Privacy Risks in Language Models: The Myth of Verbatim Memorization

In recent years, Large Language Models (LLMs) have gained significant attention in the field of Natural Language Processing (NLP) due to their exceptional accuracy in various NLP tasks. These models are trained on vast amounts of data and can learn the structure and syntax of programming languages, making them highly adept at tasks like code generation, summarization, and completion. However, a new study has revealed that these LLMs are not as accurate as previously thought due to a phenomenon called "memorization."
Memorization occurs when a model becomes too specialized in the training data, causing it to recall specific details from the data instead of generalizing to new information. This means that LLMs can accurately recall specific phrases or sentences from the training data but struggle with unseen or new data. The study found that CodeX, a popular LLM for code generation, can complete HackerRank problems without receiving the full task description due to memorization.
The authors of the study investigate the transferability of prompts to different models trained on different corpora and find that GPT-2, trained on the WebText corpus, is not as effective at generalizing to new data as CodeX. They also explore the Pythia suite of models, which are trained on the Pile corpus, and find similar results.
The study highlights the importance of understanding memorization in LLMs for code generation. Memorization can lead to overestimation of performance, causing models to appear more accurate than they actually are. This has significant implications for software engineering, as it can lead to faulty or buggy code being generated.
In conclusion, the study demonstrates that LLMs are not as accurate as previously thought due to memorization. The authors emphasize the need to develop models that can generalize well to new data and avoid overfitting to training data. By understanding the limitations of LLMs, software engineers can use these models more effectively and generate high-quality code with fewer errors.