Summary PMET Precise Model Editing in a Transformer arxiv.org
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One Line
PMET is a method that enhances LLMs by optimizing hidden states of MHSA and FFN components in Transformers, introducing a subject-centric model.
Slides
Slide Presentation (9 slides)
Key Points
- PMET is a model editing technique for Large Language Models (LLMs) that aims to modify a minor proportion of knowledge in LLMs at a relatively low cost.
- PMET optimizes the TC hidden states of both MHSA and FFN in Transformers to enable model editing.
- The paper introduces the concept of edited knowledge associated with the subject and proposes a subject-centric approach to model editing.
- The PMET model introduces optimized parameters to the hidden states of the model at each layer, utilizing a square root spread for conveying precise information.
- PMET outperforms other methods in terms of reliability, specificity, fluency, and consistency in model editing.
Summaries
21 word summary
PMET is a technique for modifying LLMs by optimizing hidden states of MHSA and FFN components in Transformers, introducing subject-centric model.
39 word summary
PMET (Precise Model Editing in a Transformer) is a technique for modifying Large Language Models (LLMs) that focuses on optimizing the hidden states of the MHSA and FFN components in Transformers. The paper introduces the concept of subject-centric model
406 word summary
PMET (Precise Model Editing in a Transformer) is a model editing technique for Large Language Models (LLMs) that aims to modify a minor proportion of knowledge in LLMs at a relatively low cost. Existing methods optimize the Transformer Layer (
The authors of the study observed that the MHSA component of Transformers undergoes more frequent changes compared to the FFN component. They propose a method called PMET that optimizes the TC hidden states of both MHSA and FFN, using the optimized
The paper discusses the problem of model editing and proposes a subject-centric approach to enable edited models to reason based on the subject. The authors redefine the model editing problem and introduce the concept of edited knowledge associated with the subject. They analyze the role of the
The excerpt discusses the changes in hidden states before and after applying MHSA and FFN in a transformer model. It describes the encoding of subject-related knowledge and the desire to retain or modify certain pieces of knowledge. The incremental weight is calculated based on the
The PMET model introduces optimized parameters to the hidden states of the model at each layer. It utilizes a square root spread to convey more precise information to critical layers. The experiments conducted on GPT-J (6B) and GPT-NeoX
The study explores PMET, a method for precise model editing in a Transformer. PMET is compared to other methods, including MEMIT and MEND, in terms of reliability, specificity, fluency, and consistency. PMET outperforms MEM
The paper discusses PMET (Precise Model Editing in a Transformer) and its impact on model retention, efficacy, generalization, consistency, specificity, and fluency. The authors conducted an ablation study to evaluate the performance of PMET in optimizing
This text excerpt includes a list of references to studies and papers related to knowledge editing in language models. The references cover topics such as inspecting and editing knowledge representations, hallucination in natural language generation, language models as knowledge bases, contextualization in masked
Reliability and specificity are important metrics for evaluating the success of model editing. However, assessing success on all knowledge related or unrelated to the target knowledge is challenging. Previous works have divided reliability into efficacy and generalization, evaluating success on edit sequences and paraph
The study titled "Can we edit factual knowledge by in-context learning?" by Ce Zheng et al. (2023) presents a method called PMET (Precise Model Editing in a Transformer) that aims to edit factual knowledge in language models. The