Rank It, Then Ask It: Input Reranking for Maximizing the Performance of LLMs on Symmetric Tasks

• URL: http://arxiv.org/abs/2412.00546v1
• Date: Sat, 30 Nov 2024 17:39:59 GMT
• Title: Rank It, Then Ask It: Input Reranking for Maximizing the Performance of LLMs on Symmetric Tasks
• Authors: Mohsen Dehghankar, Abolfazl Asudeh,
• Abstract summary: Large language models (LLMs) have quickly emerged as practical and versatile tools. We consider the application of LLMs on symmetric tasks where a query is asked on an (unordered) bag of elements.
• Score: 9.867695275243879
• License:
• Abstract: Large language models (LLMs) have quickly emerged as practical and versatile tools that provide new solutions for a wide range of domains. In this paper, we consider the application of LLMs on symmetric tasks where a query is asked on an (unordered) bag of elements. Examples of such tasks include answering aggregate queries on a database table. In general, when the bag contains a large number of elements, LLMs tend to overlook some elements, leading to challenges in generating accurate responses to the query. LLMs receive their inputs as ordered sequences. However, in this problem, we leverage the fact that the symmetric input is not ordered, and reordering should not affect the LLM's response. Observing that LLMs are less likely to miss elements at certain positions of the input, we introduce the problem of LLM input reranking: to find a ranking of the input that maximizes the LLM's accuracy for the given query without making explicit assumptions about the query. Finding the optimal ranking requires identifying (i) the relevance of each input element for answering the query and (ii) the importance of each rank position for the LLM's attention. We develop algorithms for estimating these values efficiently utilizing a helper LLM. We conduct comprehensive experiments on different synthetic and real datasets to validate our proposal and to evaluate the effectiveness of our proposed algorithms. Our experiments confirm that our reranking approach improves the accuracy of the LLMs on symmetric tasks by up to $99\%$ proximity to the optimum upper bound.

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