Papers by Chenkai Sun
Measuring the Effect of Influential Messages on Varying Personas (2023.acl-short)
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| Challenge: | a new task estimates the response a persona might have upon seeing a news message . a first benchmark dataset is used to evaluate the performance of the proposed task . |
| Approach: | They propose a task to estimate the response a persona might have upon seeing a news message. |
| Outcome: | The proposed task estimates the response a persona might have upon seeing a news message. |
Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinement (2025.coling-main)
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Chenkai Sun, Ke Yang, Revanth Gangi Reddy, Yi Fung, Hou Pong Chan, Kevin Small, ChengXiang Zhai, Heng Ji
| Challenge: | Existing research has focused on enhancing the retrieval stage and optimizing the representation of the database. |
| Approach: | They propose a framework to improve generalization across task contexts and collaborative refinement to bridge knowledge gaps among users. |
| Outcome: | The proposed framework improves generalization across task contexts and collaborative refinement to bridge knowledge gaps among users. |
Beyond Reactive Safety: Risk-Aware LLM Alignment via Long-Horizon Simulation (2025.findings-acl)
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| Challenge: | Existing alignment methods focus on reactive feedback, where immediate human perception is leveraged to judge sampled model responses as preference data for post-training. |
| Approach: | They propose a proof-of-concept framework that projects how model-generated advice could propagate through societal systems on a macroscopic scale over time, enabling more robust alignment. |
| Outcome: | The proposed framework achieves 20% improvement on existing safety benchmarks and an average win rate exceeding 70% against strong baselines. |
HySPA: Hybrid Span Generation for Scalable Text-to-Graph Extraction (2021.findings-acl)
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| Challenge: | Existing methods to extract information graphs are difficult to scale to datasets with longer input texts because of their secondorder space/time complexities. |
| Approach: | They propose a Hybrid SPan GenerAtor that invertibly maps the information graph to an alternating sequence of nodes and edge types and generates them via a hybrid span decoder. |
| Outcome: | The proposed method outperforms state-of-the-art methods on the ACE05 dataset. |
Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting (2023.emnlp-main)
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| Challenge: | Existing approaches to forecast news media responses have limited exploration of how to best process and utilize these important features. |
| Approach: | They propose a framework that leverages a large language model to induce a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics. |
| Outcome: | The proposed framework surpasses state-of-the-art in experimental evaluations for both zero-shot and supervised settings, demonstrating its effectiveness in response forecasting. |
Incorporating Task-Specific Concept Knowledge into Script Learning (2023.eacl-main)
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| Challenge: | Existing work on Goal-Oriented Scripts ignore usage context and personal preferences . proposed tasks are restrictive and rely on overly simplified assumptions . |
| Approach: | They propose a novel approach to Goal-Oriented Script Completion that uses concept prompting and script-oriented contrastive learning to improve performance. |
| Outcome: | The proposed approach improves on a WikiHow-based dataset. |