Summary of Teleoracle: Fine-tuned Retrieval-augmented Generation with Long-context Support For Network, by Nouf Alabbasi et al.
TeleOracle: Fine-Tuned Retrieval-Augmented Generation with Long-Context Support for Network
by Nouf Alabbasi, Omar Erak, Omar Alhussein, Ismail Lotfi, Sami Muhaidat, Merouane Debbah
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed TeleOracle system is a telecom-specialized retrieval-augmented generation (RAG) framework built on Phi-2 small language model (SLM). It incorporates semantic chunking and hybrid keyword and semantic search in its two-stage retriever. Additionally, it expands the context window during inference to enhance performance on open-ended queries and employs low-rank adaption for efficient fine-tuning. The system achieves a 30% improvement in accuracy over the base Phi-2 model, reaching an overall accuracy of 81.20%, outperforming larger language models while maintaining higher faithfulness scores. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TeleOracle is a new kind of computer program that helps make decisions about complex networks and technologies. It uses information from small language models to answer questions and give answers. The program has two parts: one that finds the right information, and another that uses that information to create answers. TeleOracle can also learn and adapt quickly, which makes it useful for dealing with new situations. The program is tested on a specific task called question and answer (QnA) and does very well, answering 81.20% of questions correctly. |
Keywords
* Artificial intelligence * Context window * Fine tuning * Inference * Language model * Rag * Retrieval augmented generation
