8 papers
FutureWeaver: Planning Test-Time Compute for Multi-Agent Systems with Modularized Collaboration
Dongwon Jung, Peng Shi, Muhao Chen +1
Scaling test-time computation has been shown to significantly improve large language model (LLM) performance without additional training. However, extending these techniques to mul…
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…
IPR: Intelligent Prompt Routing with User-Controlled Quality-Cost Trade-offs
Aosong Feng, Balasubramaniam Srinivasan, Yun Zhou +14
Routing incoming queries to the most cost-effective LLM while maintaining response quality poses a fundamental challenge in optimizing performance-cost trade-offs for large-scale c…
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…
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…
On Synthetic Data Strategies for Domain-Specific Generative Retrieval
Haoyang Wen, Jiang Guo, Yi Zhang +2
This paper investigates synthetic data generation strategies in developing generative retrieval models for domain-specific corpora, thereby addressing the scalability challenges in…