Papers by Youngsoo Jang
End-to-End Neural Pipeline for Goal-Oriented Dialogue Systems using GPT-2 (2020.acl-main)
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| Challenge: | End-to-end dialogue systems with monolithic neural architecture are often trained with input-output utterances without taking into account the entire annotations available in the corpus. |
| Approach: | They propose an end-to-end neural architecture for goal-oriented dialogue systems that addresses both challenges . they propose a modular architecture where modules are optimized individually . |
| Outcome: | The proposed system achieved the top position in the human evaluation task . it is based on a neural architecture that can be integrated with external systems . |
Prospector: Improving LLM Agents with Self-Asking and Trajectory Ranking (2024.findings-emnlp)
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Byoungjip Kim, Youngsoo Jang, Lajanugen Logeswaran, Geon-Hyeong Kim, Yu Jin Kim, Honglak Lee, Moontae Lee
| Challenge: | Existing LLMs are limited in their ability to incorporate feedback from an environment. |
| Approach: | They propose an LLM agent that consists of an Actor and a Critic. |
| Outcome: | The proposed agent outperforms existing LLMs on benchmark environments and shows that it can generate diverse trajectories and pick the most rewarding trajectory. |
Semantic Skill Grounding for Embodied Instruction-Following in Cross-Domain Environments (2024.findings-acl)
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| Challenge: | Existing frameworks for grounding pretrained language models as task planners are challenging due to their intricate entanglement with domain knowledge. |
| Approach: | They propose a framework that leverages the hierarchical nature of semantic skills to ground them in different domains. |
| Outcome: | The proposed framework is effective in 300 cross-domain EIF scenarios. |
IRPO: Implicit Policy Regularized Preference Optimization (2026.findings-eacl)
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| Challenge: | Recent DPOs introduce additional hyperparameters, reducing feasibility for LLM fine-tuning. |
| Approach: | They propose an algorithm that regularizes the reward against a reference policy without extra hyperparameters to address suboptimal outcomes. |
| Outcome: | The proposed algorithm outperforms baseline algorithms with the same hyperparameter complexity while maintaining training simplicity. |
Efficiently Learning To Reason or Not to Reason: Root-token Policy Optimization for Adaptive Thinking (2026.acl-long)
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| Challenge: | Large reasoning models (LRMs) externalize explicit reasoning traces before producing the answer, yet suffer from overthinking challenge. |
| Approach: | They propose a framework that enables large reasoning models to self-determine when to reason by training only the initial root token via group relative reward and group-wise advantages. |
| Outcome: | The proposed framework reduces training overhead and VRAM usage by focusing on the root token . it learns difficulty-aware adaptive thinking at just 2% of the training compute of prior methods. |
PyOpenDial: A Python-based Domain-Independent Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules (D19-3)
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| Challenge: | a recent development of spoken dialogue systems has enabled deep learning to achieve state-of-the-art performance. |
| Approach: | They propose a Python-based domain-independent, open-source toolkit for spoken dialogue systems. |
| Outcome: | The proposed toolkit extends OpenDial's Java-based architecture and provides new functions for neural dialogue state tracking and action planning. |