Summary of Scaling Laws For Discriminative Classification in Large Language Models, by Dean Wyatte et al.
Scaling Laws for Discriminative Classification in Large Language Models
by Dean Wyatte, Fatemeh Tahmasbi, Ming Li, Thomas Markovich
First submitted to arxiv on: 24 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 The paper introduces a system that utilizes large language models (LLMs) to augment customer support advocates. LLMs can generate sensible answers but are prone to hallucination, making them less suitable for immediate use in customer support. The proposed system reframes the language modeling task as a discriminative classification task, presenting top-K template responses for customer support advocates to use when responding to customers. Offline and online experiments demonstrate gains and statistically significant lifts for the system. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about using powerful language models to help customer service representatives respond better to customers. The problem with these models is that they can sometimes make things up, which isn’t helpful in a customer support setting. To fix this, the authors came up with a way to use the language models as a tool for customer support agents, providing them with suggested responses to common questions. |
Keywords
» Artificial intelligence » Classification » Hallucination
