5 citations · 13 across the 12 of their papers we have counts for
12 papers
Hypotheses-Guided Self Distillation for Continual Personalization
EunJeong Hwang, Kushan Mitra, Dan Zhang +2
As people increasingly interact with LLM assistants in daily life, continually adapting to individual preferences has become essential for effective long-term interactions. However…
Blue Data Intelligence Layer: Streaming Data and Agents for Multi-source Multi-modal Data-Centric Applications
Moin Aminnaseri, Farima Fatahi Bayat, Nikita Bhutani +17
NL2SQL systems aim to address the growing need for natural language interaction with data. However, real-world information rarely maps to a single SQL query because (1) users expre…
Learning from Supervision with Semantic and Episodic Memory: A Reflective Approach to Agent Adaptation
Jackson Hassell, Dan Zhang, Hannah Kim +2
We investigate how agents built on pretrained large language models (LLMs) can learn target classification functions from labeled examples without parameter updates. While conventi…
AIPOM: Agent-aware Interactive Planning for Multi-Agent Systems
Hannah Kim, Kushan Mitra, Chen Shen +2
Large language models (LLMs) are being increasingly used for planning in orchestrated multi-agent systems. However, existing LLM-based approaches often fall short of human expectat…
RECAP: REwriting Conversations for Intent Understanding in Agentic Planning
Kushan Mitra, Dan Zhang, Hannah Kim +1
Understanding user intent is essential for effective planning in conversational assistants, particularly those powered by large language models (LLMs) coordinating multiple agents.…
A Blueprint Architecture of Compound AI Systems for Enterprise
Eser Kandogan, Sajjadur Rahman, Nikita Bhutani +11
Large Language Models (LLMs) have showcased remarkable capabilities surpassing conventional NLP challenges, creating opportunities for use in production use cases. Towards this goa…
MEGAnno+: A Human-LLM Collaborative Annotation System
Hannah Kim, Kushan Mitra, Rafael Li Chen +2
Large language models (LLMs) can label data faster and cheaper than humans for various NLP tasks. Despite their prowess, LLMs may fall short in understanding of complex, sociocultu…
Knowledge Acquisition and Integration with Expert-in-the-loop
Sajjadur Rahman, Frederick Choi, Hannah Kim +2
Constructing and serving knowledge graphs (KGs) is an iterative and human-centered process involving on-demand programming and analysis. In this paper, we present Kyurem, a program…
Characterizing Large Language Models as Rationalizers of Knowledge-intensive Tasks
Aditi Mishra, Sajjadur Rahman, Hannah Kim +2
Large language models (LLMs) are proficient at generating fluent text with minimal task-specific supervision. Yet, their ability to provide well-grounded rationalizations for knowl…
Towards Transparent, Reusable, and Customizable Data Science in Computational Notebooks
Frederick Choi, Sajjadur Rahman, Hannah Kim +1
Data science workflows are human-centered processes involving on-demand programming and analysis. While programmable and interactive interfaces such as widgets embedded within comp…
Towards Multifaceted Human-Centered AI
Sajjadur Rahman, Hannah Kim, Dan Zhang +2
Human-centered AI workflows involve stakeholders with multiple roles interacting with each other and automated agents to accomplish diverse tasks. In this paper, we call for a holi…
MEGAnno: Exploratory Labeling for NLP in Computational Notebooks
Dan Zhang, Hannah Kim, Rafael Li Chen +2
We present MEGAnno, a novel exploratory annotation framework designed for NLP researchers and practitioners. Unlike existing labeling tools that focus on data labeling only, our fr…