StructuredRAG: JSON Response Formatting with Large Language Models

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Large Language Models have become extremely powerful at Zero-Shot Instruction Following. This benchmarker aims to target how well LLMs can follow the instruction of formatting its output in a particular JSON template. It is extremely important for processing Generative Feedback Loops that these outputs follow these instructions.

This benchmarker firstly compares f-String prompting with the Follow the Format (FF) method used in DSPy.

This benchmarker secondly compares Gemini with Llama3 (Ollama).

The benchmarker explores different RAG inspired tasks with structured outputs as follows:

Output Type	Task	Example
string	GenerateAnswer	{"answer": "The National Gallery of Art, Washington D.C., and the Pinacoteca di Brera, Milan, Italy."}
integer	RateContext	{"context_score": 5}
boolean	AssessAnswerability	{"answerable_question": True}
List[string]	ParaphraseQuestions	{"paraphrased_questions": ["Where can some of Vincenzo Civerchio’s works be found?", "Where are some pieces by Vincenzo Civerchio displayed?", "Where can I find some of Vincenzo Civerchio’s art?"]}
RAGASmetrics	RAGAS	{"faithfulness_score": 2.5, "answer_relevance_score": 1.0, "context_relevance_score": 3.5}
AnswerWithConfidence	GenerateAnswerWithConfidence	{"answer": "The National Gallery of Art, Washington D.C., and the Pinacoteca di Brera, Milan, Italy.", "confidence": 5}
List[AnswerWithConfidence]	GenerateAnswersWithConfidence	[{"answer": "National Gallery of Art, Washington D.C.", "confidence": 5}, {"answer": "Pinacoteca di Brera, Milan, Italy", "confidence": 4}]

Additional Models

class RAGASmetrics(BaseModel):
  faithfulness_score: float
  answer_relevance_score: float
  context_relevance_score: float

class AnswerWithConfidence(BaseModel):
  answer: str
  confidence: float

The WikiQuestions dataset can also be found on HuggingFace Datasets!

Citation

Please consider citing our paper if you find this work useful:

@misc{shorten2024,
      title={StructuredRAG: JSON Response Formatting with Large Language Models}, 
      author={Connor Shorten and Charles Pierse and Thomas Benjamin Smith and Erika Cardenas and Akanksha Sharma and John Trengrove and Bob van Luijt},
      year={2024},
      eprint={2408.11061},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2408.11061}, 
}