SSSS connor/CrossEncoderRanker
Rank 1: Figure 1: An illustration of the HyDE model. Document snippets are shown. HyDE serves all types of queries without changing the underlying InstructGPT and Contriever/mContriever models.

GPT-3 (Brown et al., 2020) models can be aligned to human intents to follow instructions faithfully.

With these 
Rank 2: |           | Scifact | FiQA  | DBPedia |
|-----------|---------|-------|---------|
| Contriever | 64.9    | 24.5  | 29.2    |
| HyDE w/ InstructGPT | **69.1**  | 27.3  | 36.8    |
| w/ GPT-3  | 65.9    | **27.9**  | **40.5**    |

Table 5: nDCG@10 comparing InstructGPT vs. 3-shot GPT-3 on BEIR. Bes
Rank 3: 6 Conclusion

In this paper, we introduce HyDE, a new approach for building effective dense retrievers in a completely unsupervised manner, without the need for any relevance labels. We demonstrate that some aspects of relevance modeling can be delegated to a more powerful, flexible, and general-pur
Rank 4: estimate Equation 5 by sampling N documents from g, [d̂1, d̂2, ..., d̂N]:

v̂qij = 1/N ∑_(d̂k ∼ g(qij, INSTi)) f(d̂k)    (6)

      = 1/N ∑_(k=1)^N f(d̂k)                        (7)

We also consider the query as a possible hypothesis:

v̂qij = 1/(N+1) [(∑_(k=1)^N f(d̂k)) + f(qij)]      (8)

Inner p
Rank 5: |           | sw  | wo  | ko   | ja   | bn   |
|-----------|-----|-----|------|------|------|
| **Unsupervised**         |     |     |      |      |      |
| BM25      | 38.9| 28.5| 21.2 | 41.8 |      |
| mContriever| 38.3| 22.3| 19.5 | 35.3 |      |
| HyDE      | 41.7| 30.6| 30.7 | 41.3 |      |
| 

SSSS fadeleke/prompt-to-signature
Signature generation took 0.00 seconds in inference.
Prediction(
    signature_name='DocumentSummaryAndEntityExtraction',
    task_description='Extract a concise summary of a document and identify key entities (people, organizations, locations, dates, etc.) mentioned within it.',
    signature_fields=[GeneratedField(name='document', type=<FieldType.STRING: 'str'>, role=<FieldRole.INPUT: 'input'>, description='The document text to summarize and extract entities from', pydantic_model_schema=None, literal_values=None, default_value=None), GeneratedField(name='summary', type=<FieldType.STRING: 'str'>, role=<FieldRole.OUTPUT: 'output'>, description="A concise summary of the document's main points and content", pydantic_model_schema=None, literal_values=None, default_value=None), GeneratedField(name='key_entities', type=<FieldType.PYDANTIC_MODEL: 'pydantic'>, role=<FieldRole.OUTPUT: 'output'>, description='Structured extraction of key entities found in the document', pydantic_model_schema=PydanticModelSchema(model_name='KeyEntities', description='Container for extracted entities from the document', fields=[PydanticFieldDef(name='people', type=<FieldType.LIST_STRING: 'list[str]'>, description='Names of people mentioned in the document', required=False, literal_values=None, nested_model=None), PydanticFieldDef(name='organizations', type=<FieldType.LIST_STRING: 'list[str]'>, description='Names of organizations, companies, or institutions mentioned', required=False, literal_values=None, nested_model=None), PydanticFieldDef(name='locations', type=<FieldType.LIST_STRING: 'list[str]'>, description='Geographic locations, cities, countries, or regions mentioned', required=False, literal_values=None, nested_model=None), PydanticFieldDef(name='dates', type=<FieldType.LIST_STRING: 'list[str]'>, description='Important dates or time references mentioned in the document', required=False, literal_values=None, nested_model=None), PydanticFieldDef(name='topics', type=<FieldType.LIST_STRING: 'list[str]'>, description='Main topics or themes covered in the document', required=False, literal_values=None, nested_model=None)]), literal_values=None, default_value=None)],
    reasoning=None
)