Papers by Kyochul Jang
ETHIC: Evaluating Large Language Models on Long-Context Tasks with High Information Coverage (2025.naacl-long)
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| Challenge: | Existing evaluation methods do not assess whether large language models fully utilize contextual information. |
| Approach: | They introduce a new metric to assess LLMs' ability to fully utilize contextual information. |
| Outcome: | The proposed benchmark comprises 1,986 test instances spanning four long-context tasks with high IC scores in the domains of books, debates, medicine, and law. |
DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues (2025.findings-acl)
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| Challenge: | Existing function-calling benchmarks focus on single-turn interactions but ignore complexity of real-world scenarios. |
| Approach: | They propose a framework that constructs practical function-calling datasets by synthesizing conversations through a tool graph that maintains dependencies across rounds. |
| Outcome: | The proposed framework synthesizes conversations through a tool graph that maintains dependencies across rounds and a multi-agent system with distinct personas to enhance dialogue naturalness. |