Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformers (2406.18400v2)

Abstract: LLMs have the capacity to store and recall facts. Through experimentation with open-source models, we observe that this ability to retrieve facts can be easily manipulated by changing contexts, even without altering their factual meanings. These findings highlight that LLMs might behave like an associative memory model where certain tokens in the contexts serve as clues to retrieving facts. We mathematically explore this property by studying how transformers, the building blocks of LLMs, can complete such memory tasks. We study a simple latent concept association problem with a one-layer transformer and we show theoretically and empirically that the transformer gathers information using self-attention and uses the value matrix for associative memory.

Latent Concept Association and Associative Memory in Transformers

The paper "Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformers" presented by Jiang et al. explores the intricate memory capabilities of LLMs, specifically focusing on their propensity to function via associative memory mechanisms. This paper systematically investigates the robustness of fact retrieval—a task LLMs are frequently tasked with—revealing its susceptibility to context manipulation known as "context hijacking." Through empirical evidence grounded in synthetic experiments, the paper elucidates the underlying mechanics of transformers, the fundamental architecture of LLMs, as associative memory models capable of context-sensitive data retrieval.

Context Hijacking Phenomenon

The authors present a critical evaluation of context hijacking, demonstrating that the fact retrieval capabilities of LLMs such as GPT-2, LLaMA-2, and Gemma can be compromised using targeted context alterations. Hijacking involves supplementing the input with additional context that redirects the model output away from correct factual recall toward erroneous retrievals. Rigorous benchmarking utilizing the CounterFact dataset indicates that insertion of misleading yet factually benign information can shift the model's attention and responses significantly. The efficacy scores for manipulated contexts corroborate this vulnerability: with increased repetition of contextually misleading tokens, LLMs more frequently produce incorrect outputs, confirming that the models operate within an associative memory framework, heavily reliant on token cues from input contexts.

Theoretical Insights into Latent Concept Association

To comprehensively analyze the association and retrieval functionalities of transformers, the paper introduces the latent concept association task. This synthetic task models the relationship between context tokens and output tokens through latent semantic concepts, quantitatively measured via Hamming distance in a conceptual latent space. The authors meticulously detail the architecture and performance of a simplified one-layer transformer tasked with this retrieval process.

Key theoretical insights include the construction of the value matrix as a remapping matrix facilitating associative memory. The findings suggest that transformers employ the self-attention mechanism to collate context-derived information, while the value matrix serves as a vital component for output retrieval by connecting latent representations to linguistic constructs. Moreover, the research underscores the significance of training embeddings under this paradigm—particularly in under-parameterized regimes—where the inner product relationships between embeddings align closely with latent conceptual distances, hinting at an emergent low-rank structure in the resulting representations.

Attention Mechanism's Role in Contextual Information Processing

Elaborating on the attention mechanism, the paper highlights its selective property in filtering contextually relevant tokens, steering memory retrieval towards meaningful associations. This is explored under constraints where latent variables segregate tokens into conceptual clusters, showcasing that attention weights adapt to prioritize intra-cluster (semantically similar) tokens over inter-cluster tokens, thereby evidentially mitigating noise and aiding memory recall.

Implications and Future Perspectives

This research explores the intricacies of memory recall in LLMs, presenting both theoretical models and empirical evidence that bridge transformer operations with cognitive principles of associative memory. The demonstration of context hijacking serves as both a critique and a catalyst for better understanding and improving the robustness of LLMs in real-world factual tasks.

In terms of future developments, this work opens avenues for enhancing the interpretability and reliability of LLMs. Given the demonstrated sensitivity of transformers to context, optimizing embeddings, value matrices, and attention mechanisms for more robust fact retrieval is a vital next step. Moreover, these insights could guide the development of editing and fine-tuning methods that bolster the robustness and accuracy of LLMs in sensitive applications, potentially informing frameworks that mandate improved storage and retrieval strategies at the architectural level.