12 citations · 22 across the 19 of their papers we have counts for
20 papers
Safe and Scalable Web Agent Learning via Recreated Websites
Hyungjoo Chae, Jungsoo Park, Alan Ritter
Training autonomous web agents is fundamentally limited by the environments they learn from: real-world websites are unsafe to explore, hard to reset, and rarely provide verifiable…
Apples on the Table? Evaluating Text-Guided 3D Scene Synthesis via Fine-Grained Constraint Verification
Minseok Kang, Dongwook Choi, Gyeom Hwangbo +3
Accurately synthesizing 3D scenes from user-provided text descriptions is crucial for developing embodied agents. Despite the importance of scene-description alignment, existing ev…
Can You Share Your Story? Modeling Clients' Metacognition and Openness for LLM Therapist Evaluation
Minju Kim, Dongje Yoo, Yeonjun Hwang +11
Understanding clients' thoughts and beliefs is fundamental in counseling, yet current evaluations of LLM therapists often fail to assess this ability. Existing evaluation methods r…
One Missing Piece for Open-Source Reasoning Models: A Dataset to Mitigate Cold-Starting Short CoT LLMs in RL
Hyungjoo Chae, Dongjin Kang, Jihyuk Kim +6
With the release of R1, a publicly available large reasoning model (LRM), researchers commonly train new LRMs by training language models on R1's long chain-of-thought (CoT) infere…
Rethinking Reward Model Evaluation Through the Lens of Reward Overoptimization
Sunghwan Kim, Dongjin Kang, Taeyoon Kwon +3
Reward models (RMs) play a crucial role in reinforcement learning from human feedback (RLHF), aligning model behavior with human preferences. However, existing benchmarks for rewar…
ToolHaystack: Stress-Testing Tool-Augmented Language Models in Realistic Long-Term Interactions
Beong-woo Kwak, Minju Kim, Dongha Lim +5
Large language models (LLMs) have demonstrated strong capabilities in using external tools to address user inquiries. However, most existing evaluations assume tool use in short co…
Web-Shepherd: Advancing PRMs for Reinforcing Web Agents
Hyungjoo Chae, Sunghwan Kim, Junhee Cho +18
Web navigation is a unique domain that can automate many repetitive real-life tasks and is challenging as it requires long-horizon sequential decision making beyond typical multimo…
Coffee-Gym: An Environment for Evaluating and Improving Natural Language Feedback on Erroneous Code
Hyungjoo Chae, Taeyoon Kwon, Seungjun Moon +7
This paper presents Coffee-Gym, a comprehensive RL environment for training models that provide feedback on code editing. Coffee-Gym includes two major components: (1) Coffee, a da…
Evaluating Robustness of Reward Models for Mathematical Reasoning
Sunghwan Kim, Dongjin Kang, Taeyoon Kwon +4
Reward models are key in reinforcement learning from human feedback (RLHF) systems, aligning the model behavior with human preferences. Particularly in the math domain, there have…
Web Agents with World Models: Learning and Leveraging Environment Dynamics in Web Navigation
Hyungjoo Chae, Namyoung Kim, Kai Tzu-iunn Ong +6
Large language models (LLMs) have recently gained much attention in building autonomous agents. However, the performance of current LLM-based web agents in long-horizon tasks is fa…
Do LLMs Have Distinct and Consistent Personality? TRAIT: Personality Testset designed for LLMs with Psychometrics
Seungbeen Lee, Seungwon Lim, Seungju Han +9
Recent advancements in Large Language Models (LLMs) have led to their adaptation in various domains as conversational agents. We wonder: can personality tests be applied to these a…
The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models
Seungone Kim, Juyoung Suk, Ji Yong Cho +29
As language models (LMs) become capable of handling a wide range of tasks, their evaluation is becoming as challenging as their development. Most generation benchmarks currently as…
Language Models as Compilers: Simulating Pseudocode Execution Improves Algorithmic Reasoning in Language Models
Hyungjoo Chae, Yeonghyeon Kim, Seungone Kim +8
Algorithmic reasoning refers to the ability to understand the complex patterns behind the problem and decompose them into a sequence of reasoning steps towards the solution. Such n…
Evidence-Focused Fact Summarization for Knowledge-Augmented Zero-Shot Question Answering
Sungho Ko, Hyunjin Cho, Hyungjoo Chae +2
Recent studies have investigated utilizing Knowledge Graphs (KGs) to enhance Quesetion Answering (QA) performance of Large Language Models (LLMs), yet structured KG verbalization r…
VerifiNER: Verification-augmented NER via Knowledge-grounded Reasoning with Large Language Models
Seoyeon Kim, Kwangwook Seo, Hyungjoo Chae +2
Recent approaches in domain-specific named entity recognition (NER), such as biomedical NER, have shown remarkable advances. However, they still lack of faithfulness, producing err…
Coffee: Boost Your Code LLMs by Fixing Bugs with Feedback
Seungjun Moon, Hyungjoo Chae, Yongho Song +5
Code editing is an essential step towards reliable program synthesis to automatically correct critical errors generated from code LLMs. Recent studies have demonstrated that closed…
Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agents
Hyungjoo Chae, Yongho Song, Kai Tzu-iunn Ong +6
Human-like chatbots necessitate the use of commonsense reasoning in order to effectively comprehend and respond to implicit information present within conversations. Achieving such…
CoTEVer: Chain of Thought Prompting Annotation Toolkit for Explanation Verification
Seungone Kim, Se June Joo, Yul Jang +2
Chain-of-thought (CoT) prompting enables large language models (LLMs) to solve complex reasoning tasks by generating an explanation before the final prediction. Despite it's promis…
TUTORING: Instruction-Grounded Conversational Agent for Language Learners
Hyungjoo Chae, Minjin Kim, Chaehyeong Kim +4
In this paper, we propose Tutoring bot, a generative chatbot trained on a large scale of tutor-student conversations for English-language learning. To mimic a human tutor's behavio…
Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization
Seungone Kim, Se June Joo, Hyungjoo Chae +3
In this paper, we propose to leverage the unique characteristics of dialogues sharing commonsense knowledge across participants, to resolve the difficulties in summarizing them. We…