Estimating Optimal Context Length for Hybrid Retrieval-augmented Multi-document Summarization

Adithya Pratapa, Teruko Mitamura

Keywords: retrieval-augmented generation, long-context, multi-document summarization

TL;DR: We present a novel method to estimate optimal context length for retrieval-augmented generation. Our estimate is a function of the retriever, summarizer and the downstream task.

Abstract: Recent advances in long-context reasoning abilities of language models led to interesting applications in large-scale multi-document summarization. However, prior work has shown that these long-context models are not effective at their claimed context windows. To this end, retrieval-augmented systems provide an efficient and effective alternative. However, their performance can be highly sensitive to the choice of retrieval context length. In this work, we present a hybrid method that combines retrieval-augmented systems with long-context windows supported by recent language models. Our method first estimates the optimal retrieval length as a function of the retriever, summarizer, and dataset. On a randomly sampled subset of the dataset, we use a panel of LMs to generate a pool of silver references. We use these silver references to estimate the optimal context length for a given RAG system configuration. Our results on the multi-document summarization task showcase the effectiveness of our method across model classes and sizes. We compare against length estimates from strong long-context benchmarks such as RULER and HELMET. Our analysis also highlights the effectiveness of our estimation method for very long-context LMs and its generalization to new classes of LMs.

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Submission Number: 1733