Specializing Large Language Models for Telecom Networks by ITU AI/ML in 5G Challenge

In the "AI for Good" competition, we were required to specialize large language models (LLMs) like Falcon 7.5B and Phi-2 on telecom knowledge. Using the TeleQnA dataset, participants aimed to enhance the models' accuracy on multiple-choice questions related to various telecom domains. This competition was organized by the International Telecommunication Union (ITU), Huawei Technologies, and the Technology Innovation Institute (TII).

A Key challenge included addressing the complexity and diversity of the telecom-specific questions. Participants employed techniques such as Retrieval Augmented Generation (RAG), prompt engineering, and fine-tuning to achieve these goals. This competition provided a platform for advancing the application of LLMs in the telecommunications industry.

Folder set up

.
├── data
│ ├── questions_new.txt
│ ├── TeleQnA.json
│ └── TeleQnA.txt
├── models
├── results
│ ├── context_all_train.pkl
│ └── context_all_train.csv
├── .viminfo
├── README.md
├── fine_tuning.py
├── main_temp.py
├── requirements.txt
├── utils.py
└── vector_store_for_rag.py

How to run code

1. Clone this repo

   git clone https://github.com/Nouf-Alabbasi/oKUmura_AI_Telecom_challenge.git
   cd oKUmura_AI_Telecom_challenge
   

2. Install packages from requirements

   pip install -r requirements.txt
   

3. Download data
   • Download competition data and copy it to data/ directory inside your cloned repository
   • Extract rel18 folder from rel18.rar
4. clone the longLM repo from https://github.com/datamllab/LongLM into the cloned repository

   git clone https://github.com/datamllab/LongLM.git
   

5. Run vectore_store_for_rag.py to obtain and store the vectorized documents

6. Run fine_tuning.py to finetune the model on the teleQnA training with retrieved-context
7. Run main.py to run inference on the test set, but make the following changes first
   • set 'model_path' to the path of your finetuned phi-2 model
   • when running main.py for the first time, set create_BM26_nodes to True

Explanations of features used

• rerank

  • After retrieving a large list of context chunks, the chunks are ranked based on the relevancy score from a cross-encoder. A shortlist is derived the reranked list of context chunks. We used ms-marco-MiniLM-L-6-v2, this model demonstrates relatively high speed and decent accuracy.

• self extend

  • Self-extend is a technique that uses two-level attention to allow the LLMs to handle longer input, alleviating the need for finetuning. Given the long input given to the LLM, self-extend has been a promising tool that improved performance and also opened up the door for techniques like semantic chunking, where the chunks are naturally long, to be effectively used.

• hybrid retriever

  • We used a hybrid retriever combining BM25 and a dense vector retriever to leverage the strengths of both methods: BM25 for precise keyword-based matching and the dense vector retriever for capturing semantic similarities, ensuring comprehensive and accurate retrieval of relevant information.

• embedding model

  • BAAI/bge-small-en-v1.5 is a lightweight and efficient model designed for high-speed text embedding, chosen for its ability to generate dense vector representations of text that enhance the performance of our retrieval and ranking tasks in the fine-tuned model.

• Fine-tuned Small language model

  • small language model: Phi-2
    • Phi-2 is a Transformer with 2.7 billion parameters developed by Microsoft, designed for natural language understanding and generation tasks.
    • limitations:
      • Unreliable Responses to Instruction: The model lacks instruction fine-tuning, making it less effective at following complex or detailed user instructions.
      • Language Limitations: Primarily designed for standard English, the model may struggle with informal language, slang, or other languages, leading to misunderstandings or errors.
  • Fine-tuning:
    • LoRA (Low-Rank Adaptation) was used because it significantly reduced computational and memory requirements while maintaining or improving model performance by adding a small number of trainable parameters, making the process efficient and cost-effective.
    • The model was finetuned on a question-answer set. The question was presented within the same prompt used in the inference stage, which included instructions, the question, and the context. The question set included telecom questions to mitigate the model's language limitation.

Hardware needed (e.g. Google Colab or the specifications of your local machine)

• hardware: 1 L4 GPU
• environment:
  • python version: 3.10.10
  • Required Python packages (listed in requirements.txt). the list includes, among others, the following packages:
    • transformers
    • torch (PyTorch)
    • llama-index

Expected run time for each notebook.

• expected time to create vector store
  • ~ 4 hrs for chunking and vector db
  • ~ 5 hrs for the context retrieval
• expected time for fine-tuning
  • ~ 2 hrs
• expected time for inference
  • ~40 minutes for test set 1
  • ~ 2 hrs for full test set + the time it takes to create nodes

Data

• TeleQnA_training.txt

  • 1 MB
  • This file contains the training data. It contains around 1000 questions. The model was fine-tuned on the whole teleQnA dataset provided in this repository
  • These are the fields available for each question:
    • Question: This field consists of a string that presents the question associated with a specific concept within the telecommunications domain.
    • Options: This field comprises a set of strings representing the various answer options.
    • Answer: This field contains a string that adheres to the format ’option ID: Answer’ and presents the correct response to the question. A single option is correct; however, options may include choices like “All of the Above” or “Both options 1 and 2”.
    • Explanation: This field encompasses a string that clarifies the reasoning behind the correct answer.
    • Category: This field includes a label identifying the source category (e.g., lexicon, research overview, etc.).
  • This is an example of a question from the training dataset

    "question 4": {
       "question": "How does a supporting UE attach to the same core network operator from which it detached in a shared network? [3GPP Release 17]",
       "option 1": "It requests the core network node to remember its previous selection.",
       "option 2": "It uses information stored in the UE when it was detached.",
       "option 3": "It relies on the SIM/USIM card for information.",
       "option 4": "It performs a fresh attach procedure.",
       "answer": "option 2: It uses information stored in the UE when it was detached.",
       "explanation": "A supporting UE in a shared network attaches to the same core network operator it detached from by using information stored in the UE when it was detached.",
       "category": "Standards specifications"
    },
    

• Q_A_ID_training.csv

  • 19.8 KB
  • This file contains the target for the training.txt file.

• TeleQnA_testing1.txt

  • 177.5 KB
  • This is the file you will apply your model to. This file contains 366 questions. The format is similar to the questions in the training dataset, the only difference is that the answer and explanation fields are not included for each question.

• questions_new.txt

  • 867.7 KB
  • additional testing data. This file contains 2000 extra test questions.

• rel18.rar

  • 824.4 MB
  • This is the corpus of technical documents.