Papers by Ling Zhong
Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Models (2026.findings-acl)
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| Challenge: | Recent research indicates that Large Reasoning Models suffer from a strategic bottleneck at reasoning path planning. |
| Approach: | They propose a framework that reformulates reasoning as a dynamic search for the optimal thinking strategy. |
| Outcome: | The proposed framework improves accuracy and computational cost while reducing generation length by over 22%. |
DOSE: Data Selection for Multi-Modal LLMs via Off-the-Shelf Models (2026.findings-acl)
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| Challenge: | Existing data filtering methods are expensive because they are trained on the same data they are meant to screen. |
| Approach: | They propose to use off-the-shelf pretrained models that have never seen the target data to select training samples for larger and stronger multimodal models without task-specific training. |
| Outcome: | The proposed method can achieve comparable or even better results than those trained on the full dataset in standard VQA and math benchmarks. |
Spiral of Silence in Large Language Model Agents (2025.findings-emnlp)
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| Challenge: | Existing theories of Spiral of Silence do not apply to large language models . |
| Approach: | They propose an evaluation framework for examining SoS in large language models . they consider four controlled conditions that vary the availability of "History" and "Persona" signals . |
| Outcome: | The proposed framework examines the SoS-like dynamics in large language models . it shows that history and persona together produce strong majority dominance . |
Think-Search-Patch: A Retrieval-Augmented Reasoning Framework for Repository-Level Code Repair (2025.emnlp-industry)
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Bojian Xiong, Yikun Lei, Xikai Liu, Shaowei Zhang, Pengyun Zhu, Yan Liu, Yongqi Leng, Ling Shi, Meizhi Zhong, Yurong Zhang, Yan Gao, null Yiwu, Yao Hu, Deyi Xiong
| Challenge: | Large language models suffer from multiple-file coding scenarios with strong inter-file dependencies . experimental results show that large language models exhibit inadequate performance in multi-file scenarios . |
| Approach: | They propose a retrieval-augmented reasoning framework for repository-level code repair . they use a dataset to generate standardized patches based on the key snippets . |
| Outcome: | The proposed framework improves retrieval accuracy and repair success on SWE-bench Lite . it surpasses models with larger size in managing extensive code contexts and fixing bugs spanning across multiple files. |
RouterEval: A Comprehensive Benchmark for Routing LLMs to Explore Model-level Scaling Up in LLMs (2025.findings-emnlp)
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| Challenge: | a lack of comprehensive benchmarks for Routing large language models has hindered the development of routers. |
| Approach: | They propose a router-based benchmark to evaluate Routing large language models . the benchmark includes performance records for 12 popular LLM evaluations . |
| Outcome: | The proposed model-level scaling up phenomenon can surpass the best single model in the pool and many existing strong LLMs. |
Efficient Knowledge Infusion via KG-LLM Alignment (2024.findings-acl)
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| Challenge: | Existing methods for knowledge infusion face knowledge mismatch and poor information compliance of LLMs with knowledge graphs. |
| Approach: | They propose a three-stage alignment strategy to enhance the LLM's capability to utilize information from knowledge graphs. |
| Outcome: | The proposed method outperforms baselines on biomedical question-answering datasets and outperformed existing methods. |
MiniLongBench: The Low-cost Long Context Understanding Benchmark for Large Language Models (2025.acl-long)
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| Challenge: | Existing LCU benchmarks for large language models often result in prohibitively high evaluation costs . existing benchmarks exhibit significant redundancy, which means inefficiency in evaluation . |
| Approach: | They propose a data compression method tailored for long-text data with sparse information characteristics. |
| Outcome: | The proposed method reduces evaluation costs to 4.5% of the long-text benchmark LongBench . the proposed method is based on a long-term LCU benchmark with sparse information characteristics . |