Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
173 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Large Language Models Relearn Removed Concepts (2401.01814v1)

Published 3 Jan 2024 in cs.AI

Abstract: Advances in model editing through neuron pruning hold promise for removing undesirable concepts from LLMs. However, it remains unclear whether models have the capacity to reacquire pruned concepts after editing. To investigate this, we evaluate concept relearning in models by tracking concept saliency and similarity in pruned neurons during retraining. Our findings reveal that models can quickly regain performance post-pruning by relocating advanced concepts to earlier layers and reallocating pruned concepts to primed neurons with similar semantics. This demonstrates that models exhibit polysemantic capacities and can blend old and new concepts in individual neurons. While neuron pruning provides interpretability into model concepts, our results highlight the challenges of permanent concept removal for improved model \textit{safety}. Monitoring concept reemergence and developing techniques to mitigate relearning of unsafe concepts will be important directions for more robust model editing. Overall, our work strongly demonstrates the resilience and fluidity of concept representations in LLMs post concept removal.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (20)
  1. Omer Antverg and Yonatan Belinkov. 2022. On the pitfalls of analyzing individual neurons in language models. In International Conference on Learning Representations.
  2. Identifying and controlling important neurons in neural machine translation. In International Conference on Learning Representations.
  3. An iterative pruning algorithm for feedforward neural networks. IEEE Transactions on Neural Networks, 8(3):519–531.
  4. Discovering latent concepts learned in BERT. In International Conference on Learning Representations.
  5. Neurox: A toolkit for analyzing individual neurons in neural networks. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01):9851–9852.
  6. Analyzing redundancy in pretrained transformer models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4908–4926, Online. Association for Computational Linguistics.
  7. Analyzing individual neurons in pre-trained language models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4865–4880, Online. Association for Computational Linguistics.
  8. Optimal brain damage. In Advances in Neural Information Processing Systems, volume 2. Morgan-Kaufmann.
  9. The unreasonable effectiveness of random pruning: Return of the most naive baseline for sparse training. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022.
  10. Ilya Loshchilov and Frank Hutter. 2019. Decoupled weight decay regularization. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019.
  11. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States, pages 3111–3119.
  12. Studying the plasticity in deep convolutional neural networks using random pruning. Machine Vision and Applications, 30:203 – 216.
  13. Jesse Mu and Jacob Andreas. 2020. Compositional explanations of neurons. In Advances in Neural Information Processing Systems, volume 33, pages 17153–17163. Curran Associates, Inc.
  14. Hiroki Nakayama. 2018. seqeval: A python framework for sequence labeling evaluation. Software available from https://github.com/chakki-works/seqeval.
  15. Investigating language universal and specific properties in word embeddings. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1478–1488, Berlin, Germany. Association for Computational Linguistics.
  16. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9.
  17. Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. ArXiv, abs/1910.01108.
  18. Erik F. Tjong Kim Sang. 2002. Introduction to the CoNLL-2002 shared task: Language-independent named entity recognition. In COLING-02: The 6th Conference on Natural Language Learning 2002 (CoNLL-2002).
  19. Similarity analysis of contextual word representation models. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4638–4655, Online. Association for Computational Linguistics.
  20. What part of the neural network does this? understanding LSTMs by measuring and dissecting neurons. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5823–5830, Hong Kong, China. Association for Computational Linguistics.
Citations (7)
View on Semantic Scholar

Summary

  • The paper demonstrates that LLMs quickly recover lost capabilities post neuron pruning by relearning removed semantic concepts.
  • It employs fine-tuning on named entity recognition to track how pruned neurons are compensated by earlier, semantically-primed neurons.
  • Findings reveal the rise of polysemantic neurons, underscoring both opportunities for performance gains and challenges for AI safety.

Understanding Neuroplasticity in LLMs

Introduction

The concept of neuroplasticity, widely recognized in biological brains, has an equivalent in the world of artificial intelligence, particularly within LLMs. LLMs encode an extensive range of semantic concepts, facilitating their roles in a variety of natural language processing tasks. Striving to refine these models often involves the removal of specific neurons - believed to house these very semantic concepts - to adjust the model's outputs or imbue it with new capabilities. Nonetheless, the resilience of these models in regaining lost functions after such pruning is a subject worth exploring to better understand their adaptive prowess and the challenges in model editing for a safer AI.

Pruning and Performance Recovery

The paper examined models fine-tuned for named entity recognition to understand how they regain conceptual understanding after targeted pruning of neurons. The researchers found that upon removing important neurons associated with certain concepts, the models experienced a drastic performance decline. Remarkably, after retraining for only a few epochs, the models not only reacquired the lost capabilities but occasionally surpassed the original performance levels. This swift rebound underscores a notable characteristic of LLMs: their ability to adapt and redistribute pruned concepts, facilitating rapid recovery of performance.

Redistribution of Concepts

The paper's deeper analysis revealed intriguing patterns in the process of concept redistribution. When significant concept neurons were pruned, the associated concepts didn't just vanish. Instead, they were reallocated to neurons in earlier layers of the models, which initially handled similar concepts. It appears that these neurons, by virtue of their pre-existing semantic associations, were 'primed' for relearning the pruned concepts. As retraining proceeds, these primed neurons absorb the purged information, restoring the model's conceptual framework.

Advanced Insights: Polysemantic Neurons

One of the striking observations was the emergence of what the researchers term as 'polysemantic neurons.' These involve neurons capable of handling multiple concepts simultaneously, blending the new and the old. As the pruned concept gets integrated into earlier model layers, neurons begin encapsulating a mix of previously and newly acquired information, hence becoming polysemantic. This phenomenon adds another layer of complexity to the already intricate structure of LLMs, posing further implications for model editing and the interpretability of AI systems.

Implications and Further Research

The discovery of such neuroplastic behavior has profound implications for AI safety and model refinement. Given models' proclivity for relearning removed concepts, efforts to excise undesirable features from LLMs must anticipate the potential for these features to resurface. Consequently, continuous monitoring and perhaps an iterative model editing approach during retraining might be necessitated to ensure undesired concepts remain expunged. Future research might advance this field by investigating difference in neuroplasticity across various model architectures and sizes, as well as developing scalable methods to analyze vast numbers of neurons.

Conclusion

This investigation into neuroplasticity reaffirms the adaptability of LLMs; their ability to recover and regroup post-pruning poses both challenges and opportunities for AI development. As the field progresses, understanding and harnessing this resilience will be vital to enhancing the safety, fairness, and efficacy of these powerful systems. As the AI community continues to push the boundaries, strategies to mitigate relearning of unsafe concepts will become increasingly crucial in the quest for robust and reliable machine intelligence.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown