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Seung-won Hwang

9 papers hereh-index 451 citations17 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author6
  • last author3

Across the 9 of 9 papers where every author was matched, so the position is known.

fields
  • cs.CL5
  • cs.SE3
  • cs.MA1
same name
  • Seung-won Hwang — 25 papers, h 34
  • Seung-won Hwang — 7 papers, h 8
  • Seung-won Hwang — 7 papers, h 4
  • Seung-won Hwang — 7 papers, h 4
  • Seung-won Hwang — 6 papers, h 5
  • Seung-won Hwang — 6 papers, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
collaborators
Showing 2024Show all

4 papers · 1 filter

cs.SE2024

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…

cs.CL2024

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…

cs.CL2024

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…

cs.SE2024

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…

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