2 citations · 2 across the 7 of their papers we have counts for
9 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…
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…
REaR: Retrieve, Expand and Refine for Effective Multitable Retrieval
Rishita Agarwal, Himanshu Singhal, Peter Baile Chen +3
Answering natural language queries over relational data often requires retrieving and reasoning over multiple tables, yet most retrievers optimize only for query-table relevance an…
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…
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…
Self-supervised Analogical Learning using Language Models
Ben Zhou, Sarthak Jain, Yi Zhang +4
Large language models have been shown to suffer from reasoning inconsistency issues. That is, they fail more in situations unfamiliar to the training data, even though exact or ver…
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…
Open Domain Question Answering with Conflicting Contexts
Siyi Liu, Qiang Ning, Kishaloy Halder +8
Open domain question answering systems frequently rely on information retrieved from large collections of text (such as the Web) to answer questions. However, such collections of t…
Is Table Retrieval a Solved Problem? Exploring Join-Aware Multi-Table Retrieval
Peter Baile Chen, Yi Zhang, Dan Roth
Retrieving relevant tables containing the necessary information to accurately answer a given question over tables is critical to open-domain question-answering (QA) systems. Previo…