Understanding Catastrophic Forgetting in Large Language Models

[2308.08747] An Empirical Study of Catastrophic Forgetting in Large ...

Abstract:Catastrophic forgetting (CF) is a phenomenon that occurs in machine learning when a model forgets previously learned information ...

Understanding Catastrophic Forgetting in Language Models via ...

This allows us to recover in-context learning abilities lost via instruction tuning, natural reasoning capability lost during code fine-tuning, ...

Understanding Catastrophic Forgetting in Language Models via...

A significant real-world problem with fine-tuned language models is the risk of the model forgetting how to perform tasks that it initially ...

Catastrophic Forgetting In LLMs - Cobus Greyling - Medium

Catastrophic forgetting (CF) refers to a phenomenon where a LLM tends to lose previously acquired knowledge as it learns new information.

Revisiting Catastrophic Forgetting in Large Language Model Tuning

Catastrophic Forgetting (CF) means models forgetting previously acquired knowledge when learning new data. It compromises the effectiveness ...

Understanding Catastrophic Forgetting in Language Models via ...

Section 2.6 reflects s = (X, y) 7→ (X, γy). 85. 4 Experiments on large language models. 86. Effect of instruction tuning on in ...

Understanding Catastrophic Forgetting in Large Language Models

It refers to the unsettling tendency of a model to forget previously learned information when training on new data or tasks.

Catastrophic Forgetting: The Essential Guide | Nightfall AI Security 101

In an era dominated by Large Language Models (LLMs) and expansive AI applications, understanding and mitigating this phenomenon is crucial. This guide ...

Catastrophic Forgetting: Understanding AI Memory Loss - ProjectPro

Catastrophic forgetting is a critical issue in fine-tuning large language models, where new learning can overwrite and erase previously acquired ...

Mitigating Catastrophic Forgetting in Large-Scale Models with ...

Understanding Catastrophic Forgetting ... Before diving into the solutions, it's essential to grasp the problem at hand. Catastrophic forgetting ...

Fine-Tuning LLMs: Overcoming Catastrophic Forgetting - Yurts AI

While applying LoRA for fine-tuning language models on two sequential datasets, I noticed a significant drop in the performance (aka ...

An Empirical Study of Catastrophic Forgetting in Large Language ...

196 Citations ; Interpretable Catastrophic Forgetting of Large Language Model Fine-tuning via Instruction Vector · Gangwei JiangCaigao Jiang +5 authors. Ying Wei.

Understanding Catastrophic Forgetting and Hallucinations in Large ...

Catastrophic forgetting in large language models refers to the issue where a model trained on a large dataset gradually loses its ability to ...

Understanding Catastrophic Forgetting in Language Models via ...

It is well known that fine-tuning can induce catastrophic forgetting, or unnecessary lowered performance for some pretraining tasks. But is the model ...

Understanding Catastrophic Forgetting in Large Language Models

Catastrophic forgetting is a significant challenge in the field of large language models, where the model's performance on previously learned tasks deteriorates ...

Revisiting Catastrophic Forgetting in Large Language Model Tuning

Catastrophic Forgetting (CF) means models forgetting previously acquired knowledge when learning new data. It compromises the effectiveness of large ...

Understanding What We Lose - Towards Data Science

η is the learning rate. However, the choice of this learning rate can be tricky and holds implications for catastrophic forgetting. If η is high, the model is ...

An Empirical Study of Catastrophic Forgetting in Large Language ...

Overall, this research provides valuable insights into how large language models learn and retain information over time. Understanding ...

Mitigating Catastrophic Forgetting in Large Language Models with ...

We propose a framework called Self-Synthesized Rehearsal (SSR) that uses the LLM to generate synthetic instances for rehearsal.

How does the architecture of large language models contribute to ...

Catastrophic forgetting is a phenomenon where a model loses its previously learned knowledge when it is trained on new data. Large language ...