Papers by Jonathan Pei
PREADD: Prefix-Adaptive Decoding for Controlled Text Generation (2023.findings-acl)
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| Challenge: | Existing methods for controlling text generation rely on auxiliary expert models but require training data. |
| Approach: | They propose a flexible method for controlled text generation that uses a prefix-prepended prompt instead of an external model. |
| Outcome: | The proposed method outperforms auxiliary-expert methods on three tasks by 12% or more on main metrics for each task. |
SWAG: Storytelling With Action Guidance (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) are used for one-shot creation, but they can produce inconsistent but not necessarily engaging content. |
| Approach: | They propose a novel approach to storytelling with large language models that reduces story writing to a search problem through a two-model feedback loop. |
| Outcome: | The proposed approach outperforms existing methods when evaluated by GPT-4 and through human evaluation. |
Teaching Language Models To Gather Information Proactively (2025.findings-emnlp)
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| Challenge: | Large language models are often defaulted to passive responses or narrow clarifications when faced with incomplete or under-specified prompts. |
| Approach: | They propose a new task paradigm where LLMs must identify gaps in context and strategically elicit implicit user knowledge through targeted questions. |
| Outcome: | The proposed framework outperforms o3-mini on evaluation metrics and human annotators favor clarification questions and final outlines. |