9 papers
ArcticSwarm: Deferring Early Consensus in Long-Horizon Multi-Agent Research
Soyoung Yoon, Boyi Liu, Yite Wang +6
Multi-agent systems have shown strong performance in domains with reliable verifiers such as coding, where multi-parallel candidate generation selected by a verifier is effective.…
Bootstrapping Semantic Layer from Execution for Text-to-SQL
Youngwon Lee, Jaejin Kim, Seung-won Hwang
Real-world text-to-SQL is often under-specified until user phrases are grounded in how the database stores values. Prior work attempts to address this by requiring a semantic layer…
ComposeRAG: A Modular and Composable RAG for Corpus-Grounded Multi-Hop Question Answering
Ruofan Wu, Youngwon Lee, Fan Shu +5
Retrieval-Augmented Generation (RAG) systems are increasingly diverse, yet many suffer from monolithic designs that tightly couple core functions like query reformulation, retrieva…
ConvCodeWorld: Benchmarking Conversational Code Generation in Reproducible Feedback Environments
Hojae Han, Seung-won Hwang, Rajhans Samdani +1
Large language models (LLMs) have proven invaluable for code generation, particularly in interactive settings. However, existing code generation benchmarks fail to capture the dive…
Agentic Verification for Ambiguous Query Disambiguation
Youngwon Lee, Seung-won Hwang, Ruofan Wu +5
In this work, we tackle the challenge of disambiguating queries in retrieval-augmented generation (RAG) to diverse yet answerable interpretations. State-of-the-arts follow a Divers…
PERC: Plan-As-Query Example Retrieval for Underrepresented Code Generation
Jaeseok Yoo, Hojae Han, Youngwon Lee +2
Code generation with large language models has shown significant promise, especially when employing retrieval-augmented generation (RAG) with few-shot examples. However, selecting…
CORD: Balancing COnsistency and Rank Distillation for Robust Retrieval-Augmented Generation
Youngwon Lee, Seung-won Hwang, Daniel Campos +3
With the adoption of retrieval-augmented generation (RAG), large language models (LLMs) are expected to ground their generation to the retrieved contexts. Yet, this is hindered by…
Inference Scaling for Bridging Retrieval and Augmented Generation
Youngwon Lee, Seung-won Hwang, Daniel Campos +3
Retrieval-augmented generation (RAG) has emerged as a popular approach to steering the output of a large language model (LLM) by incorporating retrieved contexts as inputs. However…
ArchCode: Incorporating Software Requirements in Code Generation with Large Language Models
Hojae Han, Jaejin Kim, Jaeseok Yoo +2
This paper aims to extend the code generation capability of large language models (LLMs) to automatically manage comprehensive software requirements from given textual descriptions…