Papers by Jiacheng Yao
From Isolated Scoring to Collaborative Ranking: A Comparison-Native Framework for LLM-Based Paper Evaluation (2026.findings-acl)
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Pujun Zheng, Jiacheng Yao, Jinquan Zheng, Chenyang Gu, Guoxiu He, Jiawei Liu, Yong Huang, Tianrui Guo, Wei Lu
| Challenge: | Large language models (LLMs) are currently used to evaluate scientific papers by assigning an absolute score to each paper independently. |
| Approach: | They propose a comparison-native framework for paper evaluation that integrates comparison into both data construction and model learning. |
| Outcome: | The proposed framework achieves an average relative improvement of 21.8% over the strong baseline DeepReview-14B, while exhibiting robust generalization to five previously unseen datasets. |
Mitigating Selection Bias in Large Language Models via Permutation-Aware GRPO (2026.acl-long)
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| Challenge: | Existing inference-time debiasing ignores that the same question should yield consistent answers across permutations. |
| Approach: | They propose a permutation-aware group-relative policy optimization which enforces permutations-consistent semantic reasoning. |
| Outcome: | The proposed model outperforms strong baselines across seven benchmarks while maintaining high overall performance. |
How Do LLMs Acquire New Knowledge? A Knowledge Circuits Perspective on Continual Pre-Training (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have exceptional capabilities in knowledge-intensive tasks . however, they struggle with knowledge updates due to dynamic nature of world knowledge . |
| Approach: | They propose to identify computational subgraphs that facilitate knowledge storage and processing . they also identify a phase shift from formation to optimization in LLMs . |
| Outcome: | The proposed model can capture factual knowledge from pre-training corpus and encapsulate it as extensive parametric knowledge. |
Implicit Relation Linking for Question Answering over Knowledge Graph (2022.findings-acl)
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| Challenge: | Existing methods rely on textual similarities between NL and KG to build relation links. |
| Approach: | They propose an implicit relation linking method called ImRL which links relation phrases in NL to relation paths in KG. |
| Outcome: | The proposed method significantly outperforms state-of-the-art methods on two benchmarks and a newly-created datasets. |
ARES: Adaptive Red-Teaming and End-to-End Repair of Policy-Reward System (2026.acl-long)
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Jiacheng Liang, Yao Ma, Tharindu Kumarage, Satyapriya Krishna, Rahul Gupta, Kai-Wei Chang, Aram Galstyan, Charith Peris
| Challenge: | Existing red-teaming approaches focus on policy-level weaknesses, but they overlook systemic weaknesses . aRES exploits dual-targeting weaknesses in both the core LLM and the RM simultaneously. |
| Approach: | a new framework uncovers weaknesses in both the core and the reward models simultaneously . a "Safety Mentor" generates semantically coherent adversarial prompts . |
| Outcome: | ARES uncovers weaknesses in both the core LLM and the RM simultaneously . it fine-tunes the LM to detect harmful content, then optimizes the core model . |
Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models (2025.acl-long)
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| Challenge: | Current methods for improving large language models rely on splitting long contexts into fixed-length chunks, compromising accuracy. |
| Approach: | They propose a method for dynamically separating and selecting chunks of long context, facilitating a more streamlined input for LLMs. |
| Outcome: | The proposed approach outperforms baseline methods on single-hop and multi-hop question-answering benchmarks. |