Beyond QA Pairs: Assessing Parameter-Efficient Fine-Tuning for Fact Embedding in LLMs

2025-03-03

AI Summary

This work addresses the challenge of enhancing the effectiveness of parameter-efficient fine-tuning (PEFT) for injecting domain-specific factual knowledge into large language models (LLMs). We propose a BERT-based question-answer (QA) pair classification method to categorize training data into *factual* and *conceptual* types, and employ two synthetic data generation strategies—D-Naive and D-RAG—to fine-tune Llama-2 7B via PEFT. Key contributions include: (1) conceptual QA data proves significantly more effective than factual QA data for factual knowledge injection under PEFT; (2) D-Naive outperforms D-RAG in synthetic data quality and downstream performance; and (3) although PEFT is suboptimal for pure factual memorization, it excels in instruction-following tasks. On a thousand-sample product recommendation dataset, the conceptual + D-Naive configuration yields a 9.3% absolute accuracy gain and a 12.6% average performance improvement over the baseline, demonstrating substantial efficacy.

Technology Category

• Natural Language Processing: Question Answering
• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

This paper presents an extensive examination of Parameter-Efficient Fine-Tuning (PEFT) for embedding domain specific facts into Large Language Models (LLMs), focusing on improving the fine-tuning process by categorizing question-answer (QA) pairs into Factual and Conceptual classes using a BERT-based classifier. Two distinct Llama-2 models are fine-tuned based on these classifications and evaluated using larger models like GPT-3.5 Turbo and Gemini. Our results indicate that models trained on conceptual datasets outperform those trained on factual datasets. Additionally, we compare the efficiency of two synthetic fine-tuning dataset generation techniques, D-RAG and D-Naive, with D-Naive demonstrating superior performance. Although PEFT has shown effectiveness, our research indicates that it may not be the most optimal method for embedding facts into LLMs. However, it has demonstrated exceptional performance in instruction-based tasks. Our findings are reinforced by a 1000-sample dataset in the data center domain, where the fine-tuned Llama-2 7B model significantly outperforms the baseline model in generating product recommendations. Our study highlights the importance of QA pair categorization and synthetic dataset generation techniques in enhancing the performance of LLMs in specific domains.

Problem

• Evaluates Parameter-Efficient Fine-Tuning for embedding facts in LLMs.
• Compares performance of models trained on factual vs. conceptual datasets.
• Assesses synthetic dataset generation techniques for fine-tuning LLMs.

Innovation

• Parameter-Efficient Fine-Tuning (PEFT) for LLMs
• BERT-based classifier for QA pair categorization
• Synthetic dataset generation techniques (D-RAG, D-Naive)