Papers by Zhuo Jiang
KBM: Delineating Knowledge Boundary for Adaptive Retrieval in Large Language Models (2025.findings-emnlp)
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Zhen Zhang, Xinyu Wang, Yong Jiang, Zile Qiao, Zhuo Chen, Guangyu Li, Feiteng Mu, Mengting Hu, Pengjun Xie, Fei Huang
| Challenge: | Retrieval-augmented generation (RAG) is employed to tackle these challenges . a Knowledge Boundary Model (KBM) is used to express the known/unknown of a given question . |
| Approach: | They propose a Knowledge Boundary Model to express the known/unknown of a given question . they find that not all questions need to trigger RAG to improve performance . |
| Outcome: | The proposed model reduces time and computational costs by retrieving parts of unknown knowledge . the proposed model can express the known/unknown of a given question and determine whether a RAG needs to be triggered . |
MARCH: Multi-Agent Reinforced Check for Hallucination (2026.acl-long)
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Zhuo Li, Yupeng Zhang, Pengyu Cheng, Jiajun Song, Mengyu Zhou, Hao Li, Shujie Hu, Yu Qin, null Erchao.zec, Xiaoxi Jiang, null Guanjunjiang
| Challenge: | Existing methods to detect hallucinations suffer from inherent confirmation bias, where the verifier inadvertently reproduces the errors of the original generation. |
| Approach: | They propose a framework that enforces rigorous factual alignment by leveraging deliberate *information asymmetry* by combining a pipeline of three specialized agents: a Solver, a Proposer, and a Checker. |
| Outcome: | Extensive experiments across hallucination benchmarks demonstrate that MARCH substantially reduces hallucinism rates. |
From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan (2026.acl-long)
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Lei Yang, Leiyu Pan, Bojian Xiong, Renren Jin, Shaowei Zhang, Yue Chen, Ling Shi, Jiang Zhou, Junru Wu, Zhen Wang, Jianxiang Peng, Juesi Xiao, Tianyu Dong, Zhuowen Han, Zhuo Chen, Yuqi Ren, Deyi Xiong
| Challenge: | Large language models have achieved remarkable success across a wide range of tasks, yet their performance remains heavily biased toward high-resource languages. |
| Approach: | They propose a pipeline for advancing Tibetan language modeling through multilingual continual pre-training with Tibetan, Chinese, and English. |
| Outcome: | The proposed model outperforms open-source and Tibetan-focused models on diverse tasks. |
Detecting Knowledge Boundary of Vision Large Language Models by Sampling-Based Inference (2025.emnlp-main)
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| Challenge: | Existing methods to detect the knowledge boundary of Vision Large Language Models (VLLMs) are expensive and require indiscriminate retrieval to address questions that require real-time information or are knowledge-intensive. |
| Approach: | They propose a method that fine-tunes a VLLM on an automatically constructed dataset for boundary identification. |
| Outcome: | The proposed method reduces indiscriminate retrieval while maintaining or improving the performance of a VLLM on an automatically constructed dataset. |
CAME: Confidence-guided Adaptive Memory Efficient Optimization (2023.acl-long)
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| Challenge: | Existing memory-efficient methods require second-moment estimates of the per-parameter gradients to maintain their performance. |
| Approach: | They propose to use memory-efficient optimizers to reduce memory usage by preserving second-moment estimates of gradients. |
| Outcome: | The proposed method achieves fast convergence and lower memory usage across training tasks. |
Using Interpretation Methods for Model Enhancement (2023.emnlp-main)
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| Challenge: | Existing frameworks for enhancing neural models with interpretation methods and gold rationales have not been fully explored. |
| Approach: | They propose a framework for utilizing interpretation methods and gold rationales to enhance neural models. |
| Outcome: | The proposed framework outperforms gradient-based methods in low-resource settings on a variety of tasks. |
Improving Retrieval Augmented Open-Domain Question-Answering with Vectorized Contexts (2024.findings-acl)
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| Challenge: | Retrieval Augmented Generation can be used to process long contexts in Open-Domain Question-Answering tasks. |
| Approach: | They propose a method to cover longer contexts in Open-Domain Question-Answering tasks by using a small encoder language model and cross-attention with origin inputs. |
| Outcome: | The proposed method can cover longer contexts while keeping the computing requirements close to the baseline. |