69 citations · 90 across the 11 of their papers we have counts for
11 papers
: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities
Physical Intelligence, Bo Ai, Ali Amin +85
We present a new robotic foundation model, called , that can enable strong out-of-the-box performance in a wide range of scenarios. can follow diverse language i…
Deconstructing In-Context Learning: Understanding Prompts via Corruption
Namrata Shivagunde, Vladislav Lialin, Sherin Muckatira +1
The ability of large language models (LLMs) to learn in context based on the provided prompt has led to an explosive growth in their use, culminating in the proliferation of…
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium
Hyewon Jeong, Sarah Jabbour, Yuzhe Yang +40
The third ML4H symposium was held in person on December 10, 2023, in New Orleans, Louisiana, USA. The symposium included research roundtable sessions to foster discussions between…
Emergent Abilities in Reduced-Scale Generative Language Models
Sherin Muckatira, Vijeta Deshpande, Vladislav Lialin +1
Large language models can solve new tasks without task-specific fine-tuning. This ability, also known as in-context learning (ICL), is considered an emergent ability and is primari…
Let's Reinforce Step by Step
Sarah Pan, Vladislav Lialin, Sherin Muckatira +1
While recent advances have boosted LM proficiency in linguistic benchmarks, LMs consistently struggle to reason correctly on complex tasks like mathematics. We turn to Reinforcemen…
ReLoRA: High-Rank Training Through Low-Rank Updates
Vladislav Lialin, Namrata Shivagunde, Sherin Muckatira +1
Despite the dominance and effectiveness of scaling, resulting in large networks with hundreds of billions of parameters, the necessity to train overparameterized models remains poo…
Honey, I Shrunk the Language: Language Model Behavior at Reduced Scale
Vijeta Deshpande, Dan Pechi, Shree Thatte +2
In recent years, language models have drastically grown in size, and the abilities of these models have been shown to improve with scale. The majority of recent scaling laws studie…
Scalable and Accurate Self-supervised Multimodal Representation Learning without Aligned Video and Text Data
Vladislav Lialin, Stephen Rawls, David Chan +3
Scaling up weakly-supervised datasets has shown to be highly effective in the image-text domain and has contributed to most of the recent state-of-the-art computer vision and multi…
Larger Probes Tell a Different Story: Extending Psycholinguistic Datasets Via In-Context Learning
Namrata Shivagunde, Vladislav Lialin, Anna Rumshisky
Language model probing is often used to test specific capabilities of models. However, conclusions from such studies may be limited when the probing benchmarks are small and lack s…
Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning
Vladislav Lialin, Vijeta Deshpande, Xiaowei Yao +1
This paper presents a systematic overview of parameter-efficient fine-tuning methods, covering over 50 papers published between early 2019 and mid-2024. These methods aim to addres…
NarrativeTime: Dense Temporal Annotation on a Timeline
Anna Rogers, Marzena Karpinska, Ankita Gupta +3
For the past decade, temporal annotation has been sparse: only a small portion of event pairs in a text was annotated. We present NarrativeTime, the first timeline-based annotation…