QMOS: Enhancing LLMs for Telecommunication with Question Masked loss and Option Shuffling

• URL: http://arxiv.org/abs/2409.14175v1
• Date: Sat, 21 Sep 2024 15:32:10 GMT
• Title: QMOS: Enhancing LLMs for Telecommunication with Question Masked loss and Option Shuffling
• Authors: Blessed Guda, Gabrial Zencha A., Lawrence Francis, Carlee Joe-Wong,
• Abstract summary: GPT-3.5 has been used in recent work, to obtain noteworthy accuracy for telecom-related questions in a Retrieval Augmented Generation framework. This paper introduces QMOS, an innovative approach which uses a Question-Masked loss and Option Shuffling trick to enhance the performance of LLMs in answering Multiple-Choice Questions in the telecommunications domain.
• Score: 10.42541749928513
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language models (LLMs) have brought about substantial advancements in the field of Question Answering (QA) systems. These models do remarkably well in addressing intricate inquiries in a variety of disciplines. However, because of domain-specific vocabulary, complex technological concepts, and the requirement for exact responses applying LLMs to specialized sectors like telecommunications presents additional obstacles. GPT-3.5 has been used in recent work, to obtain noteworthy accuracy for telecom-related questions in a Retrieval Augmented Generation (RAG) framework. Notwithstanding these developments, the practical use of models such as GPT-3.5 is restricted by their proprietary nature and high computing demands. This paper introduces QMOS, an innovative approach which uses a Question-Masked loss and Option Shuffling trick to enhance the performance of LLMs in answering Multiple-Choice Questions in the telecommunications domain. Our focus was on using opensource, smaller language models (Phi-2 and Falcon-7B) within an enhanced RAG framework. Our multi-faceted approach involves several enhancements to the whole LLM-RAG pipeline of finetuning, retrieval, prompt engineering and inference. Our approaches significantly outperform existing results, achieving accuracy improvements from baselines of 24.70% to 49.30% with Falcon-7B and from 42.07% to 84.65% with Phi-2.

Related papers

• TeleOracle: Fine-Tuned Retrieval-Augmented Generation with Long-Context Support for Network [4.551436852242372]
  We present TeleOracle, a telecom-specialized retrieval-augmented generation (RAG) system built on the Phi-2 small language model (SLM) To improve context retrieval, TeleOracle employs a two-stage retriever that incorporates semantic chunking and hybrid keyword and semantic search. A thorough analysis of the model's performance indicates that our RAG framework is effective in aligning Phi-2 to the telecom domain in a downstream question and answer (QnA) task, achieving a 30% improvement in accuracy over the base Phi-2 model.
  arXiv  Detail & Related papers  (2024-11-04T21:12:08Z)
• Exploring Language Model Generalization in Low-Resource Extractive QA [57.14068405860034]
  We investigate Extractive Question Answering (EQA) with Large Language Models (LLMs) under domain drift. We devise a series of experiments to empirically explain the performance gap.
  arXiv  Detail & Related papers  (2024-09-27T05:06:43Z)
• Rephrase and Contrast: Fine-Tuning Language Models for Enhanced Understanding of Communication and Computer Networks [13.829525575305206]
  This paper introduces our Rephrase and Contrast (RaC) framework, an efficient fine-tuning framework. RaC enhances LLMs' comprehension and critical thinking abilities by incorporating question reformulation and contrastive analysis. To efficiently construct the dataset for RaC fine-tuning, we develop a GPT-assisted data mining method for generating high-quality question-answer pairs.
  arXiv  Detail & Related papers  (2024-09-21T16:04:43Z)
• MindStar: Enhancing Math Reasoning in Pre-trained LLMs at Inference Time [51.5039731721706]
  MindStar is a purely inference-based searching method for large language models. It formulates reasoning tasks as searching problems and proposes two search ideas to identify the optimal reasoning paths. It significantly enhances the reasoning abilities of open-source models, such as Llama-2-13B and Mistral-7B, and achieves comparable performance to GPT-3.5 and Grok-1.
  arXiv  Detail & Related papers  (2024-05-25T15:07:33Z)
• Personalized Wireless Federated Learning for Large Language Models [75.22457544349668]
  Large Language Models (LLMs) have revolutionized natural language processing tasks. Their deployment in wireless networks still face challenges, i.e., a lack of privacy and security protection mechanisms. We introduce two personalized wireless federated fine-tuning methods with low communication overhead.
  arXiv  Detail & Related papers  (2024-04-20T02:30:21Z)
• Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity [59.57065228857247]
  Retrieval-augmented Large Language Models (LLMs) have emerged as a promising approach to enhancing response accuracy in several tasks, such as Question-Answering (QA) We propose a novel adaptive QA framework, that can dynamically select the most suitable strategy for (retrieval-augmented) LLMs based on the query complexity. We validate our model on a set of open-domain QA datasets, covering multiple query complexities, and show that ours enhances the overall efficiency and accuracy of QA systems.
  arXiv  Detail & Related papers  (2024-03-21T13:52:30Z)
• Enhancing Textbook Question Answering Task with Large Language Models and Retrieval Augmented Generation [3.948068081583197]
  This paper proposes a methodology that handle the out-of-domain scenario in Textbook question answering (TQA) Through supervised fine-tuning of the LLM model Llama-2 and the incorporation of RAG, our architecture outperforms the baseline, achieving a 4.12% accuracy improvement on validation set and 9.84% on test set for non-diagram multiple-choice questions.
  arXiv  Detail & Related papers  (2024-02-05T11:58:56Z)
• Large Language Model Enhanced Multi-Agent Systems for 6G Communications [94.45712802626794]
  We propose a multi-agent system with customized communication knowledge and tools for solving communication related tasks using natural language. We validate the effectiveness of the proposed multi-agent system by designing a semantic communication system.
  arXiv  Detail & Related papers  (2023-12-13T02:35:57Z)
• Self-prompted Chain-of-Thought on Large Language Models for Open-domain Multi-hop Reasoning [70.74928578278957]
  In open-domain question-answering (ODQA), most existing questions require single-hop reasoning on commonsense. Large language models (LLMs) have found significant utility in facilitating ODQA without external corpus. We propose Self-prompted Chain-of-Thought (SP-CoT), an automated framework to mass-produce high quality CoTs.
  arXiv  Detail & Related papers  (2023-10-20T14:51:10Z)
• Analysis of the Reasoning with Redundant Information Provided Ability of Large Language Models [0.0]
  Large Language Models (LLMs) have demonstrated impressive capabilities across a range of natural language processing tasks. To address this gap, a new form of Question-Answering (QA) task, termed Reasoning with Redundant Information Provided (RRIP), is introduced. This study evaluates two popular LLMs, LlaMA2-13B-chat and generative pre-trained transformer 3.5 (GPT-3.5), contrasting their performance on traditional QA tasks against RRIP tasks.
  arXiv  Detail & Related papers  (2023-10-06T06:20:06Z)
• Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities [32.035405009895264]
  Large language models (LLMs) are revolutionizing AI development and potentially shaping our future. The status quo cloud-based deployment faces some critical challenges: 1) long response time; 2) high bandwidth costs; and 3) the violation of data privacy. 6G mobile edge computing (MEC) systems may resolve these pressing issues. This article serves as a position paper for thoroughly identifying the motivation, challenges, and pathway for empowering LLMs at the 6G edge.
  arXiv  Detail & Related papers  (2023-09-28T06:22:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.