13 citations · 29 across the 22 of their papers we have counts for
17 papers · 1 filter
Follow the Entities: A Corpus Map for Agentic Search
Soyeong Jeong, Sujay Kumar Jauhar, Sung Ju Hwang +1
Answering questions and completing tasks over large document collections often requires connecting evidence spread across multiple documents, such as a project's approval recorded…
MemRefine: LLM-Guided Compression for Long-Term Agent Memory
Minjae Kim, Jinheon Baek, Soyeong Jeong +1
Large language model (LLM) agents are increasingly expected to operate over long-term interactions, where information from past dialogues must be preserved and recalled to support…
TIDE: Proactive Multi-Problem Discovery via Template-Guided Iteration
Soyeong Jeong, Jinheon Baek, Minki Kang +1
Agents are widely deployed as assistants over documents, tools, and code. However, they typically act only on explicit user requests, which surface only the problems the user has n…
When Thoughts Meet Facts: Reusable Reasoning for Long-Context LMs
Soyeong Jeong, Taehee Jung, Sung Ju Hwang +2
Recent Long-Context Language Models (LCLMs) can process hundreds of thousands of tokens in a single prompt, enabling new opportunities for knowledge-intensive multi-hop reasoning b…
System Prompt Optimization with Meta-Learning
Yumin Choi, Jinheon Baek, Sung Ju Hwang
Large Language Models (LLMs) have shown remarkable capabilities, with optimizing their input prompts playing a pivotal role in maximizing their performance. However, while LLM prom…
UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities
Woongyeong Yeo, Kangsan Kim, Soyeong Jeong +2
Retrieval-Augmented Generation (RAG) has shown substantial promise in improving factual accuracy by grounding model responses with external knowledge relevant to queries. However,…
Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning
Minju Seo, Jinheon Baek, Seongyun Lee +1
Despite the rapid growth of machine learning research, corresponding code implementations are often unavailable, making it slow and labor-intensive for researchers to reproduce res…
Sketch-of-Thought: Efficient LLM Reasoning with Adaptive Cognitive-Inspired Sketching
Simon A. Aytes, Jinheon Baek, Sung Ju Hwang
Recent advances in large language models (LLMs) have enabled strong reasoning capabilities through Chain-of-Thought (CoT) prompting, which elicits step-by-step problem solving, but…
Efficient Real-time Refinement of Language Model Text Generation
Joonho Ko, Jinheon Baek, Sung Ju Hwang
Large language models (LLMs) have shown remarkable performance across a wide range of natural language tasks. However, a critical challenge remains in that they sometimes generate…
Unified Multimodal Interleaved Document Representation for Retrieval
Jaewoo Lee, Joonho Ko, Jinheon Baek +2
Information Retrieval (IR) methods aim to identify documents relevant to a query, which have been widely applied in various natural language tasks. However, existing approaches typ…
Database-Augmented Query Representation for Information Retrieval
Soyeong Jeong, Jinheon Baek, Sukmin Cho +2
Information retrieval models that aim to search for documents relevant to a query have shown multiple successes, which have been applied to diverse tasks. Yet, the query from the u…
LG AI Research & KAIST at EHRSQL 2024: Self-Training Large Language Models with Pseudo-Labeled Unanswerable Questions for a Reliable Text-to-SQL System on EHRs
Yongrae Jo, Seongyun Lee, Minju Seo +2
Text-to-SQL models are pivotal for making Electronic Health Records (EHRs) accessible to healthcare professionals without SQL knowledge. With the advancements in large language mod…
ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models
Jinheon Baek, Sujay Kumar Jauhar, Silviu Cucerzan +1
The pace of scientific research, vital for improving human life, is complex, slow, and needs specialized expertise. Meanwhile, novel, impactful research often stems from both a dee…
Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity
Soyeong Jeong, Jinheon Baek, Sukmin Cho +2
Retrieval-Augmented Large Language Models (LLMs), which incorporate the non-parametric knowledge from external knowledge bases into LLMs, have emerged as a promising approach to en…
Retrieval-Augmented Data Augmentation for Low-Resource Domain Tasks
Minju Seo, Jinheon Baek, James Thorne +1
Despite large successes of recent language models on diverse tasks, they suffer from severe performance degeneration in low-resource settings with limited training data available.…
Test-Time Self-Adaptive Small Language Models for Question Answering
Soyeong Jeong, Jinheon Baek, Sukmin Cho +2
Recent instruction-finetuned large language models (LMs) have achieved notable performances in various tasks, such as question-answering (QA). However, despite their ability to mem…
Knowledge-Augmented Language Model Verification
Jinheon Baek, Soyeong Jeong, Minki Kang +2
Recent Language Models (LMs) have shown impressive capabilities in generating texts with the knowledge internalized in parameters. Yet, LMs often generate the factually incorrect r…