Summary Mass-Editing Memory in a Transformer arxiv.org
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One Line
The authors present MEMIT, a scalable method for updating language models with multiple memories, improving upon previous work focused on single associations.
Slides
Slide Presentation (13 slides)
Key Points
- MEMIT is a method for directly updating a language model with many memories, demonstrating its scalability to thousands of associations for GPT-J.
- SERAC is a system proposed in 2022 that allows for routing rewritten facts through different parameters while keeping the original weights unmodified.
- The study explores the challenge of achieving fluent text generation in a transformer model and proposes the method called MEMIT to address it.
- MEMIT involves replacing memory vectors and inserting residuals for each layer's update in the language model.
- MEMIT outperforms other methods in editing different categories of facts in large language models.
- The document includes a bibliography of references related to language models, knowledge representation, and natural language processing (NLP).
- The concept of causal tracing is introduced, which involves measuring the causal indirect effect of hidden states on factual associations in a transformer model.
- The experiments conducted show that diversity does not significantly impact MEMIT's performance in editing factual memories.
Summaries
22 word summary
The authors introduce MEMIT, a scalable method for updating language models with multiple memories, expanding on previous work limited to single associations.
43 word summary
Recent work has focused on updating large language models with new memories, but is limited to updating single associations. The authors propose MEMIT, a method for directly updating a language model with many memories, demonstrating its scalability to thousands of associations for GPT-J
506 word summary
Recent work has focused on updating large language models with new memories, but is limited to updating single associations. The authors propose MEMIT, a method for directly updating a language model with many memories, demonstrating its scalability to thousands of associations for GPT-J
SERAC is a system proposed in 2022 that allows for routing rewritten facts through different parameters while keeping the original weights unmodified. The method does not involve meta-learning but instead uses direct parameter updates based on an explicitly computed mapping. The focus is
The study explores the challenge of achieving fluent text generation in a transformer model. Previous research has examined this issue with a few edits, but the authors investigate whether it can be accomplished on a larger scale. They propose a method called MEMIT, which inserts
The summary is organized into separate paragraphs to distinguish distinct ideas and maintain the original order of the ideas presented.
The MEMIT update is described, outlining the steps of replacing memory vectors and inserting residuals for each layer's update. The process involves optimizing equations and
The paper discusses a method called MEMIT for mass-editing memory in a transformer model. It introduces keys and memories for inserting edits into the model. The algorithm for MEMIT is summarized. The experiments are conducted on two autoregressive LLMs
MEMIT is a method for editing factual memories in large language models. While its execution time is currently high, it has the potential to be optimized by batching independent optimizations. MEMIT outperforms other methods in editing different categories of facts, although it
This excerpt contains a list of references to various articles and papers related to the topic of memory editing in transformers. Some key points to note are:
1. The first reference is to a paper titled "Freebase: A shared database of structured general human
This document is a bibliography that includes various references to papers and resources related to language models and knowledge representation. The references cover topics such as detecting, updating, and visualizing model beliefs, common sense knowledge, language model capabilities, correlation matrix memories, temporal
This text excerpt includes a list of references to various papers and resources related to natural language processing (NLP) and language models. The references cover topics such as the impact of context on language models' predictions, the capabilities of language models as unsup
The document discusses the use of mass-editing memory in a transformer model. It references several papers on knowledge graphs and natural language processing. The concept of causal tracing is introduced, which involves measuring the causal indirect effect of hidden states on factual associations. The
During inference, the learning rate scale is set to 1.0. The MEND method is the fastest, taking 98.25 seconds for 10,000 updates on GPT-J. The default hyperparameters for ROME are provided in
We conducted experiments to compare the performance of MEMIT in four pairs of relations with varying levels of diversity. We found that the effectiveness of the edits closely followed the average of the individual splits, indicating that diversity did not significantly impact MEMIT's performance.