Papers by Mei Si
Towards a Progression-Aware Autonomous Dialogue Agent (2022.naacl-main)
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| Challenge: | Recent advances in large-scale language modeling and generation have enabled the creation of dialogue agents that exhibit human-like responses in a wide range of conversational scenarios. |
| Approach: | They propose a framework in which dialogue agents can evaluate the progression of a conversation toward or away from desired outcomes and use this signal to inform planning for subsequent responses. |
| Outcome: | The proposed framework evaluates the progression of a conversation toward or away from desired outcomes and uses this signal to inform planning for subsequent responses. |
A Corpus for Commonsense Inference in Story Cloze Test (2022.lrec-1)
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| Challenge: | Story Cloze Test (SOTA) models can achieve over 90% accuracy on predicting the last sentence, but high accuracy can be achieved by merely using surface-level features. |
| Approach: | They constructed a human-labeled and human-verified commonsense knowledge inference dataset using data from 1871 stories and three human workers labeled each story. |
| Outcome: | The proposed models can achieve 90% accuracy on predicting the last sentence, but they don't perform well on new and more challenging tasks. |
Reflections & Resonance: Two-Agent Partnership for Advancing LLM-based Story Annotation (2024.lrec-main)
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| Challenge: | Existing methods for story annotation require a meticulous and resourceintensive effort, but the advent of advanced computational tools like GPT-4 can streamline the process and mitigate common limitations. |
| Approach: | They propose a multi-agent system that generates tailored prompts for a large language model and provides feedback to refine the initial prompts. |
| Outcome: | The proposed system significantly improves the model's reconstruction accuracy and confidence, demonstrating that dynamic interaction between agents significantly boosts the annotation process's precision and efficiency. |