Summary of Paraphrase and Aggregate with Large Language Models For Minimizing Intent Classification Errors, by Vikas Yadav and Zheng Tang and Vijay Srinivasan
Paraphrase and Aggregate with Large Language Models for Minimizing Intent Classification Errors
by Vikas Yadav, Zheng Tang, Vijay Srinivasan
First submitted to arxiv on: 24 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 Large language models (LLMs) have excelled in natural language generation tasks but received less attention for decision-making tasks like classification. Our study demonstrates that LLMs, such as LLaMa, can achieve high performance on large multi-class classification tasks but still commit classification errors and generate out-of-vocabulary class labels. To address these critical issues, we introduce the Paraphrase and AGgregate (PAG)-LLM approach, which generates multiple paraphrases of an input query, performs multi-class classification for each paraphrase, and aggregates the results based on confidence scores. We evaluate PAG-LLM on two large multi-class datasets: CLINC and Banking, achieving 22.7% and 15.1% error reduction. Our approach is particularly effective for hard examples where LLMs are uncertain, reducing misclassification and hallucinated label generation errors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well language models can help make decisions when given a lot of information. Language models are really good at generating text but not as good at making choices between many options. The researchers found that these language models often make mistakes or create new classes that aren’t valid. To fix this, they developed a new approach called Paraphrase and AGgregate (PAG)-LLM. This method generates multiple versions of the information, makes decisions for each version, and then combines the results based on how confident it is in each answer. The researchers tested PAG-LLM with two large sets of data and found that it reduced mistakes by 22.7% and 15.1%. This approach works especially well when the model is unsure, helping to avoid bad decisions. |
Keywords
» Artificial intelligence » Attention » Classification » Llama
