Papers by Tianle Gu
Adaptive Spatial and Temporal Redundancy Optimization for Efficient Reasoning in Large Language Models (2026.acl-long)
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Tianle Chen, Pengyu Cheng, Qiyuan Zhu, Jiacheng Wang, Bei Liu, Hao Gu, Ruijie Shen, Xiaofeng Hou, Sirui Han, Jiacheng Liu
| Challenge: | Existing research to improve CoT efficiency falls into three categories, each with distinct limitations. |
| Approach: | They propose a training-free framework that addresses both dimensions of CoT reasoning by applying a progressive precision reduction strategy coupled with an entropy-based confidence mechanism for adaptive termination. |
| Outcome: | Empirical results show that the proposed framework achieves 11.3 efficiency gain without compromising accuracy. |
MorphMark: Flexible Adaptive Watermarking for Large Language Models (2025.acl-long)
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| Challenge: | Existing methods for tracing text origins struggle with a watermark effectiveness dilemma . weaker watermarks preserve text quality, while stronger ones enhance effectiveness . |
| Approach: | They propose a method that adjusts watermark strength in response to changes in a key factor . they first formalize the problem within a multi-objective trade-off analysis framework . |
| Outcome: | The proposed method improves watermark effectiveness but reduces text quality . the proposed method prioritizes flexibility and time and space efficiency . |
Benchmarking Large Language Models Under Data Contamination: A Survey from Static to Dynamic Evaluation (2025.emnlp-main)
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Simin Chen, Yiming Chen, Zexin Li, Yifan Jiang, Zhongwei Wan, Yixin He, Dezhi Ran, Tianle Gu, Haizhou Li, Tao Xie, Baishakhi Ray
| Challenge: | In the era of evaluating large language models, data contamination is an increasingly prominent concern . static benchmarking has been used for evaluation, but there are limitations of *dynamic* benchmarks . |
| Approach: | They propose a series of optimal design principles for *dynamic* benchmarking and analyze the limitations of existing *static* benchmarks. |
| Outcome: | The proposed benchmarks highlight a critical gap in the evaluation of LLMs. |
Invisible Entropy: Towards Safe and Efficient Low-Entropy LLM Watermarking (2025.emnlp-main)
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| Challenge: | Existing methods to watermark low-entropy content are expensive and risky . IE reduces parameter size by 99% while achieving performance on par with state-of-the-art methods . |
| Approach: | They propose a logit-based watermarking paradigm that uses entropy-based features to predict whether the next token is high or low. |
| Outcome: | The proposed method reduces parameter size by 99% while achieving performance on par with state-of-the-art methods. |
Probing the Safety Robustness of LLMs in Latent Space (2026.acl-long)
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Tianle Gu, Kexin Huang, Zongqi Wang, Yixu Wang, Jie Li, Xin Wang, Yang Yao, Yujiu Yang, Yan Teng, Yingchun Wang
| Challenge: | Despite substantial progress in safety alignment techniques, aligned large language models can still produce unsafe responses under minor internal perturbations. |
| Approach: | They introduce Activation Steering Attack (ASA) and leverage the Negative Log-Likelihood (NLL) as a diagnostic signal to probe the local sensitivity of safety behaviors in latent space. |
| Outcome: | The proposed method is model-agnostic and supervision-free, enabling a general and reproducible diagnostic metric for analyzing safety robustness. |
A Cognitive Writing Perspective for Constrained Long-Form Text Generation (2025.findings-acl)
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| Challenge: | Large Language Models struggle to generate high-quality long-form text in a single pass . a new framework that trains LLMs to write human-like writing capabilities is needed . |
| Approach: | They propose a framework that equips large language models with human-like cognitive writing capabilities . they use a planning agent and multiple Generation Agents to generate long-form text in parallel . |
| Outcome: | CogWriter surpasses GPT-4o by 22% in complex instruction completion accuracy . the framework can generate coherent text in a single pass with fluency that rivals human writers . |
From Evasion to Concealment: Stealthy Knowledge Unlearning for LLMs (2025.findings-acl)
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| Challenge: | Existing approaches to unlearning often treat nonsensical responses or template-based refusals as the unlearning target, making the process even more vulnerable to attacks and jailbreaks. |
| Approach: | They propose a method that uses inverted facts to remove the need for auxiliary models or retaining data while avoiding leakage. |
| Outcome: | Evaluated on the ToFU Knowledge Unlearning dataset using Llama2-7B-Chat and Phi-1.5, MEOW outperforms baselines in forgetting quality while preserving model utility. |
SCAN: Structured Capability Assessment and Navigation for LLMs (2026.acl-long)
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| Challenge: | Existing research has focused on approximating model rankings, but such benchmarks fail to provide users and developers with a comprehensive and fine-grained understanding of a specific model’s capabilities. |
| Approach: | They propose a framework that enables detailed characterization of LLM capabilities through comprehensive and fine-grained evaluation. |
| Outcome: | The proposed framework enables detailed characterization of large language models through comprehensive and fine-grained evaluation. |
ESC-Eval: Evaluating Emotion Support Conversations in Large Language Models (2024.emnlp-main)
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Haiquan Zhao, Lingyu Li, Shisong Chen, Shuqi Kong, Jiaan Wang, Kexin Huang, Tianle Gu, Yixu Wang, Jian Wang, Liang Dandan, Zhixu Li, Yan Teng, Yanghua Xiao, Yingchun Wang
| Challenge: | Emotion Support Conversation (ESC) is a crucial application for reducing stress and providing emotional guidance. |
| Approach: | They re-organize 2,801 role-playing cards to define roles of role-players . they train a specific role- playing model called ESC-Role which behaves more like a confused person than GPT-4 . |
| Outcome: | The proposed model behaves more like a confused person than GPT-4, and the model performs better than GPLs. |
CoLLiE: Collaborative Training of Large Language Models in an Efficient Way (2023.emnlp-demo)
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Kai Lv, Shuo Zhang, Tianle Gu, Shuhao Xing, Jiawei Hong, Keyu Chen, Xiaoran Liu, Yuqing Yang, Honglin Guo, Tengxiao Liu, Yu Sun, Qipeng Guo, Hang Yan, Xipeng Qiu
| Challenge: | Large language models (LLMs) are increasingly pivotal in a wide range of tasks . however, the resources required for training these models necessitate efficient solutions . |
| Approach: | They propose a library that facilitates collaborative training of large language models . they use 3D parallelism, parameter-efficient fine-tuning methods and optimizers . |
| Outcome: | The proposed library has proven superior training efficiency in comparison with prevalent solutions in pre-training and fine-tuning scenarios. |
Few-shot In-context Learning on Knowledge Base Question Answering (2023.acl-long)
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| Challenge: | KB-BINDER enables few-shot in-context learning over knowledge base questions . KBQA is a difficult problem due to the heterogeneity of knowledge bases . |
| Approach: | They propose a framework that enables few-shot in-context learning over KBQA tasks. |
| Outcome: | The proposed framework can outperform state-of-the-art models on GraphQA and MetaQA datasets. |
Word Form Matters: LLMs’ Semantic Reconstruction under Typoglycemia (2025.findings-acl)
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| Challenge: | Typoglycemia is a phenomenon where people can read words even when the middle letters of the words are scrambled. |
| Approach: | They propose a reliable metric to quantify the degree of semantic reconstruction and validate its effectiveness. |
| Outcome: | The proposed metric quantifies the degree of semantic reconstruction and validates its effectiveness. |
Fair Text-Attributed Graph Representation Learning (2025.findings-emnlp)
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| Challenge: | Text-Attributed Graphs (TAGs) inherit issues from Graph Neural Networks such as fairness. |
| Approach: | They propose to evolve LM-as-encoder to LM as-fair-encoding process to explore fairness in TAGRL. |
| Outcome: | The proposed process can be integrated with fairness-enhancing strategies on the GNNs decoder side. |