5 citations · 12 across the 12 of their papers we have counts for
12 papers
Towards Direct Evaluation of Harness Optimizers via Priority Ranking
Kai Tzu-iunn Ong, Minseok Kang, Dongwook Choi +11
Harness optimization enables automated agent creation by having an optimizer agent iteratively update the harness of target agents. Despite its success, current studies evaluate op…
PRINCIPLES: Synthetic Strategy Memory for Proactive Dialogue Agents
Namyoung Kim, Kai Tzu-iunn Ong, Yeonjun Hwang +5
Dialogue agents based on large language models (LLMs) have shown promising performance in proactive dialogue, which requires effective strategy planning. However, existing approach…
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…
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…
Large Language Models Are Self-Taught Reasoners: Enhancing LLM Applications via Tailored Problem-Solving Demonstrations
Kai Tzu-iunn Ong, Taeyoon Kwon, Jinyoung Yeo
Guiding large language models with a selected set of human-authored demonstrations is a common practice for improving LLM applications. However, human effort can be costly, especia…
Towards Lifelong Dialogue Agents via Timeline-based Memory Management
Kai Tzu-iunn Ong, Namyoung Kim, Minju Gwak +6
To achieve lifelong human-agent interaction, dialogue agents need to constantly memorize perceived information and properly retrieve it for response generation (RG). While prior st…
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…
Commonsense-augmented Memory Construction and Management in Long-term Conversations via Context-aware Persona Refinement
Hana Kim, Kai Tzu-iunn Ong, Seoyeon Kim +2
Memorizing and utilizing speakers' personas is a common practice for response generation in long-term conversations. Yet, human-authored datasets often provide uninformative person…
Large Language Models are Clinical Reasoners: Reasoning-Aware Diagnosis Framework with Prompt-Generated Rationales
Taeyoon Kwon, Kai Tzu-iunn Ong, Dongjin Kang +7
Machine reasoning has made great progress in recent years owing to large language models (LLMs). In the clinical domain, however, most NLP-driven projects mainly focus on clinical…
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…
Evidence-empowered Transfer Learning for Alzheimer's Disease
Kai Tzu-iunn Ong, Hana Kim, Minjin Kim +6
Transfer learning has been widely utilized to mitigate the data scarcity problem in the field of Alzheimer's disease (AD). Conventional transfer learning relies on re-using models…