Papers by Yichao Liu
GIFT: Guided Fine-Tuning and Transfer for Enhancing Instruction-Tuned Language Models (2026.acl-long)
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Zhiwen Ruan, Yichao Du, Jianjie Zheng, Longyue Wang, Yun Chen, Peng Li, Jinsong Su, Yang Liu, Guanhua Chen
| Challenge: | Existing adapter-based transfer methods treat instruction-tuned models as passive targets . direct fine-tuning can disrupt this delicate balance and lead to instability or performance degradation. |
| Approach: | They propose a framework that incorporates instruction-level guidance into task adaptation. |
| Outcome: | The proposed framework outperforms direct fine-tuning and representative transfer-based baselines while maintaining robust generalization and favorable test-time scaling behavior. |
IMTLab: An Open-Source Platform for Building, Evaluating, and Diagnosing Interactive Machine Translation Systems (2023.emnlp-main)
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Xu Huang, Zhirui Zhang, Ruize Gao, Yichao Du, Lemao Liu, Guoping Huang, Shuming Shi, Jiajun Chen, Shujian Huang
| Challenge: | Existing systems that use a left-to-right completion paradigm are inefficient and expensive. |
| Approach: | They propose an open-source end-to-end interactive machine translation system platform . they propose to use a prefix-constrained decoding approach to achieve end- to-end evaluation . |
| Outcome: | The proposed system can guarantee high-quality, error-free translations . it uses prefix-constrained decoding and improves on previous systems . |
Nearest Neighbor Machine Translation is Meta-Optimizer on Output Projection Layer (2023.emnlp-main)
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| Challenge: | Nearest Neighbor Machine Translation (kNN-MT) is a powerful domain adaptation tool . the reasons for its success have not been thoroughly investigated . |
| Approach: | They propose to integrate pre-trained Neural Machine Translation models with token-level retrieval . they propose to implicitly execute gradient descent on the output projection layer of NMT . |
| Outcome: | The proposed approach outperforms model fine-tuning on in-domain tests while achieving better performance on out-of-domain sets. |
DetectRL-X: Towards Reliable Multilingual and Real-World LLM-Generated Text Detection (2026.acl-long)
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Junchao Wu, Yefeng Liu, Chenyu Zhu, Hao Zhang, Zeyu Wu, Tianqi Shi, Yichao Du, Longyue Wang, Weihua Luo, Jinsong Su, Derek F. Wong
| Challenge: | Existing detectors are limited in their ability to detect large language models generated content in multilingual environments. |
| Approach: | They propose a multilingual benchmark to evaluate advanced detectors across 8 dimensions to better align with real-world applications. |
| Outcome: | The proposed benchmark encompasses 8 languages commonly used in commercial contexts and collects human-written texts from 6 domains highly susceptible to LLM misuse. |
Bridging Relevance and Reasoning: Rationale Distillation in Retrieval-Augmented Generation (2025.findings-acl)
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Pengyue Jia, Derong Xu, Xiaopeng Li, Zhaocheng Du, Xiangyang Li, Yichao Wang, Yuhao Wang, Qidong Liu, Maolin Wang, Huifeng Guo, Ruiming Tang, Xiangyu Zhao
| Challenge: | Existing approaches to rerank and align documents based on reasoning capabilities of large language models (LLMs) . prior work shows that LLMs have exceptional reasoning and text generation capabilities . |
| Approach: | They propose a rationale extraction method that leverages reasoning capabilities of large language models to extract the rationales necessary for answering a query. |
| Outcome: | The proposed method is compared with baseline methods on two tasks across three datasets. |
Tree-KG: An Expandable Knowledge Graph Construction Framework for Knowledge-intensive Domains (2025.acl-long)
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| Challenge: | Knowledge graphs are a useful tool for organizing complex data in knowledge-intensive domains. |
| Approach: | They propose an expandable framework that combines structured domain texts with advanced semantic techniques to create a tree-like graph from textbooks. |
| Outcome: | The proposed framework surpasses competing methods in the text-Annotated dataset with high scores on the Text-Annalytated data. |
MemSearch-o1: Empowering Large Language Models with Reasoning-Aligned Memory Growth in Agentic Search (2026.acl-long)
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Sheng Zhang, Junyi Li, Yingyi Zhang, Pengyue Jia, Yichao Wang, Xiaowei Qian, Wenlin Zhang, Maolin Wang, Yong Liu, Xiangyu Zhao
| Challenge: | Existing methods for memory management struggle to capture fine-grained semantic relations between queries and documents. |
| Approach: | They propose a framework for reasoning and agentic search that grows fine-grained memory fragments from seed tokens from queries, then retraces and deep refines the memory via a contribution function. |
| Outcome: | Experiments on eight benchmark datasets show that MemSearch-o1 significantly mitigates memory dilution and more effectively activates reasoning potential of diverse LLMs. |
Interventional Rationalization (2023.emnlp-main)
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| Challenge: | Existing methods for rationalization use spurious correlations in data to compose rationales and make predictions. |
| Approach: | They propose a method to discover the causal rationales by using a structural causal model. |
| Outcome: | The proposed method is based on the causal theory and validates on three real-world datasets. |
Fine-grained Medical Vision-Language Representation Learning for Radiology Report Generation (2023.emnlp-main)
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| Challenge: | Existing methods to learn medical vision-language representations by contrasting images with entire reports are not effective. |
| Approach: | They propose a phenotype-driven medical vision-language representation learning framework to bridge the gap between visual and textual modalities for improved text-oriented generation. |
| Outcome: | The proposed framework bridges the gap between visual and textual modalities for improved radiology report generation. |