Loops On Retrieval Augmented Generation (LoRAG)
Ayush Thakur Rashmi Vashisth
This paper presents Loops On Retrieval Augmented Generation (LoRAG), a new
framework designed to enhance the quality of retrieval-augmented text
generation through the incorporation of an iterative loop mechanism. The
architecture integrates a generative model, a retrieval mechanism, and a
dynamic loop module, allowing for iterative refinement of the generated text
through interactions with relevant information retrieved from the input
context. Experimental evaluations on benchmark datasets demonstrate that LoRAG
surpasses existing state-of-the-art models in terms of BLEU score, ROUGE score,
and perplexity, showcasing its effectiveness in achieving both coherence and
relevance in generated text. The qualitative assessment further illustrates
LoRAG's capability to produce contextually rich and coherent outputs. This
research contributes valuable insights into the potential of iterative loops in
mitigating challenges in text generation, positioning LoRAG as a promising
advancement in the field.