ECoRAG framework enhances LLM performance in ODQA by compressing retrieved documents based on evidentiality, reducing latency and token usage.
Large Language Models (LLMs) have shown remarkable performance in Open-Domain
Question Answering (ODQA) by leveraging external documents through
Retrieval-Augmented Generation (RAG). To reduce RAG overhead, from longer
context, context compression is necessary. However, prior compression methods
do not focus on filtering out non-evidential information, which limit the
performance in LLM-based RAG. We thus propose Evidentiality-guided RAG, or
ECoRAG framework. ECoRAG improves LLM performance by compressing retrieved
documents based on evidentiality, ensuring whether answer generation is
supported by the correct evidence. As an additional step, ECoRAG reflects
whether the compressed content provides sufficient evidence, and if not,
retrieves more until sufficient. Experiments show that ECoRAG improves LLM
performance on ODQA tasks, outperforming existing compression methods.
Furthermore, ECoRAG is highly cost-efficient, as it not only reduces latency
but also minimizes token usage by retaining only the necessary information to
generate the correct answer. Code is available at
https://github.com/ldilab/ECoRAG.