25 citations · 85 across the 17 of their papers we have counts for
19 papers
Meta: Recursive Self-Improvement through Emergent Depth
Zae Myung Kim, Young-Jun Lee, Seungyeon Jwa +1
Self-improving LLM agents refine answers, not the process that produces those answers. Systems that add a meta-level hold that level fixed, and those that edit themselves must leav…
Structure Liberates: How Constrained Sensemaking Produces More Novel Research Output
James Mooney, Zae Myung Kim, Young-Jun Lee +1
Scientific discovery is an extended process of ideation -- surveying prior work, forming hypotheses, and refining reasoning -- yet existing LLM research agents treat this phase as…
The Amazing Agent Race: Strong Tool Users, Weak Navigators
Zae Myung Kim, Dongseok Lee, Jaehyung Kim +2
Existing tool-use benchmarks for LLM agents are overwhelmingly linear: our analysis of six benchmarks shows 55 to 100% of instances are simple chains of 2 to 5 steps. We introduce…
Toward Evaluative Thinking: Meta Policy Optimization with Evolving Reward Models
Zae Myung Kim, Chanwoo Park, Vipul Raheja +2
Reward-based alignment methods for large language models (LLMs) face two key limitations: vulnerability to reward hacking, where models exploit flaws in the reward signal; and reli…
Align to Structure: Aligning Large Language Models with Structural Information
Zae Myung Kim, Anand Ramachandran, Farideh Tavazoee +3
Generating long, coherent text remains a challenge for large language models (LLMs), as they lack hierarchical planning and structured organization in discourse generation. We intr…
Anchors Aweigh! Sail for Optimal Unified Multi-Modal Representations
Minoh Jeong, Zae Myung Kim, Min Namgung +3
A unified representation space in multi-modal learning is essential for effectively integrating diverse data sources, such as text, images, and audio, to enhance efficiency and per…
Human-AI Collaborative Taxonomy Construction: A Case Study in Profession-Specific Writing Assistants
Minhwa Lee, Zae Myung Kim, Vivek Khetan +1
Large Language Models (LLMs) have assisted humans in several writing tasks, including text revision and story generation. However, their effectiveness in supporting domain-specific…
Threads of Subtlety: Detecting Machine-Generated Texts Through Discourse Motifs
Zae Myung Kim, Kwang Hee Lee, Preston Zhu +2
With the advent of large language models (LLM), the line between human-crafted and machine-generated texts has become increasingly blurred. This paper delves into the inquiry of id…
Under the Surface: Tracking the Artifactuality of LLM-Generated Data
Debarati Das, Karin De Langis, Anna Martin-Boyle +14
This work delves into the expanding role of large language models (LLMs) in generating artificial data. LLMs are increasingly employed to create a variety of outputs, including ann…
Benchmarking Cognitive Biases in Large Language Models as Evaluators
Ryan Koo, Minhwa Lee, Vipul Raheja +3
Large Language Models are cognitively biased judges. Large Language Models (LLMs) have recently been shown to be effective as automatic evaluators with simple prompting and in-cont…
A Survey of Diffusion Models in Natural Language Processing
Hao Zou, Zae Myung Kim, Dongyeop Kang
This survey paper provides a comprehensive review of the use of diffusion models in natural language processing (NLP). Diffusion models are a class of mathematical models that aim…
An Analysis of Reader Engagement in Literary Fiction through Eye Tracking and Linguistic Features
Rose Neis, Karin de Langis, Zae Myung Kim +1
Capturing readers' engagement in fiction is a challenging but important aspect of narrative understanding. In this study, we collected 23 readers' reactions to 2 short stories thro…
"Is the Pope Catholic?" Applying Chain-of-Thought Reasoning to Understanding Conversational Implicatures
Zae Myung Kim, David E. Taylor, Dongyeop Kang
Conversational implicatures are pragmatic inferences that require listeners to deduce the intended meaning conveyed by a speaker from their explicit utterances. Although such infer…
Diffusion Models in NLP: A Survey
Yuansong Zhu, Yu Zhao
Diffusion models have become a powerful family of deep generative models, with record-breaking performance in many applications. This paper first gives an overview and derivation o…
Improving Iterative Text Revision by Learning Where to Edit from Other Revision Tasks
Zae Myung Kim, Wanyu Du, Vipul Raheja +2
Iterative text revision improves text quality by fixing grammatical errors, rephrasing for better readability or contextual appropriateness, or reorganizing sentence structures thr…
Read, Revise, Repeat: A System Demonstration for Human-in-the-loop Iterative Text Revision
Wanyu Du, Zae Myung Kim, Vipul Raheja +2
Revision is an essential part of the human writing process. It tends to be strategic, adaptive, and, more importantly, iterative in nature. Despite the success of large language mo…
Understanding Iterative Revision from Human-Written Text
Wanyu Du, Vipul Raheja, Dhruv Kumar +3
Writing is, by nature, a strategic, adaptive, and more importantly, an iterative process. A crucial part of writing is editing and revising the text. Previous works on text revisio…
Do Multilingual Neural Machine Translation Models Contain Language Pair Specific Attention Heads?
Zae Myung Kim, Laurent Besacier, Vassilina Nikoulina +1
Recent studies on the analysis of the multilingual representations focus on identifying whether there is an emergence of language-independent representations, or whether a multilin…
A Multilingual Neural Machine Translation Model for Biomedical Data
Alexandre Bérard, Zae Myung Kim, Vassilina Nikoulina +2
We release a multilingual neural machine translation model, which can be used to translate text in the biomedical domain. The model can translate from 5 languages (French, German,…