ofermend commited on
Commit
ade58fc
1 Parent(s): 04f8b0f

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +3 -2
README.md CHANGED
@@ -13,9 +13,10 @@ HHEM-2.1-Open is a major upgrade to [HHEM-1.0-Open](https://huggingface.co/vecta
13
  If you are interested to learn more about RAG or experiment with Vectara, you can [sign up](https://console.vectara.com/signup/?utm_source=huggingface&utm_medium=space&utm_term=hhem-model&utm_content=console&utm_campaign=) for a free Vectara account.
14
 
15
  ## Hallucination Detection 101
16
- By "hallucinated" or "factually inconsistent", we mean that a text (hypothesis, to be judged) is not supported by another text (evidence/premise, given). You **always need two** pieces of text to determine whether a text is hallucinated or not. When applied to RAG (retrieval augmented generation), the LLM is provided by several pieces of text (often called facts or context) retrieved from some dataset, and a hallucination would indicate that the summary (hypothesis) is not supported by those facts (evidence).
17
 
18
- A common type of hallucination in RAG is **factual but hallucinated**. For example, given the premise _"The capital of France is Berlin"_, the hypothesis _"The capital of France is Paris"_ is hallucinated -- although it is true in the world knowledge. This happens when LLMs do not generate content based on the textual data provided to them as part of the RAG retrieval process, but rather generate content based on their pre-trained knowledge.
 
19
 
20
  ## Using HHEM-2.1-Open
21
 
 
13
  If you are interested to learn more about RAG or experiment with Vectara, you can [sign up](https://console.vectara.com/signup/?utm_source=huggingface&utm_medium=space&utm_term=hhem-model&utm_content=console&utm_campaign=) for a free Vectara account.
14
 
15
  ## Hallucination Detection 101
16
+ By "hallucinated" or "factually inconsistent", we mean that a text (hypothesis, to be judged) is not supported by another text (evidence/premise, given). You **always need two** pieces of text to determine whether a text is hallucinated or not. When applied to RAG (retrieval augmented generation), the LLM is provided with several pieces of text (often called facts or context) retrieved from some dataset, and a hallucination would indicate that the summary (hypothesis) is not supported by those facts (evidence).
17
 
18
+ A common type of hallucination in RAG is **factual but hallucinated**.
19
+ For example, given the premise _"The capital of France is Berlin"_, the hypothesis _"The capital of France is Paris"_ is hallucinated -- although it is true in the world knowledge. This happens when LLMs do not generate content based on the textual data provided to them as part of the RAG retrieval process, but rather generate content based on their pre-trained knowledge.
20
 
21
  ## Using HHEM-2.1-Open
22