Papers by Jiarui Yao
Rethinking Diverse Human Preference Learning through Principal Component Analysis (2025.findings-acl)
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| Challenge: | Decomposed Reward Models extract diverse human preferences from binary comparisons without fine-grained annotations. |
| Approach: | They propose a decomposed reward model that extracts diverse human preferences from binary comparisons without fine-grained annotations. |
| Outcome: | The proposed approach extracts diverse human preferences from binary comparisons without fine-grained annotations. |
Modal Dependency Parsing via Language Model Priming (2022.naacl-main)
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| Challenge: | modal dependency parsing is a task of parse a text into its modal dependence structure . the root node of an MDS is always the author of a document, the ultimate source of information sources . |
| Approach: | They propose a modal dependency parser based on priming pre-trained language models and evaluate it on two data sets. |
| Outcome: | The proposed parser improves on two data sets. |
Factuality Assessment as Modal Dependency Parsing (2021.acl-long)
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| Challenge: | a critical step towards factuality assessment is to determine the factuality of events in text. |
| Approach: | They propose a modal dependency parsing task that assesses the factuality of events in text . they crowdsource a large-scale data set annotated with modal dependence structures . |
| Outcome: | The proposed model outperforms the pipeline model in factuality assessment . the proposed model is based on a crowdsourced dataset . |
MiCRo: Mixture Modeling and Context-aware Routing for Personalized Preference Learning (2025.emnlp-main)
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| Challenge: | Existing reward models assume a global reward function, limiting personalization and pluralistic alignment. |
| Approach: | They propose a framework that leverages binary preference datasets to enhance personalized preference learning. |
| Outcome: | The proposed framework captures diverse human preferences without fine-grained annotations and significantly improves personalized preference learning on downstream tasks. |
EscapeBench: Towards Advancing Creative Intelligence of Language Model Agents (2025.acl-long)
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Cheng Qian, Peixuan Han, Qinyu Luo, Bingxiang He, Xiusi Chen, Yuji Zhang, Hongyi Du, Jiarui Yao, Xiaocheng Yang, Denghui Zhang, Yunzhu Li, Heng Ji
| Challenge: | Existing language model agents excel in planning and reasoning, but lack creativity in unfamiliar environments. |
| Approach: | They propose a benchmark suite of room escape game environments to challenge agents with creative reasoning, unconventional tool use and iterative problem-solving to uncover implicit goals. |
| Outcome: | The proposed framework can perform with 40% fewer steps and hints and performs robustly across difficulty levels. |
Annotating Temporal Dependency Graphs via Crowdsourcing (2020.emnlp-main)
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| Challenge: | Existing temporal annotation schemes have been limited due to the complexity of temporal relations between events. |
| Approach: | They propose to build a corpus of Wikinews articles annotated with temporal dependency graphs . they also propose a crowdsourcing strategy to annotate TDGs based on the corpus . |
| Outcome: | The proposed method achieves a good trade-off between completeness and practicality in temporal annotation. |
FANS: Formal Answer Selection for LLM Natural Language Math Reasoning Using Lean4 (2025.emnlp-main)
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| Challenge: | Existing frameworks that use Lean4 to enhance LLMs' NL reasoning abilities have been controversial in the field of math reasoning. |
| Approach: | They propose a framework that utilizes Lean4 to enhance LLMs’ NL math reasoning ability by generating a Lean 4 theorem statement and a proof-generating LLM. |
| Outcome: | The proposed framework improves LLMs' NL math reasoning ability by 2% across several math benchmarks and higher further based on reward models or in subfields such as algebra and number theory. |