Update README.md

README.md

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 # Dataset Card for "databricks-dolly-15k-curated-multilingual"
 
-A curated and multilingual version of the Databricks Dolly instructions dataset.
+A curated and multilingual version of the Databricks Dolly instructions dataset. It includes a programmatically and manually corrected version of the original `en` dataset. See below.
 
 **STATUS**:
 
@@ -97,11 +97,17 @@ This dataset collection is a curated and machine-translated version of the `data
 The goal is to give practitioners a starting point for training open-source instruction-following models with better-quality English data and translated data beyond English. However, as the translation quality will not be perfect, we highly recommend dedicating time to curate and fix translation issues. Below we explain how to load the datasets into [Argilla for data curation and fixing](https://github.com/argilla-io/argilla). Additionally, we'll be improving the datasets made available here, with the help of different communities.
 
 
-Currently, the original English version has been curated combining automatic processing and collaborative human curation using Argilla (~400 records have been manually edited and fixed). The following graph shows a summary about the number of edited fields.
+Currently, the original English version has been curated combining automatic processing and collaborative human curation using Argilla (~400 records have been manually edited and fixed). The following graph shows a summary of the number of edited fields.
 
+The main issues (likely many issues still remaining) are the following:
+
+1. Some labelers misunderstood the usage of the `context` field. This `context` field is used as part of the prompt for instruction-tuning and in other works it's called `input` (e.g., Alpaca). Likely, the name context, has led to some labelers using it to provide the full context of where they have extracted the response. This is problematic for some types of tasks (summarization, closed-qa or information-extraction) because sometimes the context is shorter than or unrelated to summaries, or the information cannot be extracted from the context (closed-qa, information-extraction).
+2. Some labelers misunderstood the way to give instructions for summarization or closed-qa, for example, they ask: Who is Thomas Jefferson? then provide a very long context and a response equally long.
+
+We programmatically identified records with these potential issues and ran a campaign to fix it and as a result more than 400 records have been adapted. See below for statistics: 
 ![Edited records](edited_records.png)
 
-As a result of this curation process the content of the fields have been reduced, counted in number of tokens, especially for the responses:
+As a result of this curation process the content of the fields has been reduced, counted in number of tokens, especially for the responses:
 
 
 ![Edited records](tokens_diff.png)
@@ -139,7 +145,7 @@ As described in the README of the original dataset, this dataset can be used for
 
 ### Languages
 
-Currently: `es`, `fr`, `de`
+Currently: `es`, `fr`, `de`, `en`
 
 Join Argilla [Slack community](https://join.slack.com/t/rubrixworkspace/shared_invite/zt-whigkyjn-a3IUJLD7gDbTZ0rKlvcJ5g) if you want to help us include other languages.