Improving Long Document Understanding with R&R Method

Concetti Chiave

The authors introduce the R&R method to enhance long document-based question-answering by combining reprompting and in-context retrieval techniques, resulting in improved accuracy and reduced complexity.

Sintesi

The R&R method combines reprompting and in-context retrieval to address the "lost in the middle" effect in long document-based question-answering. By repeating task instructions and retrieving relevant passages, it boosts accuracy while minimizing LLM usage cost. The study compares different methods on various datasets, showcasing the benefits of R&R for improving performance on large language models.

Statistiche

We test R&R with GPT-4 Turbo and Claude-2.1 on documents up to 80k tokens in length.
A 16-point boost in QA accuracy was observed on average with R&R.
Compared to short-context chunkwise methods, R&R enables the use of larger chunks that cost fewer LLM calls and output tokens.
The fuzzy-match score is used for evaluation across different datasets and document lengths.

Citazioni

"In ICR, rather than having a long-context LLM answer a question directly, we first prompt the LLM to retrieve some number of passages from the context document that are most relevant to the question."
"Our results suggest that our method R&R can indeed be helpful to extend the context length at which LLMs operate effectively for document-based QA."

Approfondimenti chiave tratti da

Can't Remember Details in Long Documents? You Need Some R&R

by Devanshu Agr... alle arxiv.org 03-11-2024

https://arxiv.org/pdf/2403.05004.pdf

Domande più approfondite

How does reprompting impact other tasks beyond document-based QA?

Reprompting can have a positive impact on various tasks beyond document-based QA. By repeating the task instructions periodically throughout the context, it helps to reduce the distance between relevant information and the given task. This can be beneficial in tasks like text generation, where maintaining coherence and relevance throughout a long piece of text is crucial. Reprompting could also enhance chatbot interactions by ensuring that the bot stays focused on the user's query and provides accurate responses consistently.

What potential drawbacks or limitations might arise from using the R&R method?

While R&R has shown promising results in improving accuracy for long-context language models in document-based QA, there are some potential drawbacks or limitations to consider. One limitation could be an increase in computational resources required due to additional LLM calls for retrieval and reprompting steps. The complexity of managing multiple prompts and aggregating retrieved passages may also introduce challenges in scaling up this method for large datasets. Additionally, there might be cases where irrelevant information is repeated through reprompting, leading to noise that could affect model performance.

How could prompt-based approaches like R&R be applied to improve summarization tasks?

Prompt-based approaches like R&R can be adapted to enhance summarization tasks by guiding the language model towards generating concise and informative summaries from longer documents or articles. In this context, reprompting could help maintain focus on key points or themes within the text while reducing redundancy or irrelevant details. By instructing the model to retrieve essential passages before generating a summary, it ensures that only relevant information is included in the final output. This approach can lead to more coherent and accurate summaries with improved readability and content retention for users seeking condensed versions of lengthy texts.