Papers by Gaeun Kim
StablePrompt : Automatic Prompt Tuning using Reinforcement Learning for Large Language Model (2024.emnlp-main)
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| Challenge: | Recent advances in large language models have made it difficult to find appropriate prompts for tasks with multiple input-output formats. |
| Approach: | They propose a prompt tuning method based on reinforcement learning (RL) they propose an anchor model and an extension for generating input-dependent prompts. |
| Outcome: | The proposed method outperforms existing methods on a variety of tasks and achieves State-of-the-art performance across diverse types and sizes of LLMs. |
Deep Context- and Relation-Aware Learning for Aspect-based Sentiment Analysis (2021.acl-short)
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| Challenge: | Existing methods for aspect-based sentiment analysis (ABSA) consider relationships implicitly among subtasks at the word level. |
| Approach: | They propose a deep contextualized relation-aware network that allows interactive relations among subtasks . they propose self-supervised strategies that deal with multiple aspects . |
| Outcome: | The proposed method outperforms state-of-the-art methods on three widely used benchmarks. |