4 citations · 6 across the 9 of their papers we have counts for
9 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…
Verification-Aware Planning for Multi-Agent Systems
Tianyang Xu, Dan Zhang, Kushan Mitra +1
Large language model (LLM) agents are increasingly deployed to tackle complex tasks, often necessitating collaboration among multiple specialized agents. However, multi-agent colla…
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.…
FactLens: Benchmarking Fine-Grained Fact Verification
Kushan Mitra, Dan Zhang, Sajjadur Rahman +1
Large Language Models (LLMs) have shown impressive capability in language generation and understanding, but their tendency to hallucinate and produce factually incorrect informatio…
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…
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…