

Longrag
Overview :
LongRAG is a robust dual-perspective, retrieval-augmented generation system paradigm based on large language models (LLM), designed to enhance the understanding and retrieval capabilities of complex long-text knowledge. This model is particularly suited for Long-Context Question Answering (LCQA), as it effectively handles global information and factual details. Background information indicates that LongRAG improves performance on long-text question-answering tasks by integrating retrieval and generation techniques, especially in scenarios requiring multi-hop reasoning. The model is open-source and freely available, primarily targeting researchers and developers.
Target Users :
The primary target audience includes researchers and developers in the field of natural language processing, particularly those specializing in long-text question-answering tasks. LongRAG offers a powerful tool to assist them in constructing and optimizing their own question-answering systems, especially in scenarios requiring extensive text processing and complex reasoning.
Use Cases
Example 1: Using the LongRAG model for question-answering tasks on the HotpotQA dataset, showcasing its advantages in multi-hop questioning.
Example 2: Application of LongRAG on the 2WikiMultiHopQA dataset, addressing complex questions involving two Wikipedia pages.
Example 3: Demonstrating LongRAG's capabilities in long-text question answering in the music domain with the MusiQue dataset.
Features
? Dual-perspective understanding: LongRAG enhances comprehension of long texts from both global and detailed viewpoints.
? Retrieval enhancement: By leveraging retrieval techniques, it improves the model's performance on long-text question-answering tasks.
? Multi-hop reasoning: Suitable for complex question-answering tasks that require multi-step reasoning.
? Long-text handling: Specifically optimized to manage texts that exceed the model's typical processing length.
? Open-source and free: The model's code is openly available, allowing researchers and developers to use and modify it at no cost.
? Flexible configuration: Supports various parameter configurations to adapt to different question-answering tasks and datasets.
? Outstanding performance: Demonstrated superior performance across multiple long-text question-answering datasets.
How to Use
1. Install dependencies: Use pip to install the dependencies listed in requirements.txt.
2. Data preparation: Download and standardize the necessary training and evaluation datasets.
3. Build dataset: Run the gen_instruction.py and gen_index.py scripts to prepare data for SFT and retrieval.
4. Model training: Download LLaMA-Factory, place the constructed instruction data into its data directory, modify dataset_info.json, and run the sft.sh script to begin fine-tuning.
5. Model evaluation: Execute the main.py script in the src directory to perform inference and evaluation, using various parameter configurations to suit different models and tasks.
6. Result analysis: Evaluation results will be saved in the log directory, allowing for performance analysis of the model across different datasets.
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