Papers by Jun Fan
Magnet: Multi-turn Tool-use Data Synthesis and Distillation via Graph Translation (2025.acl-long)
Copied to clipboard
Fan Yin, Zifeng Wang, I-Hung Hsu, Jun Yan, Ke Jiang, Yanfei Chen, Jindong Gu, Long Le, Kai-Wei Chang, Chen-Yu Lee, Hamid Palangi, Tomas Pfister
| Challenge: | Large language models have been shown to be effective in multi-turn interactions . however, their performance may be limited in complex, multi-turned interactions involving users and multiple tools. |
| Approach: | They propose a framework for synthesizing high-quality training trajectories to enhance the function calling capability of large language model agents in multi-turn conversations with humans. |
| Outcome: | The proposed model outperforms the teacher model by 68.01 on BFCL-v3 and 73.30 on ToolQuery. |
MAPS: Motivation-Aware Personalized Search via LLM-Driven Consultation Alignment (2025.acl-long)
Copied to clipboard
| Challenge: | Existing personalized product search methods assume that users’ query fully captures their real motivation, but in practice, user's queries do not always articulate the requirements. |
| Approach: | They propose a Motivation-Aware Personalized Search method that embeds queries and consultations into a unified semantic space via LLMs and utilizes a Mixture of Attention Experts (MoAE) to prioritize critical semantics. |
| Outcome: | Extensive experiments on real and synthetic data show that the proposed method outperforms existing methods in retrieval and ranking tasks. |
CoMoL: Efficient Mixture of LoRA Experts via Dynamic Core Space Merging (2026.findings-acl)
Copied to clipboard
Jie Cao, Zhenxuan Fan, Zhuonan Wang, Tianwei Lin, Ziyuan Zhao, Rolan Yan, Wenqiao Zhang, Feifei Shao, Hongwei Wang, Jun Xiao, Siliang Tang
| Challenge: | Existing PEFT methods suffer from limited parameter efficiency and coarse-grained adaptation due to proliferation of LoRA experts and instance-level routing. |
| Approach: | They propose a new MoE-LoRA framework that incorporates expert diversity, parameter efficiency, and fine-grained adaptation. |
| Outcome: | The proposed framework outperforms existing methods on multiple tasks while maintaining parameter efficiency. |
ReFL: Reflective Feedback Learning for Hallucination Detection of Large Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for detecting hallucinations depend on external knowledge sources, incurring high computational costs and limiting real-time applicability, or extract the model’s internal states, leading to poor generalization. |
| Approach: | They propose a hallucination detection framework that leverages corrective in-context learning to guide LLMs to recognize their own prediction errors and adjust internal representations, critically without updating model weights. |
| Outcome: | The proposed framework outperforms existing methods on two benchmark datasets and achieves state-of-the-art performance. |
Better Simultaneous Translation with Monotonic Knowledge Distillation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to train offline MT models require generating target tokens before source sentence is fully consumed. |
| Approach: | They propose a method that leverages traditional translation models as teachers to generate monotonic yet accurate reference translations for sequence-level knowledge distillation. |
| Outcome: | The proposed approach improves on strong baselines and on a monotonic version of the WMT15 De-En test set. |
Where to Attack: A Dynamic Locator Model for Backdoor Attack in Text Classifications (2022.coling-1)
Copied to clipboard
| Challenge: | BackDoor Attack (BDA) study aims to train a poisoned model with clean data and some trigger-embedded instances to perform normally on normal inputs. |
| Approach: | They propose to train a poisoned model with clean and poisonest inputs . they propose to use triggers to predict those poisonets as target labels . |
| Outcome: | The proposed model can predict P2P dynamically without human intervention. |
kFolden: k-Fold Ensemble for Out-Of-Distribution Detection (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies studying OOD detection in NLP often rely on external data to diversify model predictions. |
| Approach: | They propose a framework which mimics OOD detection behavior without external data . they take text classification as an archetype and compare them to existing datasets . |
| Outcome: | The proposed framework can resolve in- and out-distribution examples in a natural way using existing datasets. |
Exploiting Contextual Knowledge in LLMs through 𝒱-usable Information based Layer Enhancement (2025.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to enhance the context-faithfulness of Large Language Models (LLMs) ignore the fundamental mechanism of how contextual information is processed within LLMs’ internal states. |
| Approach: | They propose a method that enhances the utilization of contextual knowledge within LLMs’ internal representations by employing V-usable information analysis. |
| Outcome: | The proposed method improves context-faithfulness generation in Question-Answering tasks, particularly in scenarios involving unknown or conflicting contextual knowledge. |
Interleaved Tool-Call Reasoning for Protein Function Understanding (2026.acl-long)
Copied to clipboard
| Challenge: | Recent advances in large language models have highlighted the effectiveness of chain-of-thought reasoning in symbolic domains such as mathematics and programming. |
| Approach: | They propose a tool-augmented protein reasoning agent that unifies problem decomposition, tool invocation, and grounded answer generation. |
| Outcome: | The proposed protein function understanding agent outperforms text-only reasoning models with an average performance improvement of 103%. |
Domain-Lifelong Learning for Dialogue State Tracking via Knowledge Preservation Networks (2021.emnlp-main)
Copied to clipboard
Qingbin Liu, Pengfei Cao, Cao Liu, Jiansong Chen, Xunliang Cai, Fan Yang, Shizhu He, Kang Liu, Jun Zhao
| Challenge: | Existing offline DST models require a fixed dataset to train . Existing domain-lifelong learning methods are impractical in real-world applications . |
| Approach: | They propose a domain-lifelong learning method to continuously train a DST model on new data to learn incessantly emerging new domains while avoiding catastrophically forgetting old learned domains. |
| Outcome: | The proposed method outperforms state-of-the-art lifelong learning methods by 4.25% and 8.27% on the MultiWOZ and the SGD benchmarks. |
Leveraging AMR Graph Structure for Better Sequence-to-Sequence AMR Parsing (2024.lrec-main)
Copied to clipboard
| Challenge: | Recent studies on AMR parsing often regard this task as a seq2seq translation problem. |
| Approach: | They propose to translate AMR graphs into AMR token sequences in pre-processing and recover AMR from sequences after decoding. |
| Outcome: | The proposed approach outperforms baseline and achieves 85.5 0.1 and 84.2 0.2 Smatch scores on AMR 2.0 and AMR 3.0. |
On the Step Length Confounding in LLM Reasoning Data Selection (2026.findings-acl)
Copied to clipboard
Bing Wang, Rui Miao, Chen Shen, Shaotian Yan, Kaiyuan Liu, Ximing Li, Xiaosong Yuan, Sinan Fan, Jun Zhang, Jieping Ye
| Challenge: | Existing pipelines generate long reasoning data from more capable Large Language Models (LLMs) and apply manually heuristic or naturalness-based selection methods to filter high-quality samples. |
| Approach: | They propose to use supervised fine-tuning to generate long reasoning data from more capable Large Language Models and apply manually heuristic or naturalness-based selection methods to filter high-quality samples. |
| Outcome: | Experiments on four LLMs and five evaluation benchmarks show that the proposed approach is effective in mitigating step length confounding problem. |
LoPT: Lossless Parallel Tokenization Acceleration for Long Context Inference of Large Language Model (2026.acl-long)
Copied to clipboard
| Challenge: | Existing parallel tokenization methods suffer from inconsistent results due to boundary artifacts that occur after merging. |
| Approach: | They propose a Lossless Parallel Tokenization framework that ensures output identical to standard sequential tokenization. |
| Outcome: | The proposed method achieves significant speedup while guaranteeing lossless tokenization. |
Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment (2026.acl-long)
Copied to clipboard
Yuming Yang, Mingyoung Lai, Wanxu Zhao, Xiaoran Fan, Zhiheng Xi, Mingqi Wu, Chiyue Huang, Jun Zhao, Haijun Lv, Jian Tong, Yunhua Zhou, Yicheng Zou, Qipeng Guo, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing methods assess suitability primarily through student likelihood, favoring trajectories that align closely with the student model’s current behavior but overlooking more informative ones. |
| Approach: | They propose a Rank–Surprisal Ratio metric that captures both alignment and informativeness to assess the suitability of a reasoning trajectory. |
| Outcome: | The proposed metric captures both alignment and informativeness to assess the suitability of a reasoning trajectory. |
Learning to Control the Specificity in Neural Response Generation (P18-1)
Copied to clipboard
| Challenge: | Existing generative conversational models tend to favor general and trivial responses which appear frequently. |
| Approach: | They propose a controlled response generation mechanism to handle different utterance-response relationships in terms of specificity. |
| Outcome: | The proposed model outperforms state-of-the-art models under automatic and human evaluations. |
Competency-Aware Neural Machine Translation: Can Machine Translation Know its Own Translation Quality? (2022.emnlp-main)
Copied to clipboard
| Challenge: | Neural machine translation models are often criticized for failures that happen without competency awareness. |
| Approach: | They propose a method that extends conventional NMT with a self-estimator to translate a source sentence and estimate its competency. |
| Outcome: | The proposed method performs on translation tasks intact and on quality estimation tasks better than existing methods. |
PILE: Pairwise Iterative Logits Ensemble for Multi-Teacher Labeled Distillation (2022.emnlp-industry)
Copied to clipboard
Lianshang Cai, Linhao Zhang, Dehong Ma, Jun Fan, Daiting Shi, Yi Wu, Zhicong Cheng, Simiu Gu, Dawei Yin
| Challenge: | Pre-trained language models have been a key part of ranking systems . knowledge distillation is widely used to maintain high performance while keeping efficient computations. |
| Approach: | They propose an algorithm to combine knowledge from multi-teachers and label information to achieve competitive performance in offline and online experiments. |
| Outcome: | The proposed method has been deployed in a real-world commercial search system. |
RTCFake: Speech Deepfake Detection in Real-Time Communication (2026.findings-acl)
Copied to clipboard
Jun Xue, Zhuolin Yi, Yihuan Huang, Yanzhen Ren, Yujie Chen, Cunhang Fan, Zicheng Su, Yongcheng Zhang, Bo Cai
| Challenge: | Existing detection studies focus on offline simulations and struggle to cope with complex distortions introduced during RTC transmission. |
| Approach: | They propose a large-scale speech deepfake dataset tailored for RTC scenarios . the dataset is constructed by transmitting speech through multiple social media and conferencing platforms . |
| Outcome: | The proposed dataset is constructed by transmitting speech through multiple mainstream social media and conferencing platforms, enabling precise pairing between offline and online speech. |
UEGP: Unified Expert-Guided Pre-training for Knowledge Rekindle (2024.findings-naacl)
Copied to clipboard
Yutao Mou, Kexiang Wang, Jianhe Lin, Dehong Ma, Jun Fan, Daiting Shi, Zhicong Cheng, Gu Simiu, Dawei Yin, Weiran Xu
| Challenge: | Existing paradigms for pre-training and fine-tuning have limitations . knowledge rekindle aims to break through performance upper bounds of experts without introducing additional annotated data. |
| Approach: | They propose a new paradigm for pre-training and fine-tuning that aims to re-incorporate the fine- tuned expert model into the training cycle and break through performance upper bounds of experts. |
| Outcome: | The proposed model breaks through performance upper bounds of experts without additional annotated data. |
Similarity = Value? Consultation Value-Assessment and Alignment for Personalized Search (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods rely on semantic similarity to align historical consultations with current queries due to the absence of ‘value’ labels, but this lacks exploration of needs in user consultations. |
| Approach: | They propose a consultation value assessment framework that evaluates historical consultations from three novel perspectives: (1) Scenario Scope Value, (2) Posterior Action Value, and (3) Time Decay Value. |
| Outcome: | The proposed model outperforms baselines on public and commercial datasets on both retrieval and ranking tasks. |