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README.md

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+---
+license: cc-by-4.0
+task_categories:
+- translation
+language:
+- es
+- en
+- ca
+- pt
+- fr
+- eu
+- gl
+- de
+- nl
+- el
+- it
+size_categories:
+- 1M<n<10M
+configs:
+- config_name: default
+  data_files:
+    - split: train
+      path: acadtrain.parquet
+    - split: test
+      path: acadbench/instruct_acadbench.json
+---
+
+# Dataset Card for ACAData
+
+## Dataset Description
+
+- **Point of Contact:** langtech@bsc.es
+
+
+### Dataset Summary
+
+ACAData is a multilingual instruction tuning dataset containing parallel text paragraphs from the academic domain.
+
+
+### Supported Tasks and Leaderboards
+
+The dataset is meant to be used for fine-tuning and benchmarking general purpose LLM's on Machine Translation tasks.
+
+### Languages
+
+The dataset contains (mainly long) paragraph of scientific texts from the academic domain in many European language pairs.
+The language coverage and distribution of the dataset is represented in the following tables. For further details, we refer to the paper [**add paper ref as soon as available*]() (to be published).
+
+
+## Dataset Structure
+
+ACAData is composed of two different subsets: **ACAD-Train** and **ACAD-Bench**. The first is intended for training while the second serves as the benchmarking split.
+
+**IMPORTANT**: 
+
+**ACAD-Train** is released in raw format as a Parquet file where each row contains a paragraph aligned across multiple languages, with one language per column, with a total number of **739,211** raw instances.
+This corresponds to the dataset before conversion into the instruction format described in [CITE PAPER]. During conversion, each parallel pair is used to generate two instruction instances (one per translation direction), resulting in **1,478,422** training instances.
+
+**ACAD-Bench**, on the other hand, is released directly in instruction format, where each pair has already been duplicated and swapped to cover both translation directions.
+Total number of instances: **5,944**. ACAD-Bench is ready to be used for model evaluation, as detailed in the Evaluation section of [PUT PAPER HERE].
+
+
+### Data Instances
+
+The key characteristics of ACAD-Train are the following: 
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/66703ae2d5c5d8fd8d999a44/0X5uUYLFYPok6r-5eNxe0.png)
+
+The key characteristics of ACAD-Bench are the following:
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/66703ae2d5c5d8fd8d999a44/fXuOo9P-ohZC1RDstXBpC.png)
+
+
+The [`acadtrain.parquet` file](https://huggingface.co/datasets/LangTech-MT/ACAData/blob/main/acadtrain.parquet) has the following structure:
+
+
+```markdown
+  lang1_code  lang2_code  lang1                                               lang2                                               lang1_prob  lang2_prob  alignment
+0        ast         ca  Introducción al analisis forense con distribuc...  Introducció a l'anàlisi forense...                        0.982162    0.999984   0.963158
+1        ast         ca  Creación de un almacén de datos...                 Creació d'un magatzem de dades ...                        0.847277    0.999991   0.990240
+2        ast         ca  Monografía ilustrada sobre la i...                 Monografia il·lustrada sobre la...                        0.848437    0.990025   0.980378
+3        ast         ca  Entrevista con el escritor alba...                 Entrevista amb l'escriptor alba...                        0.803880    0.979105   0.995741
+4        ast         en  Afondamos nesti trabayu con abo...                 Following the short essay Topon...                        0.999985    0.983456   0.912953
+```
+
+On the other hand, the instances of the [`instruct_acadbench.parquet` file](https://huggingface.co/datasets/LangTech-MT/ACAData/blob/main/acadbench/instruct_acadbench.json) have the following structure (Catalan → English example):
+
+```json
+{
+    "id": "test_ca-en_abstract_dataset_{idx}",
+    "task": "abstract_dataset",
+    "lang": "ca-en",
+    "conversations": [
+      {
+        "from": "human",
+        "value": "Translate the following text from Catalan to English.\nCatalan: {lang1}\:"
+      },
+      {
+        "from": "gpt",
+        "value": "{lang2}"
+      }
+    ]
+  },
+```
+
+In this case, given the human input, the model outputs the translation. The model's output is then compared against the target ({lang2}) for benchmarking.
+
+
+
+### Data Fields
+
+- **lang1_code**: ISO language code of the text in **lang1** (the first text in the pair).
+- **lang2_code**: ISO language code of the text in **lang2** (the second text in the pair).
+- **lang1**: The first text in the bilingual instance.
+- **lang2**: The second text in the bilingual instance.
+- **lang1_prob**: Language identification probability for **lang1** (GlotLid).
+- **lang2_prob**: Language identification probability for **lang2** (GlotLid).
+- **alignment**: Cosine similarity between the embeddings of **lang1** and **lang2** (LaBSE).
+
+
+### Data Splits
+
+The dataset contains two splits: `train`(ACAD-Train)  and `benchmarking` (ACAD-Bench).
+
+## Dataset Creation
+
+### Curation Rationale
+
+This dataset is aimed at improving the Machine Translation performance of LLM's in the academic domain.
+
+### Source Data
+
+Translation pairs were harvested from the metadata of multiple European Academic repositories using the [OAI-PMH protocol](https://www.openarchives.org/OAI/openarchivesprotocol.html). For each harvested metadata record we extracted the textual content from the record’s "description" field and used those texts as the source for candidate segments.
+
+#### Initial Data Collection and Normalization
+
+Using OAI-PMH, we inspected each record’s description field to detect multiple entries. When multiple entries were present, we extracted embeddings for each entry with [LaBSE](https://huggingface.co/sentence-transformers/LaBSE), computed pairwise cosine similarities, and selected translation pairs with similarity ≥ 0.80. Language identification was then performed using [GlotLID](https://github.com/cisnlp/GlotLID).
+
+For normalization, we applied preprocessing before embedding and language ID: stripped leading language markers (e.g., “(Spanish)”, “(eng)”); normalized punctuation and typography (converted all quotation marks and apostrophes to ASCII equivalents, replaced masculine ordinals “º” with degree symbols “°”, and converted superscript/subscript digits to regular digits); removed common inline markers (short bracketed/parenthesized codes, leading // or :); collapsed simple HTML tags; and collapsed repeated whitespace into single spaces. We also applied Unicode NFKC normalization and, where appropriate, lowercasing to ensure consistent tokenization and more stable embeddings.
+
+#### Who are the source language producers?
+
+In the following table, we provide a complete list of the source repositories from where the data were extracted (**the shown number of instances is before deduplication**).
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/646772c2b990713c5031e8f9/0fnlMtUMw8eFxLt78PZAs.png)
+
+### Annotations
+
+#### Annotation process
+
+The dataset does not contain any annotations.
+
+#### Who are the annotators?
+
+[N/A]
+
+### Personal and Sensitive Information
+
+No specific anonymisation process has been applied. Personal and sensitive information might be present in the data. This needs to be considered when using the data for fine-tuning models.
+
+
+## Evaluation
+
+
+Aggregated results for the XX ↔ EN and XX ↔ ES translation directions in ACAD-Bench dataset. Baselines are grouped into **large-scale proprietary general models**, **medium- to small-sized open-weights models** and **dedicated MMNMT models**. For every metric, the top-scoring system is shown in **bold**. For a more detailed evaluation analysis, please refer to the paper.
+
+
+<details>
+<summary>xx → en</summary>
+
+| Direction | Model | d-BLEU | BP | Blonde | Comet | Comet-Kiwi |
+| :--- | :--- | :---: | :---: | :---: | :---: | :---: |
+| XX → EN | GPT-mini | 46.03 | **1.00** | 0.60 | **0.84** | 0.77 |
+| | GPT-nano | 41.30 | 0.97 | 0.55 | **0.84** | **0.78** |
+| | Gemini-2 | 48.65 | **1.00** | 0.61 | **0.84** | 0.77 |
+| | Gemini-2.5 | 45.10 | 0.98 | 0.58 | **0.84** | 0.77 |
+| | Llama-3-8B | 43.12 | 0.99 | 0.56 | 0.83 | 0.76 |
+| | Gemma-3-27B | 46.37 | 0.98 | 0.59 | **0.84** | 0.77 |
+| | MADLAD-7B | 38.69 | 0.86 | 0.51 | 0.81 | 0.77 |
+| | Salamandra-2B | 37.09 | 0.92 | 0.52 | 0.82 | 0.75 |
+| | &nbsp;&nbsp;+ ACADTRAIN | 48.45 | **1.00** | 0.61 | 0.83 | 0.76 |
+| | Salamandra-7B | 45.87 | 0.99 | 0.59 | 0.83 | 0.76 |
+| | &nbsp;&nbsp;+ ACADTRAIN | **50.07** | **1.00** | **0.62** | **0.84** | 0.76 |
+
+</details>
+
+
+<details>
+<summary>en → xx</summary>
+
+| Direction | Model | d-BLEU | BP | Blonde | Comet | Comet-Kiwi |
+| :--- | :--- | :---: | :---: | :---: | :---: | :---: |
+| EN → XX | GPT-mini | 45.01 | 0.99 | - | 0.86 | **0.82** |
+| | GPT-nano | 43.78 | **1.00** | - | 0.86 | **0.82** |
+| | Gemini-2 | 48.00 | 0.99 | - | **0.87** | **0.82** |
+| | Gemini-2.5 | 47.75 | 0.99 | - | **0.87** | **0.82** |
+| | Llama-3-8B | 39.87 | 0.99 | - | 0.85 | 0.81 |
+| | Gemma-3-27B | 46.29 | 0.99 | - | 0.86 | **0.82** |
+| | MADLAD-7B | 36.08 | 0.82 | - | 0.83 | 0.80 |
+| | Salamandra-2B | 32.91 | 0.90 | - | 0.83 | 0.78 |
+| | &nbsp;&nbsp;+ ACADTRAIN | 46.86 | 0.98 | - | 0.86 | 0.81 |
+| | Salamandra-7B | 42.55 | 0.98 | - | 0.86 | 0.81 |
+| | &nbsp;&nbsp;+ ACADTRAIN | **49.20** | 0.98 | - | 0.86 | 0.81 |
+
+</details>
+
+
+<details>
+<summary>xx → es</summary>
+
+| Direction | Model | d-BLEU | BP | Blonde | Comet | Comet-Kiwi |
+| :--- | :--- | :---: | :---: | :---: | :---: | :---: |
+| XX → ES | GPT-mini | 60.60 | 0.98 | - | 0.86 | **0.82** |
+| | GPT-nano | 57.88 | **0.99** | - | 0.86 | **0.82** |
+| | Gemini-2 | 62.02 | 0.99 | - | 0.86 | **0.82** |
+| | Gemini-2.5 | 61.43 | 0.98 | - | **0.87** | **0.82** |
+| | Llama-3-8B | 55.4 | 0.98 | - | 0.86 | 0.81 |
+| | Gemma-3-27B | 60.71 | 0.98 | - | 0.86 | **0.82** |
+| | MADLAD-7B | 43.44 | 0.76 | - | 0.83 | 0.81 |
+| | Salamandra-2B | 50.09 | 0.92 | - | 0.85 | 0.80 |
+| | &nbsp;&nbsp;+ ACADTRAIN | 61.97 | 0.98 | - | 0.86 | **0.82** |
+| | Salamandra-7B | 57.55 | 0.98 | - | 0.86 | **0.82** |
+| | &nbsp;&nbsp;+ ACADTRAIN | **63.60** | 0.98 | - | 0.86 | **0.82** |
+
+</details>
+
+
+<details>
+<summary>es → xx</summary>
+
+| Direction | Model | d-BLEU | BP | Blonde | Comet | Comet-Kiwi |
+| :--- | :--- | :---: | :---: | :---: | :---: | :---: |
+| ES → XX | GPT-mini | 54.19 | **0.99** | - | **0.86** | **0.81** |
+| | GPT-nano | 51.95 | **0.99** | - | **0.86** | **0.81** |
+| | Gemini-2 | 60.28 | **0.99** | - | **0.86** | **0.81** |
+| | Gemini-2.5 | 57.61 | **0.99** | - | **0.86** | **0.81** |
+| | Llama-3-8B | 52.12 | **0.99** | - | 0.85 | 0.80 |
+| | Gemma-3-27B | 57.31 | **0.99** | - | **0.86** | **0.81** |
+| | MADLAD-7B | 40.13 | 0.79 | - | 0.83 | **0.81** |
+| | Salamandra-2B | 47.84 | 0.94 | - | 0.84 | 0.80 |
+| | &nbsp;&nbsp;+ ACADTRAIN | 60.09 | **0.99** | - | **0.86** | **0.81** |
+| | Salamandra-7B | 55.65 | 0.98 | - | **0.86** | 0.80 |
+| | &nbsp;&nbsp;+ ACADTRAIN | **61.61** | **0.99** | - | **0.86** | **0.81** |
+
+</details>
+
+
+
+## Considerations for Using the Data
+
+### Discussion of Biases
+
+No specific bias mitigation strategies were applied to this dataset. 
+Inherent biases may exist within the data.
+
+### Other Known Limitations
+
+The dataset contains data of the academic domain. Applications of this dataset in domains or languages not included in the training set would be of limited use.
+
+## Additional Information
+
+### Dataset Curators
+
+Language Technologies Unit at the Barcelona Supercomputing Center (langtech@bsc.es).
+
+### Funding
+
+This work is funded by the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the project Modelos del Lenguaje. 
+
+This work has been promoted and financed by the Government of Catalonia through the [Aina project](https://projecteaina.cat/).
+
+This work is funded by the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the [project ILENIA](https://proyectoilenia.es/) with reference 2022/TL22/00215337.
+
+
+### Licensing Information
+
+This work is licensed under an [Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) license.
+
+### Citation Information
+
+[N/A]
+
+### Contributions
+
+[N/A]

acadbench/instruct_acadbench.json

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acadtrain.parquet

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