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Found 8 papers, 2 papers with code
Is Bigger Edit Batch Size Always Better? -- An Empirical Study on Model Editing with Llama-3
This study presents a targeted model editing analysis focused on the latest large language model, Llama-3.
A Unified Framework for Model Editing
We introduce a unifying framework that brings two leading "locate-and-edit" model editing techniques -- ROME and MEMIT -- under a single conceptual umbrella, optimizing for the same goal, which we call the preservation-memorization objective.
Rebuilding ROME : Resolving Model Collapse during Sequential Model Editing
With this paper, we provide a more stable implementation ROME, which we call r-ROME and show that model collapse is no longer observed when making large scale sequential edits with r-ROME, while further improving generalization and locality of model editing compared to the original implementation of ROME.
Identifying Multiple Personalities in Large Language Models with External Evaluation
In this paper, we investigate LLM personalities using an alternate personality measurement method, which we refer to as the external evaluation method, where instead of prompting LLMs with multiple-choice questions in the Likert scale, we evaluate LLMs' personalities by analyzing their responses toward open-ended situational questions using an external machine learning model.
Towards Hierarchical Spoken Language Dysfluency Modeling
Speech disfluency modeling is the bottleneck for both speech therapy and language learning.
Model Editing at Scale leads to Gradual and Catastrophic Forgetting
With this in mind, we evaluate the current model editing methods at scale, focusing on two state of the art methods: ROME and MEMIT.
Towards an Interpretable Representation of Speaker Identity via Perceptual Voice Qualities
Unlike other data modalities such as text and vision, speech does not lend itself to easy interpretation.
Self-Assessment Tests are Unreliable Measures of LLM Personality
These simple tests, done on ChatGPT and three Llama2 models of different sizes, show that self-assessment personality tests created for humans are unreliable measures of personality in LLMs.
