Understanding the Interplay between Memorization and Learning in Large Language Models
summary
The gist
The gist: Memorization measures disagree on memorization order, and optimal learning of a language cannot avoid partial memorization of training strings, while improved learning decreases contextual
In short
The study re-examines three memorization measures—recollection-based, counterfactual, and contextual—to understand how LLMs learn. It finds that optimal language learning requires some memorization, but improved learning reduces contextual and counterfactual memorization while increasing recollection-based memorization. This trade-off depends heavily on which measure is used.
Key concepts
- Recollection-based Memorization
- This measure detects when an LLM starts remembering a specific training string by looking at the cross-entropy loss of generating that string. It uses a simple threshold to mark the exact epoch where memorization begins, though this method is noted as being error-prone due to arbitrary threshold choices.
- Contextual Memorization
- This concept distinguishes true memorization from learning by defining it based on how well a model can recall a string's context without explicit training on it. It uses the 'optimal contextual recollection' as a stricter benchmark, starting memorization only when the training loss exceeds this ideal contextual threshold.
- Counterfactual Memorization
- Inspired by differential privacy, this measure captures how much an LLM's performance on a string changes when that string is included or excluded from the training data. It reflects divergence between models trained with and without the specific string, providing a view of memorization dynamics over training epochs.
Terminology used across episodes
This episode discusses
- Understanding the Interplay between Memorization and Learning in Large Language Models · Paper Radio
- Sparks of Artificial General Intelligence: Early experiments with GPT-4
- Rethinking LLM Memorization through the Lens of Adversarial Compression
- Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy
- Counterfactual Memorization in Neural Language Models
- Deduplicating Training Data Makes Language Models Better
- Quantifying Memorization Across Neural Language Models
- What Languages are Easy to Language-Model? A Perspective from Learning Probabilistic Regular Languages
- In-Context Language Learning: Architectures and Algorithms
- Transparency at the Source: Evaluating and Interpreting Language Models With Access to the True Distribution
- Injecting structural hints: Using language models to study inductive biases in language learning
- Examining the Inductive Bias of Neural Language Models with Artificial Languages
- Transformer Working Memory Enables Regular Language Reasoning and Natural Language Length Extrapolation
- Characterizing Intrinsic Compositionality in Transformers with Tree Projections
- Transformers Learn Shortcuts to Automata
- On the Ability and Limitations of Transformers to Recognize Formal Languages
- What Formal Languages Can Transformers Express? A Survey
- Neural Networks and the Chomsky Hierarchy
- Why are Sensitive Functions Hard for Transformers?
- Are All Languages Equally Hard to Language-Model?
- What Kind of Language Is Hard to Language-Model?
The paper
Understanding the Interplay between Memorization and Learning in Large Language Models · Read on arXiv
Bishwamittra Ghosh, Soumi Das, Qinyuan Wu, Mohammad Aflah Khan, Krishna P. Gummadi, Evimaria Terzi, Deepak Garg
Max Planck Institute for Software Systems, Germany
Transcript
Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.
Tom: I'm Tom, and with me are Jane, Lu, senior AI researcher at Tsinghua, Meng, lead engineer at a mysterious AI startup and Lalam, the in-house Large Language Model.
Jane: Today's paper: "Understanding the Interplay between Memorization and Learning in Large Language Models".
Tom: The gist: Memorization measures disagree on memorization order, and optimal learning of a language cannot avoid partial memorization of training strings,
Jane: First, who's behind it and why it matters.
Paper summary: Tom: So, to recap, this paper tackles whether optimal language learning can happen without memorizing any strings. The authors contend that this is infeasible because every string has its own training epoch where it starts to be memorized, and those epochs vary a lot across different strings.
Jane: They found that you need some level of nonzero memorization for truly optimal learning, whether you're dealing with high entropy or low entropy languages. They also showed that improving learning by adding more training data actually decreases contextual and counterfactual memorization scores while increasing recollection-based ones.
Lu: That trade-off between learning and memorization is really interesting because it depends entirely on which measure you use to look at it, which is a point they highlight in their study of the interplay between memorization and learning in large language models.
Meng: So, if we use the right measure, like contextual or counterfactual ones, we might get a better picture of what's actually happening during training rather than just looking at how well the model can recall something after training.
Lalam: It seems like this paper suggests that existing privacy-focused measures might be overstating the risk posed by memorization, especially when you consider how easily models can use contextual reasoning to reconstruct things.
Conclusion: Tom: So, to wrap up this talk on "Understanding the Interplay between Memorization and Learning in Large Language Models," the authors are suggesting that we need to be really careful about which memorization measure we apply when we're trying to judge model performance or privacy risks.
Jane: Exactly. The paper argues that contextual and counterfactual measures are stricter criteria than recollection-based ones, meaning they offer a better alignment with what you want to know about the learning process itself.
Lu: The main implication is that relying on simple recollection-based checks might just be exaggerating the memorization risks posed by large language models because those strings are often predictable through contextual reasoning.
Meng: For practical use, this means we should probably prefer looking at contextual or counterfactual scores over just checking if a model can recall a specific training string.
Lalam: Ultimately, the study concludes that learning an entire language without some degree of memorization isn't possible in how these models are trained right now, and we need to use those more nuanced measures before applying them to real-world privacy discussions.
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