8 citations · 25 across the 16 of their papers we have counts for
18 papers
ExperienceIndex: Artifact-Grounded Memory
Peter Baile Chen, Geoffrey X. Yu, Xinming Liu +5
Knowledge-intensive tasks require answering many questions by reasoning about a shared corpus of artifacts (e.g., court cases, or scientific literature). As humans interact with th…
Carnot: Interpretable, Interactive, and Optimized Execution of Deep Research Queries
Matthew Russo, Yash Agarwal, Tianyu Li +5
Enterprises increasingly seek to query data lakes using natural language via AI-driven tools like semantic operators or deep research agents. However, the latter operates as an opa…
Agent-Aided Design for Dynamic CAD Models
Mitch Adler, Matthew Russo, Michael Cafarella
In the past year, researchers have created agentic systems that can design real-world CAD-style objects in a training-free setting, a new variety of system that we call Agent-Aided…
SAGE: Selective Attention-Guided Extraction for Token-Efficient Document Indexing
Xinzhi Wang, Peter Baile Chen, Gerardo Vitagliano +5
Large language models with long context windows can answer complex questions directly from full-length academic, technical, and policy documents, but passing entire documents is of…
CONCUR: A Framework for Continual Constrained and Unconstrained Routing
Peter Baile Chen, Weiyue Li, Dan Roth +3
AI tasks differ in complexity and are best addressed with different computation strategies (e.g., combinations of models and decoding methods). Hence, an effective routing system t…
OpenEstimate: Evaluating LLMs on Reasoning Under Uncertainty with Real-World Data
Alana Renda, Jillian Ross, Michael Cafarella +1
Real-world settings where language models (LMs) are deployed -- in domains spanning healthcare, finance, and other forms of knowledge work -- require models to grapple with incompl…
Log-Augmented Generation: Scaling Test-Time Reasoning with Reusable Computation
Peter Baile Chen, Yi Zhang, Dan Roth +3
While humans naturally learn and adapt from past experiences, large language models (LLMs) and their agentic counterparts struggle to retain reasoning from previous tasks and apply…
Abacus: A Cost-Based Optimizer for Semantic Operator Systems
Matthew Russo, Chunwei Liu, Sivaprasad Sudhir +4
LLMs enable an exciting new class of data processing applications over large collections of unstructured documents. Several new programming frameworks have enabled developers to bu…
The Cambridge Report on Database Research
Anastasia Ailamaki, Samuel Madden, Daniel Abadi +43
On October 19 and 20, 2023, the authors of this report convened in Cambridge, MA, to discuss the state of the database research field, its recent accomplishments and ongoing challe…
EnrichIndex: Using LLMs to Enrich Retrieval Indices Offline
Peter Baile Chen, Tomer Wolfson, Michael Cafarella +1
Existing information retrieval systems excel in cases where the language of target documents closely matches that of the user query. However, real-world retrieval systems are often…
PalimpChat: Declarative and Interactive AI analytics
Chunwei Liu, Gerardo Vitagliano, Brandon Rose +3
Thanks to the advances in generative architectures and large language models, data scientists can now code pipelines of machine-learning operations to process large collections of…
Can we Retrieve Everything All at Once? ARM: An Alignment-Oriented LLM-based Retrieval Method
Peter Baile Chen, Yi Zhang, Michael Cafarella +1
Real-world open-domain questions can be complicated, particularly when answering them involves information from multiple information sources. LLMs have demonstrated impressive perf…
Variable Extraction for Model Recovery in Scientific Literature
Chunwei Liu, Enrique Noriega-Atala, Adarsh Pyarelal +2
The global output of academic publications exceeds 5 million articles per year, making it difficult for humans to keep up with even a tiny fraction of scientific output. We need me…
Is the GPU Half-Empty or Half-Full? Practical Scheduling Techniques for LLMs
Ferdi Kossmann, Bruce Fontaine, Daya Khudia +2
Serving systems for Large Language Models (LLMs) improve throughput by processing several requests concurrently. However, multiplexing hardware resources between concurrent request…
BEAVER: An Enterprise Benchmark for Text-to-SQL
Peter Baile Chen, Devin Yang, Weiyue Li +6
Existing text-to-SQL benchmarks have largely been constructed from public databases with well-structured schemas and simplistic question-SQL pairs. While large language models (LLM…
MDCR: A Dataset for Multi-Document Conditional Reasoning
Peter Baile Chen, Yi Zhang, Chunwei Liu +3
The same real-life questions posed to different individuals may lead to different answers based on their unique situations. For instance, whether a student is eligible for a schola…
A Declarative System for Optimizing AI Workloads
Chunwei Liu, Matthew Russo, Michael Cafarella +7
A long-standing goal of data management systems has been to build systems which can compute quantitative insights over large corpora of unstructured data in a cost-effective manner…
SEED: Domain-Specific Data Curation With Large Language Models
Zui Chen, Lei Cao, Sam Madden +7
Data curation tasks that prepare data for analytics are critical for turning data into actionable insights. However, due to the diverse requirements of applications in different do…