Papers by Lingfeng Zhang
Session-level Language Modeling for Conversational Speech (D18-1)
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| Challenge: | Xiong et al., 2017) generalizes language models for conversational speech recognition . recurrent neural networks (RNNs) read a list of words sequentially and predict the next word at each position. |
| Approach: | They propose to generalize language models for conversational speech recognition to capture conversation-level phenomena such as adjacency pairs, lexical entrainment, and topical coherence. |
| Outcome: | The proposed model reduces perplexity and improves word error rate over standard models in the conversational telephone speech domain. |
WebUncertainty: Dual-Level Uncertainty Driven Planning and Reasoning For Autonomous Web Agent (2026.findings-acl)
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| Challenge: | Existing web agents struggle with complex tasks due to rigid planning strategies and hallucination-prone reasoning. |
| Approach: | They propose a task-uncertainty-driven Adaptive Planning Mechanism that adaptively selects planning modes to navigate unknown environments. |
| Outcome: | The proposed framework performs better on the WebArena and WebVoyager benchmarks than existing frameworks. |
RolePlot: A Systematic Framework for Evaluating and Enhancing the Plot-Progression Capabilities of Role-Playing Agents (2025.acl-long)
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| Challenge: | Existing research has focused on role-playing agents’ ability to portray specified characters, but their ability to advance the plot requires substantial improvements to deliver more engaging interaction. |
| Approach: | They propose a role-playing framework to evaluate and enhance the plot-progression capabilities of role-players. |
| Outcome: | The proposed framework improves RPAs’ ability to time plot developments and yields a significant increase in conversation turns and sustained higher arousal levels. |
MapNav: A Novel Memory Representation via Annotated Semantic Maps for VLM-based Vision-and-Language Navigation (2025.acl-long)
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Lingfeng Zhang, Xiaoshuai Hao, Qinwen Xu, Qiang Zhang, Xinyao Zhang, Pengwei Wang, Jing Zhang, Zhongyuan Wang, Shanghang Zhang, Renjing Xu
| Challenge: | Vision-language navigation (VLN) is a key task in Embodied AI . traditional approaches rely on historical observations as spatio-temporal contexts for decision making . |
| Approach: | They propose a vision-language navigation model that leverages an annotation system to replace historical frames. |
| Outcome: | The proposed model can be used as a new memory representation method in vision-language navigation . it can be applied to simulated and real-world environments, and it is validated by experiments . |
Sequential-NIAH: A Needle-In-A-Haystack Benchmark for Extracting Sequential Needles from Long Contexts (2025.emnlp-main)
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Yifei Yu, Qian-Wen Zhang, Lingfeng Qiao, Di Yin, Fang Li, Jie Wang, Chen Zeng Xi, Suncong Zheng, Xiaolong Liang, Xing Sun
| Challenge: | Recent models have extended Corresponding Author. context lengths to millions of tokens while maintaining reasoning and comprehension capabilities. |
| Approach: | They propose a benchmark to evaluate the ability of large language models to extract sequential information items from long contexts. |
| Outcome: | The proposed model achieves maximum accuracy of 63.50% on six well-known LLMs. |
VisFinEval: A Scenario-Driven Chinese Multimodal Benchmark for Holistic Financial Understanding (2025.emnlp-main)
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Zhaowei Liu, Xin Guo, Haotian Xia, Lingfeng Zeng, Fangqi Lou, Jinyi Niu, Mengping Li, Qi Qi, Jiahuan Li, Wei Zhang, Yinglong Wang, Weige Cai, Weining Shen, Liwen Zhang
| Challenge: | Existing benchmarks focus on text comprehension, but MLLMs lack the ability to integrate visual data over financial visuals. |
| Approach: | They evaluate 21 state-of-the-art multimodal large language models in a zero-shot setting . they use an annotated question–answer pair from eight common financial image modalities . |
| Outcome: | The new benchmark outperforms existing models but trailed financial experts by 14 percentage points. |
Xinference: Making Large Model Serving Easy (2024.emnlp-demo)
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| Challenge: | Open-source large models are rapidly catching up with the closed-source models . however, many current inference tools are not as simple and convenient to use. |
| Approach: | They develop an open-source library to simplify the deployment and management of large models. |
| Outcome: | The proposed library outperforms open-source models and offers high throughput and low latency. |
NavA3: Understanding Any Instruction, Navigating Anywhere, Finding Anything (2026.acl-long)
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Lingfeng Zhang, Xiaoshuai Hao, Yingbo Tang, Haoxiang Fu, Xinyu Zheng, Pengwei Wang, Zhongyuan Wang, Wenbo Ding, Shanghang Zhang
| Challenge: | Existing embodied navigation methods struggle with such tasks due to their limitations in comprehending high-level human instructions and localizing objects with an open vocabulary. |
| Approach: | They propose a hierarchical framework for long-horizon navigation that integrates human instructions with 3D scene views. |
| Outcome: | The proposed model achieves SOTA results and can complete long-horizon navigation tasks across different robot embodiments in real-world environments. |
The Language Barrier: Dissecting Safety Challenges of LLMs in Multilingual Contexts (2024.findings-acl)
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Lingfeng Shen, Weiting Tan, Sihao Chen, Yunmo Chen, Jingyu Zhang, Haoran Xu, Boyuan Zheng, Philipp Koehn, Daniel Khashabi
| Challenge: | Recent studies show that malicious prompt instructions could solicit objectionable content from LLMs. |
| Approach: | They compare how state-of-the-art LLMs respond to malicious prompts in different languages . they find that LLM's generate unsafe responses more often when a prompt is written in a lower-resource language . |
| Outcome: | The proposed model can generate unsafe responses more often when a malicious prompt is written in a lower-resource language, and less irrelevant responses when written in lower-source languages. |
SemStamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation (2024.naacl-long)
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Abe Hou, Jingyu Zhang, Tianxing He, Yichen Wang, Yung-Sung Chuang, Hongwei Wang, Lingfeng Shen, Benjamin Van Durme, Daniel Khashabi, Yulia Tsvetkov
| Challenge: | Existing watermarked generation algorithms employ token-level designs and are vulnerable to paraphrase attacks. |
| Approach: | They propose a sentence-level watermarking algorithm that uses locality-sensitive hashing to partition the semantic space of sentences. |
| Outcome: | The proposed algorithm is more robust than the existing state-of-the-art method on paraphrasers and domains, while posing only minor degradations to SemStamp. |