Summary of Qmos: Enhancing Llms For Telecommunication with Question Masked Loss and Option Shuffling, by Blessed Guda et al.
QMOS: Enhancing LLMs for Telecommunication with Question Masked loss and Option Shuffling
by Blessed Guda, Gabrial Zencha Ashungafac, Lawrence Francis, Carlee Joe-Wong
First submitted to arxiv on: 21 Sep 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 The paper proposes an innovative approach called QMOS that enhances the performance of Large Language Models (LLMs) in answering Multiple-Choice Questions in the telecommunications domain. Specifically, it uses a Question-Masked loss and Option Shuffling trick to improve the accuracy of LLMs like Phi-2 and Falcon-7B within a Retrieval Augmented Generation (RAG) framework. The approach involves several enhancements to the LLM-RAG pipeline, including finetuning, retrieval, prompt engineering, and inference. Compared to existing results, QMOS significantly outperforms baselines, achieving accuracy improvements of 24.70% to 49.30% with Falcon-7B and 42.07% to 84.65% with Phi-2. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us answer questions better in the telecommunications field by using special language models like GPT-3.5 or smaller models like Phi-2 and Falcon-7B. The model is made more accurate by adding some new tricks, called QMOS. This makes it easier to understand telecom-related topics. |
Keywords
» Artificial intelligence » Gpt » Inference » Prompt » Rag » Retrieval augmented generation
