7 citations · 9 across the 6 of their papers we have counts for
6 papers
Can David Beat Goliath? On Multi-Hop Reasoning with Resource-Constrained Agents
Hojae Han, Heeyun Jung, Jongyoon Kim +1
Reinforcement learning (RL) trains small language model agents to answer multi-hop questions by retrieving evidence over multiple turns, but reported gains typically rely on thousa…
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…
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…
Ever-Evolving Memory by Blending and Refining the Past
Seo Hyun Kim, Keummin Ka, Yohan Jo +3
For a human-like chatbot, constructing a long-term memory is crucial. However, current large language models often lack this capability, leading to instances of missing important u…
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…
CareCall: a Call-Based Active Monitoring Dialog Agent for Managing COVID-19 Pandemic
Sang-Woo Lee, Hyunhoon Jung, SukHyun Ko +17
Tracking suspected cases of COVID-19 is crucial to suppressing the spread of COVID-19 pandemic. Active monitoring and proactive inspection are indispensable to mitigate COVID-19 sp…