Papers by Yunlong Feng
A Two-Stage Framework with Self-Supervised Distillation for Cross-Domain Text Classification (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing work on cross-domain text classification relies on domain-invariant features or task-agnostic features. |
| Approach: | They propose a two-stage framework for cross-domain text classification that leverages or reuses rich labeled data from the source domain and unlabeled data in the target domain. |
| Outcome: | The proposed framework achieves state-of-the-art on a public cross-domain text classification benchmark. |
OpenSLU: A Unified, Modularized, and Extensible Toolkit for Spoken Language Understanding (2023.acl-demo)
Copied to clipboard
| Challenge: | Spoken Language Understanding (SLU) is a task-oriented dialogue system . open-source toolkit provides a unified, modularized, and extensible toolkit for SLU . |
| Approach: | They introduce an open-source toolkit to provide a unified toolkit for spoken language understanding. |
| Outcome: | The proposed toolkit unifies 10 models for both single-intent and multi-intention scenarios. |
Beyond Static Evaluation: A Dynamic Approach to Assessing AI Assistants’ API Invocation Capabilities (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing evaluation methods for human-machine interactions are static and can be misleading. |
| Approach: | They propose to use a LLM-based user agent to assess an assistant's API call capability without human involvement. |
| Outcome: | The proposed method mirrors real human conversation patterns in human-machine interactions, and shows that it aligns more closely with human assessment. |
Stealthy Jailbreak Attacks on Large Language Models via Benign Data Mirroring (2025.naacl-long)
Copied to clipboard
Honglin Mu, Han He, Yuxin Zhou, Yunlong Feng, Yang Xu, Libo Qin, Xiaoming Shi, Zeming Liu, Xudong Han, Qi Shi, Qingfu Zhu, Wanxiang Che
| Challenge: | Existing black-box jailbreak methods often rely on model feedback . existing methods may be intercepted by content moderators during the search process . |
| Approach: | They propose a method that guides malicious prompt construction by local training a mirror model of the target black-box model through benign data distillation. |
| Outcome: | The proposed method achieves a 92% attack success rate and 80% stealth rate on a subset of AdvBench. |
SCALAR: Scientific Citation-based Live Assessment of Long-context Academic Reasoning (2026.eacl-long)
Copied to clipboard
| Challenge: | Long-context understanding is a critical capability for large language models . evaluating this capability requires extensive human annotation, which is time-consuming and costly. |
| Approach: | They propose a benchmark to assess citation-grounded long-context reasoning in academic writing. |
| Outcome: | The proposed benchmark compares state-of-the-art models with human experts on two tasks . human experts achieve 90% accuracy, but most models struggle with the cloze-style task . |
Concise and Precise Context Compression for Tool-Using Language Models (2024.findings-acl)
Copied to clipboard
Yang Xu, Yunlong Feng, Honglin Mu, Yutai Hou, Yitong Li, Xinghao Wang, Wanjun Zhong, Zhongyang Li, Dandan Tu, Qingfu Zhu, Min Zhang, Wanxiang Che
| Challenge: | Existing methods suffer from key information loss and difficulty in adjusting the length of compressed sequences based on documentation lengths. |
| Approach: | They propose two strategies for compressing tool documentation into concise and precise summary sequences for tool-using language models. |
| Outcome: | The proposed approach achieves comparable performance to the upper-bound baseline under 16x compression ratio. |
N-LTP: An Open-source Neural Language Technology Platform for Chinese (2021.emnlp-demo)
Copied to clipboard
| Challenge: | Existing tools that teach an independent model for each task are not supported in Chinese. |
| Approach: | They propose an open-source neural language platform supporting six Chinese NLP tasks . source code, documentation, and pre-trained models are available at https://github.com/hit-SCIR/ltp . |
| Outcome: | The proposed platform supports six Chinese NLP tasks. |
Can Large Language Models Understand You Better? An MBTI Personality Detection Dataset Aligned with Population Traits (2025.coling-main)
Copied to clipboard
Bohan Li, Jiannan Guan, Longxu Dou, Yunlong Feng, Dingzirui Wang, Yang Xu, Enbo Wang, Qiguang Chen, Bichen Wang, Xiao Xu, Yimeng Zhang, Libo Qin, Yanyan Zhao, Qingfu Zhu, Wanxiang Che
| Challenge: | Existing data on MBTI personality detection are based on self-reported labels and fail to capture the full range of population personality traits. |
| Approach: | They construct a manually annotated MBTI personality detection dataset with soft labels under the guidance of psychologists and use them to identify the task. |
| Outcome: | The MBTIBench is the first manually annotated MBti personality detection dataset with soft labels under the guidance of psychologists. |
Self-Constructed Context Decompilation with Fined-grained Alignment Enhancement (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Decompilation is the process of converting compiled code back into a high-level programming language for analysis when source code is unavailable. |
| Approach: | They propose two methods to improve decompilation performance without fine-tuning and fine-grained alignment enhancement to achieve further improvements. |
| Outcome: | The proposed methods achieved a Re-Executability performance improvement of approximately 3.90% on the Decompile-Eval benchmark, establishing a new state-of-the-art performance of 52.41%. |
Improving Language Model Reasoning with Self-motivated Learning (2024.lrec-main)
Copied to clipboard
| Challenge: | Large-scale high-quality training data is important for improving the performance of models. |
| Approach: | They propose a framework that motivates the model to automatically generate rationales on existing datasets and improves the performance of reasoning through reinforcement learning. |
| Outcome: | The proposed model outperforms InstructGPT on multiple reasoning datasets and outperformed InstructGPT on other datasets. |
ProxyQA: An Alternative Framework for Evaluating Long-Form Text Generation with Large Language Models (2024.acl-long)
Copied to clipboard
Haochen Tan, Zhijiang Guo, Zhan Shi, Lu Xu, Zhili Liu, Yunlong Feng, Xiaoguang Li, Yasheng Wang, Lifeng Shang, Qun Liu, Linqi Song
| Challenge: | Existing evaluation methods for large language models are labor-intensive and lack efficiency. |
| Approach: | They propose a framework dedicated to assessing long-text generation that includes in-depth human-curated meta-questions spanning various domains . they use a set of proxy-quests with pre-annotated answers to assess the content's quality by incorporating the generated texts as contextual background. |
| Outcome: | The proposed framework assesses the quality of long-text content by matching it with references through human evaluation or automated metrics. |
Schema-Guided User Satisfaction Modeling for Task-Oriented Dialogues (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies on USM neglect explicitly modeling the user’s task goals fulfillment using the task schema. |
| Approach: | They propose a schema-guided user satisfaction modeling framework that explicitly models the degree to which the user’s preferences regarding task attributes are fulfilled by the system. |
| Outcome: | The proposed framework outperforms existing methods on benchmark datasets and shows that it can interpret and scale well with unseen tasks and can work in low-resource settings. |