| --- |
| license: cc-by-4.0 |
| task_categories: |
| - translation |
| - text-generation |
| language: |
| - fr |
| - en |
| - it |
| - sk |
| tags: |
| - llm |
| - translation |
| - back-translation |
| - semantic-drift |
| - embeddings |
| - openrouter |
| pretty_name: LLM repeated back-translation trajectories |
| configs: |
| - config_name: translations |
| data_files: |
| - split: train |
| path: translations.jsonl |
| - config_name: experiments |
| data_files: |
| - split: train |
| path: experiments.jsonl |
| - config_name: sources |
| data_files: |
| - split: train |
| path: sources.jsonl |
| - config_name: known_failures |
| data_files: |
| - split: train |
| path: known_failures.csv |
| - config_name: pivot_to_original_metrics |
| data_files: |
| - split: train |
| path: pivot_to_original_metrics.csv |
| - config_name: pivot_pairwise_metrics |
| data_files: |
| - split: train |
| path: pivot_pairwise_metrics.csv |
| - config_name: pivot_to_original_metrics_openai_3_small |
| data_files: |
| - split: train |
| path: pivot_to_original_metrics_openai_3_small.csv |
| - config_name: pivot_to_original_metrics_voyageai_voyage_4 |
| data_files: |
| - split: train |
| path: pivot_to_original_metrics_voyageai_voyage_4.csv |
| - config_name: pivot_pairwise_metrics_openai_3_small |
| data_files: |
| - split: train |
| path: pivot_pairwise_metrics_openai_3_small.csv |
| - config_name: pivot_pairwise_metrics_voyageai_voyage_4 |
| data_files: |
| - split: train |
| path: pivot_pairwise_metrics_voyageai_voyage_4.csv |
| --- |
| |
| # LLM repeated back-translation trajectories |
|
|
| What happens to a text when it is translated back and forth repeatedly by an LLM? |
|
|
| This exploratory dataset starts from short French source texts and sends them through different pivot languages, one translation at a time. Each translation step is a new, stateless API call: the model receives only the fixed translation instruction, the target language, and the previous step's text. |
|
|
| The dataset is designed to explore whether repeated translation produces semantic drift, surface rewriting, compression, convergence, model-specific failures, or pivot-language effects. |
|
|
| ## Protocol |
|
|
| For a French source and a pivot language: |
|
|
| ```text |
| FR0 → pivot1 → FR2 → pivot3 → FR4 → ... → FR50 |
| ``` |
|
|
| - `step = 0`: original French source text. |
| - Odd steps: translation into the pivot language. |
| - Even steps: translation back into French. |
| - Metrics are computed on returned-to-French steps only: `2, 4, ..., 50`, against the original `FR0`. |
| - Each translation is an independent API call; no conversation history is kept. |
|
|
| ## Main experiment dimensions |
|
|
| Sources: |
|
|
| - `source_02`: Alexandre Dumas, *Le Comte de Monte-Cristo* excerpt. |
| - `source_06`: Marcel Proust, *À l'ombre des jeunes filles en fleurs* excerpt. |
| - `source_07`: French mathematical/pigeonhole-principle text. |
|
|
| Pivot languages: |
|
|
| - English |
| - Italian |
| - Slovak |
|
|
| Translation models: |
|
|
| - `openai/gpt-4o-mini` |
| - `poolside/laguna-s-2.1` |
| - `thinkingmachines/inkling-small` |
|
|
| Embedding models used for semantic metrics: |
|
|
| - `openai/text-embedding-3-small` |
| - `voyageai/voyage-4` |
|
|
| ## Files |
|
|
| ### `translations.jsonl` |
|
|
| Raw translation trajectories. This is the primary dataset file. It contains every step, including odd pivot-language steps and even returned-to-French steps. |
|
|
| Important fields: |
|
|
| - `experiment_id` |
| - `timestamp` |
| - `translation_model` |
| - `provider` |
| - `pivot_language` |
| - `language_pair` |
| - `source_id` |
| - `step` |
| - `language` — requested/expected language for that step |
| - `text` — raw model output, minimally stripped of transport-level newlines only |
|
|
| ### `experiments.jsonl` |
|
|
| Run-level metadata: |
|
|
| - model and provider |
| - generation parameters |
| - prompt template |
| - selected source metadata |
| - maximum translation steps |
| - pivot language |
|
|
| Local filesystem/platform details are intentionally excluded from the exported dataset. |
|
|
| ### `pivot_to_original_metrics.csv` |
| |
| Combined metrics against the original French text for both embedding models. |
| |
| One row is roughly: |
| |
| ```text |
| source × translation_model × pivot_language × step × embedding_model |
| ``` |
| |
| Key metrics: |
| |
| - `semantic_similarity_to_original`: cosine similarity between embeddings of `FR0` and `FRn`. |
| - `word_overlap_to_original`: Jaccard similarity of unique lowercased words. |
| - `edit_similarity_to_original`: normalized word-level edit similarity. |
| - `word_count_ratio_to_original`: word count at step `n` divided by word count at step `0`. |
| |
| ### `pivot_pairwise_metrics.csv` |
| |
| Pairwise comparison between outputs produced through different pivot languages at the same source/model/step. |
| |
| This is useful for studying whether translation converges to a universal representation or to pivot-conditioned basins. |
| |
| ### Embedding-specific metric files |
| |
| The combined metric files above are also provided split by embedding model: |
| |
| - `pivot_to_original_metrics_openai_3_small.csv` |
| - `pivot_to_original_metrics_voyageai_voyage_4.csv` |
| - `pivot_pairwise_metrics_openai_3_small.csv` |
| - `pivot_pairwise_metrics_voyageai_voyage_4.csv` |
| |
| ### `embeddings/` |
| |
| Raw embedding arrays and row indexes: |
| |
| ```text |
| embeddings/embeddings_openai_3_small.npy |
| embeddings/embedding_index_openai_3_small.json |
| embeddings/embeddings_voyageai_voyage_4.npy |
| embeddings/embedding_index_voyageai_voyage_4.json |
| ``` |
| |
| Embeddings are derived artifacts and are kept separate from raw translations. |
| |
| ### `sources/` and `sources.jsonl` |
| |
| Human-readable source Markdown files with front matter, plus a JSONL summary of source metadata. |
| |
| ### `known_failures.csv` |
| |
| Known obvious output failures detected in the raw trajectories. For example, one model/pivot/source configuration produced premature end markers such as `Fin du texte.` / `Koniec textu.`. These records are preserved in `translations.jsonl` because raw outputs are experimental data, but they are flagged here for analysis. |
| |
| ### `openrouter_models.json` |
| |
| Cached OpenRouter model metadata snapshot, if available, including context limits, max output tokens, and pricing metadata. |
| |
| ## Translation prompt |
| |
| The same prompt template was used for all translations: |
| |
| ```text |
| Translate the following text into {target_language}. |
| |
| Preserve the meaning, level of certainty, terminology, structure, and style as faithfully as possible. |
| |
| Return only the translated text. Do not add explanations, comments, notes, quotation marks, or formatting not present in the source. |
| |
| TEXT: |
| {text} |
| ``` |
| |
| ## Preliminary observations |
| |
| The dataset supports several exploratory observations: |
| |
| - Some model/pivot combinations show a sharp initial projection followed by a plateau. |
| - Literary texts can lose substantial surface/style similarity while preserving broad semantic similarity. |
| - Pivot language matters: different pivots can produce different stable French paraphrases. |
| - Model choice matters: some models appear more stable than others under repeated translation. |
| - Some trajectories can fail instruction-following or collapse into end markers; these are preserved and flagged. |
| |
| These are exploratory observations, not claims of universal behavior. |
| |
| ## Limitations |
| |
| - The dataset is small and exploratory. |
| - `language` is the requested language, not automatically detected language. |
| - Some model outputs may be malformed or in the wrong language. |
| - Metrics are approximations: embedding similarity does not capture all stylistic or legal/literary nuances. |
| - Source licensing/provenance should be reviewed carefully before reuse in downstream publications. |
| |
| ## Suggested use |
| |
| This dataset is useful for: |
| |
| - studying repeated LLM translation trajectories; |
| - comparing semantic vs surface-form preservation; |
| - exploring model and pivot-language effects; |
| - building visual tools for inspecting raw translation outputs behind metrics. |
| |
