text: A.1 All datasets 
  
  Table 3 displays the size of each dataset along with 
  the average number of tokens per sample and their 
  references. The dataset’s content was tokenized 
  using cl100k_base encoding. For Retrieval, the 
  two numbers refer to the queries and the docu- 
  ments. For Reranking, the three numbers refer to 
  the queries, the pairs of queries with relevant docu- 
  ments and the pairs of queries with irrelevant ones, 
  respectively. The pairs of queries and documents 
  are obtained from the 90 documents extracted. For 
  SummEvalFr, the three numbers refer to the texts, 
  human and machine summaries, respectively. 
  SummEvalFr Extracts of humans and machine 
  summaries translated in French from SummEvalFr 
  and the original ones in English from SummEval 
  (Fabbri et al., 2021) are shown in Figure 9. As ex- 
  plained in section 3.1.3, we use a LLM to evaluate 
  the quality of translations for human summaries, 
  we provide the prompt used with GPT-4 for this 
  evaluation in Figure 8. 
  
  Figure 3 represents the semantic similarity be- 
  tween each dataset. The methodology was as fol- 
  lows: 90 random samples per dataset are embedded 
  using the multilingual-e5-large model. The embed- 
  dings of each dataset’s samples are averaged. The 
  similarity between each dataset is then calculated 
  using cosine similarity as in (Muennighoff et al., 
  2022). 