10 citations · 20 across the 8 of their papers we have counts for
8 papers
Continual Learning Bench: Evaluating Frontier AI Systems in Real-World Stateful Environments
Parth Asawa, Christopher M. Glaze, Gabriel Orlanski +7
Continual learning, the ability of AI systems to improve through sequential experience, has attracted substantial interest, but no high-quality benchmark exists to evaluate it. We…
SIEVE: Sample-Efficient Parametric Learning from Natural Language
Parth Asawa, Alexandros G. Dimakis, Matei Zaharia
Natural language context-such as instructions, knowledge, or feedback-contains rich signal for adapting language models. While in-context learning provides adaptation via the promp…
How to Train Your Advisor: Steering Black-Box LLMs with Advisor Models
Parth Asawa, Alan Zhu, Abigail O'Neill +3
Frontier language models are deployed as black-box services, where model weights cannot be modified and customization is limited to prompting. We introduce Advisor Models, a method…
BARE: Leveraging Base Language Models for Few-Shot Synthetic Data Generation
Alan Zhu, Parth Asawa, Jared Quincy Davis +5
As the demand for high-quality data in model training grows, researchers and developers are increasingly generating synthetic data to tune and train LLMs. However, current data gen…
Semantic Operators: A Declarative Model for Rich, AI-based Data Processing
Liana Patel, Siddharth Jha, Melissa Pan +4
The semantic capabilities of large language models (LLMs) have the potential to enable rich analytics and reasoning over vast knowledge corpora. Unfortunately, existing systems eit…
SPADE: Synthesizing Data Quality Assertions for Large Language Model Pipelines
Shreya Shankar, Haotian Li, Parth Asawa +7
Large language models (LLMs) are being increasingly deployed as part of pipelines that repeatedly process or generate data of some sort. However, a common barrier to deployment are…
Revisiting Prompt Engineering via Declarative Crowdsourcing
Aditya G. Parameswaran, Shreya Shankar, Parth Asawa +2
Large language models (LLMs) are incredibly powerful at comprehending and generating data in the form of text, but are brittle and error-prone. There has been an advent of toolkits…
COVID-19 in Spain and India: Comparing Policy Implications by Analyzing Epidemiological and Social Media Data
Parth Asawa, Manas Gaur, Kaushik Roy +1
The COVID-19 pandemic has forced public health experts to develop contingent policies to stem the spread of infection, including measures such as partial/complete lockdowns. The ef…