649 citations · 780 across the 20 of their papers we have counts for
5 papers · 1 filter
Muse Glimmer 30B: An On-Device Agentic Model
Arash Ebrahim Abtahizadeh, Muna Aghamelu, Adil Ahmed +327
We introduce Muse Glimmer, an open-source 30B parameter causal language model with perception encoder, purpose-built for autonomous agentic tasks on consumer hardware. Muse Glimmer…
Hallucination reduction with CASAL: Contrastive Activation Steering For Amortized Learning
Wannan, Yang, Xinchi Qiu +6
Large Language Models (LLMs) exhibit impressive capabilities but often hallucinate, confidently providing incorrect answers instead of admitting ignorance. Prior work has shown tha…
How much do language models memorize?
John X. Morris, Chawin Sitawarin, Chuan Guo +5
We propose a new method for estimating how much a model knows about a datapoint and use it to measure the capacity of modern language models. Prior studies of language model memori…
Using Captum to Explain Generative Language Models
Vivek Miglani, Aobo Yang, Aram H. Markosyan +2
Captum is a comprehensive library for model explainability in PyTorch, offering a range of methods from the interpretability literature to enhance users' understanding of PyTorch m…
Fine-grained Interpretation and Causation Analysis in Deep NLP Models
Hassan Sajjad, Narine Kokhlikyan, Fahim Dalvi +1
This paper is a write-up for the tutorial on "Fine-grained Interpretation and Causation Analysis in Deep NLP Models" that we are presenting at NAACL 2021. We present and discuss th…