Summary of Enhanced Facet Generation with Llm Editing, by Joosung Lee et al.
Enhanced Facet Generation with LLM Editing
by Joosung Lee, Jinhong Kim
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper presents a significant advancement in information retrieval, specifically in facet identification of user queries. The authors acknowledge that previous studies have improved facet prediction by leveraging retrieved documents and related queries from search engines. However, these approaches are limited when applied to other applications where the search engine is not available or updated frequently. To overcome this challenge, the researchers propose two strategies for predicting facets using only query inputs without relying on external modules. The first approach employs multi-task learning to predict Search Engine Results Pages (SERPs), which enables the model to deeply understand queries. The second strategy combines a Large Language Model (LLM) with a small model to enhance facet generation, resulting in improved performance when compared to individual models. This work has implications for various applications, including internal document search and private domain search. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers better understand what people are searching for online. Right now, search engines can give users lots of results if they know what kind of information is being searched for (like a specific topic or type of content). But what if the search engine isn’t available? The researchers came up with two new ways to help computers figure out what’s being searched for without needing an external search engine. One method is like teaching a computer to predict what will show up on a search results page. Another way combines powerful language models with smaller ones to get even better results. These advancements could be useful in situations where internal documents need to be searched or when companies want to keep their online information private. |
Keywords
» Artificial intelligence » Large language model » Multi task
