prompt Std",
- "SU Best Prompt",
- "SU Best Prompt Id",
- "NER",
- "NER Prompt Average",
- "NER Prompt Std",
- "NER Best Prompt",
- "NER Best Prompt Id",
- "REL",
- "REL Prompt Average",
- "REL Prompt Std",
- "REL Best Prompt",
- "REL Best Prompt Id",
- "#Params (B)"
- "data": [
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- 1,
- "๐ต๐ต",
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- false,
- "<a target="_blank" href="https://huggingface.co/m-polignano/ANITA-NEXT-24B-Magistral-2506-VISION-ITA" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">ANITA-NEXT-24B-Magistral-2506-VISION-ITA</a> ๐ต๐ต๐๏ธ",
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- "<a target="_blank" href="https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">Qwen3-Next-80B-A3B-Instruct</a> ๐ต๐ต๐ต๐๏ธ",
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- 82
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- false,
- "<a target="_blank" href="https://huggingface.co/mistralai/Mistral-Small-24B-Instruct-2501" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">Mistral-Small-24B-Instruct-2501</a>",
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- "<a target="_blank" href="https://huggingface.co/Qwen/Qwen2.5-72B-Instruct" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">Qwen2.5-72B-Instruct</a>",
- 52.38,
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- 7,
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- 10,
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- 39.77,
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- 42.98,
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- 73
- [
- 5,
- "๐ต๐ต",
- "๐ พ๏ธ",
- false,
- "<a target="_blank" href="https://huggingface.co/m-polignano/ANITA-NEXT-24B-Magistral-2506-ITA" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">ANITA-NEXT-24B-Magistral-2506-ITA</a>",
- 47.43,
- 70.61,
- 68.21,
- 9.62,
- 80.5,
- 4,
- 60.56,
- 55.65,
- 20.04,
- 76.94,
- 4,
- 65.27,
- 62.52,
- 9.56,
- 72.53,
- 4,
- 59.82,
- 56.37,
- 26.43,
- 89.6,
- 3,
- 58.74,
- 54.7,
- 19.88,
- 66.94,
- 5,
- 54.57,
- 53.16,
- 33.68,
- 96.76,
- 4,
- 49.64,
- 49.36,
- 0.79,
- 49.92,
- 9,
- 13.93,
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- 14.05,
- 8,
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- 10,
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- 30.4,
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- 32.51,
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- 24
- [
- 6,
- "๐ต๐ต๐ต",
- "5๏ธโฃ",
- true,
- "<a target="_blank" href="https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">Llama-3.3-70B-Instruct</a> ๐ต๐ต๐ต๐",
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- 80.25,
- 2.38,
- 83.5,
- 3,
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- 78.98,
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- 77.82,
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- 68.83,
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- 99,
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- 71
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- false,
- "<a target="_blank" href="https://huggingface.co/allenai/Llama-3.1-Tulu-3-70B" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">Llama-3.1-Tulu-3-70B</a>",
- 51.6,
- 71.71,
- 69.67,
- 11.26,
- 79.75,
- 2,
- 59.89,
- 54.84,
- 16.28,
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- false,
- "<a target="_blank" href="https://huggingface.co/google/gemma-3-27b-it" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">gemma-3-27b-it</a>",
- 49.89,
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- 28
- [
- 9,
- "๐ต๐ต",
- "5๏ธโฃ",
- true,
- "<a target="_blank" href="https://huggingface.co/mistralai/Mistral-Small-24B-Instruct-2501" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">Mistral-Small-24B-Instruct-2501</a> ๐ต๐ต๐",
- 57.67,
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- "metadata": null
Summarization (SUM) --- Generative task
The input is a news article. The model has to generate a concise summary of the input text, capturing the key information and main points.
| # | Prompt |
|---|---|
| 7 | Riassumi il seguente articolo di giornale: '{{source}}'\nRiassunto: |
| 8 | Devi risolvere un compito di sintesi automatica del testo. Riassumi il seguente articolo di giornale: '{{source}}'\nRiassunto: |
Combined Performance = (1 - (Best Prompt - Prompt Average) / 100) * Best Prompt. Prompt Average = F1 averaged over the 2 prompts. Best Prompt = F1 of the best prompt. Prompt ID = ID of the best prompt (see legend above).
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- "SA",
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- "HS",
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- "AT",
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- "WIC",
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- "FAQ",
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- "FAQ Prompt Std",
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- "FAQ Best Prompt Id",
- "LS",
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- "LS Prompt Std",
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- "Comb. Perf. โฌ๏ธ",
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- "NER",
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- "NER Best Prompt",
- "NER Best Prompt Id",
- "REL",
- "REL Prompt Average",
- "REL Prompt Std",
- "REL Best Prompt",
- "REL Best Prompt Id",
- "#Params (B)"
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- "metadata": null
Named Entity Recognition (NER) --- Generative task
The input is a sentence. The model has to identify and classify Named Entities into predefined categories such as person, organization, and location.
| # | Prompt |
|---|---|
| 7 | Estrai tutte le entitร di tipo PER (persona), LOC (luogo) e ORG (organizzazione) dal testo seguente. Riporta ogni entitร con il formato: Entitร $Tipo, separando ciascuna coppia con ','. Se non ci sono entitร da estrarre, rispondi con '&&NOENT&&'.\nTesto: '{{text}}'\nEntitร : |
| 8 | Devi svolgere un compito di riconoscimento delle entitร nei testi. Estrai tutte le entitร di tipo PER (persona), LOC (luogo) e ORG (organizzazione) dal testo seguente. Riporta ogni entitร con il formato: Entitร $Tipo, separando ciascuna coppia con ','. Se non ci sono entitร da estrarre, rispondi con '&&NOENT&&'.\nTesto: '{{text}}'\nEntitร : |
Combined Performance = (1 - (Best Prompt - Prompt Average) / 100) * Best Prompt. Prompt Average = F1 averaged over the 2 prompts. Best Prompt = F1 of the best prompt. Prompt ID = ID of the best prompt (see legend above).
- "headers": [
- "Rank",
- "Size",
- "FS",
- "IS_FS",
- "Model",
- "Avg. Comb. Perf. โฌ๏ธ",
- "TE",
- "TE Prompt Average",
- "TE Prompt Std",
- "TE Best Prompt",
- "TE Best Prompt Id",
- "SA",
- "SA Prompt Average",
- "SA Prompt Std",
- "SA Best Prompt",
- "SA Best Prompt Id",
- "HS",
- "HS Prompt Average",
- "HS Prompt Std",
- "HS Best Prompt",
- "HS Best Prompt Id",
- "AT",
- "AT Prompt Average",
- "AT Prompt Std",
- "AT Best Prompt",
- "AT Best Prompt Id",
- "WIC",
- "WIC Prompt Average",
- "WIC Prompt Std",
- "WIC Best Prompt",
- "WIC Best Prompt Id",
- "FAQ",
- "FAQ Prompt Average",
- "FAQ Prompt Std",
- "FAQ Best Prompt",
- "FAQ Best Prompt Id",
- "LS",
- "LS Prompt Average",
- "LS Prompt Std",
- "LS Best Prompt",
- "LS Best Prompt Id",
- "SU",
- "SU Prompt Average",
- "SU Prompt Std",
- "SU Best Prompt",
- "SU Best Prompt Id",
- "Comb. Perf. โฌ๏ธ",
- "Prompt Average",
- "Prompt Std",
- "Best Prompt",
- "Best Prompt Id",
- "REL",
- "REL Prompt Average",
- "REL Prompt Std",
- "REL Best Prompt",
- "REL Best Prompt Id",
- "#Params (B)"
- "data": [
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- "metadata": null
Relation Extraction (REL) --- Generative task
The input is a sentence of a clinical text. The model must identify and extract relationships between laboratory test results (e.g., blood pressure like 120/80 mmHg) and the corresponding tests or procedures that generated them (e.g., blood pressure test).
| # | Prompt |
|---|---|
| 7 | Dato un documento medico devi estrarre tutte le misurazioni degli esami medici presenti. Riporta ogni relazione nel formato: misurazione$esame, separando ciascuna coppia con '%'. Se non ci sono relazioni da estrarre, rispondi con '&&NOREL&&'.\nTesto: '{{text}}'\nRelazioni: |
| 8 | Devi svolgere un compito di estrazione di relazioni da documenti medici. Dato un documento medico devi estrarre tutte le misurazioni degli esami medici presenti. Riporta ogni relazione nel formato: misurazione$esame, separando ciascuna coppia con '%'. Se non ci sono relazioni da estrarre, rispondi con '&&NOREL&&'.\nTesto: '{{text}}'\nRelazioni: |
Combined Performance = (1 - (Best Prompt - Prompt Average) / 100) * Best Prompt. Prompt Average = F1 averaged over the 2 prompts. Best Prompt = F1 of the best prompt. Prompt ID = ID of the best prompt (see legend above).
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- "SA",
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- "HS",
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- "LS",
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- "SU",
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- "Comb. Perf. โฌ๏ธ",
- "Prompt Average",
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- "metadata": null
This project has benefited from the following support:
๐ง Codebase: Based on and extended from the Open Italian LLM Leaderboard, developed by Alessandro Ercolani and Samuele Colombo. We warmly thank them for their invaluable support and guidance in implementing this leaderboard.
๐ถ Funding: Partially supported by the PNRR project FAIR - Future AI Research (PE00000013), under the NRRP MUR program funded by NextGenerationEU.
๐ฅ๏ธ Computation: We gratefully acknowledge CINECA for granting access to the LEONARDO supercomputer.