Summary of Rank It, Then Ask It: Input Reranking For Maximizing the Performance Of Llms on Symmetric Tasks, by Mohsen Dehghankar et al.
Rank It, Then Ask It: Input Reranking for Maximizing the Performance of LLMs on Symmetric Tasks
by Mohsen Dehghankar, Abolfazl Asudeh
First submitted to arxiv on: 30 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Databases (cs.DB); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary LLMs have been found to be useful for a wide range of tasks, including those with unordered bags of elements. This paper focuses on applying LLMs to symmetric tasks where a query is asked about an unordered bag. Examples include answering aggregate queries on a database table. The issue arises when the bag contains many elements, causing LLMs to overlook some, leading to inaccurate responses. Since LLMs are typically fed ordered sequences, this problem requires reordering the input in a way that does not affect the model’s response. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can be used for many tasks, like answering questions about groups of things. This paper looks at how these models work with unordered groups. When there are many items, the models might miss some, which makes their answers less accurate. Since the models usually get ordered lists as input, this problem is all about figuring out how to reorder things without changing what the model says. |
