Text Summarization refers to the process of condensing and distilling the content of a piece of text, such as an article, document, or paragraph, into a shorter version while retaining its most important information and meaning. Text summarization is a crucial task facilitated by LLMs, which are powerful natural language processing models capable of understanding and generating human-like text.

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Optimizing LLM API usage costs with novel query-aware reduction of relevant enterprise data

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Costs of LLM API usage rise rapidly when proprietary enterprise data is used as context for user queries to generate more accurate responses from LLMs. To reduce costs, we propose LeanContext, which generates query-aware, compact and AI model-friendly summaries of relevant enterprise data context. This is unlike traditional summarizers that produce query-unaware human-friendly summaries that are also not as compact. We first use retrieval augmented generation (RAG) to generate a query-aware enterprise data context, which includes key, query-relevant enterprise data. Then, we use reinforcement learning to further reduce the context while ensuring that a prompt consisting of the user query and the reduced context elicits an LLM response that is just as accurate as the LLM response to a prompt that uses the original enterprise data context. Our reduced context is not only query-dependent, but it is also variable-sized. Our experimental results demonstrate that LeanContext (a) reduces costs of LLM API usage by 37% to 68% (compared to RAG), while maintaining the accuracy of the LLM response, and (b) improves accuracy of responses by 26% to 38% when state-of-the-art summarizers reduce RAG context.

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LeanContext: Cost-efficient Domain-specific Question Answering Using LLMs

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Question-answering (QA) is a significant application of Large Language Models (LLMs), shaping chatbot capabilities across healthcare, education, and customer service. However, widespread LLM integration presents a challenge for small businesses due to the high expenses of LLM API usage. Costs rise rapidly when domain-specific data (context) is used alongside queries for accurate domain-specific LLM responses. Extracting context from domain-specific data is implemented by a Retrieval Augmented Generation (RAG) approach. One option is to summarize the RAG context by using LLMs and reduce the context. However, this can also filter out useful information that is necessary to answer some domain-specific queries. In this paper, we shift from human-oriented summarizers to AI model-friendly summaries. Our approach, LeanContext, efficiently extracts k key sentences from the context that are closely aligned with the query. The choice of k is neither static nor random; we introduce a reinforcement learning technique that dynamically determines k based on the query and context. The rest of the less important sentences are either reduced using a free open-source text reduction method or eliminated. We evaluate LeanContext against several recent query-aware and query-unaware context reduction approaches on prominent datasets (arxiv papers and BBC news articles, NarrativeQA). Despite cost reductions of 37.29% to 67.81%, LeanContext’s ROUGE-1 score decreases only by 1.41% to 2.65% compared to a baseline that retains the entire context (no summarization). LeanContext stands out for its ability to provide precise responses, outperforming competitors by leveraging open-source summarization techniques. Human evaluations of the responses further confirm and validate this superiority. Additionally, if open-source pre-trained LLM-based summarizers are used to reduce context (into human consumable summaries), LeanContext can further modify the reduced context to enhance the accuracy (ROUGE-1 score) by 13.22% to 24.61%.

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