Papers by Bo Feng

Beyond Linguistic Cues: Fine-grained Conversational Emotion Recognition via Belief-Desire Modelling (2024.lrec-main)

Challenge:	Existing frameworks that increase context window do not guarantee robust performance across long input tasks.
Approach:	They propose a framework that enables language models to handle extended inputs within limited context windows efficiently.
Outcome:	The framework improves performance on long-document question-answering and Needle-in-a-Haystack tasks.

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Bo Xu, Longjiao Li, Wei Luo, Mehdi Naseriparsa, Zhehuan Zhao, Hongfei Lin, Feng Xia

Adaptive Backtracking for Privacy Protection in Large Language Models (2026.findings-acl)

Challenge:	Emotion recognition in conversation (ERC) is essential for dialogue systems to identify the emotions expressed by speakers.
Approach:	They propose a method that incorporates both belief and desire to accurately identify emotions by extracting emotion-eliciting events from utterances and construct graphs that represent beliefs and desires in conversations.
Outcome:	The proposed model outperforms existing models on four popular ERC datasets and validates its performance with multiple state-of-the-art models.

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Zhihao Yao, Yuxuan Gu, Xiachong Feng, Weitao Ma, Bo Li, Xiaocheng Feng, Bing Qin

Putting Captions to the Test: Evaluating Video Caption Quality through Multiple-Choice Question Answering (2026.acl-long)

Challenge:	Existing privacy protection methods are prone to privacy leakage, but they are not effective in ensuring the privacy of users.
Approach:	They propose to capture latent leakage tendency of large language models during generation process and to construct a new benchmark for personal information.
Outcome:	The proposed method improves privacy by up to 14% over strong baselines against adversarial attacks, avoiding the degradation of response utility.

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Zizhen Wang, Bo Feng, Zhengfeng Lai, Shiyu Li, Yang Lu, Meng Cao, Ping Huang, Xiaoming Simon Wang

Leveraging Key Information Modeling to Improve Less-Data Constrained News Headline Generation via Duality Fine-Tuning (2022.aacl-main)

Challenge:	Existing metrics for video captioning are based on text-based comparisons with ground-truth references.
Approach:	They propose a reference-free benchmark that assesses video captions based on their utility . they will release the benchmark to facilitate reproducible research .
Outcome:	The proposed benchmark improves on human-verified, fine-grained questions . it correlates significantly better with human judgments than existing metrics .

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Zhuoxuan Jiang, Lingfeng Qiao, Di Yin, Shanshan Feng, Bo Ren

Text Generation

Challenge:	Recent language generative models are mostly trained on large-scale datasets, while in some real scenarios, the training datasets are often expensive and would be small-scale.
Approach:	They propose a novel duality fine-tuning method to capture more information from limited data and build connections between tasks.
Outcome:	The proposed method can capture more information from limited data, build connections between separate tasks, and is suitable for less-data constrained generation tasks.

SPD-Faith Bench: Diagnosing and Improving Faithfulness in Chain-of-Thought for Multimodal Large Language Models (2026.findings-acl)

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Weijiang Lv, Yaoxuan Feng, Xiaobo Xia, Jiayu Wang, Yan Jing, Wenchao Chen, Bo Chen

Continual Dialogue State Tracking via Reason-of-Select Distillation (2024.findings-acl)

Challenge:	Existing studies on multimodal faithfulness have focused on perceptual hallucinations, raising concerns about the validity of reasoning traces.
Approach:	They propose a diagnostic benchmark that enforces explicit visual comparison to assess faithfulness of reasoning traces.
Outcome:	The proposed framework improves visual routing and aligns reasoning with perception.

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Yujie Feng, Bo Liu, Xiaoyu Dong, Zexin Lu, Li-Ming Zhan, Xiao-Ming Wu, Albert Lam

Dialogue Systems & Conversational Agents

Challenge:	Existing research on dialogue systems has focused on domain-specific offline systems lacking adaptation abilities.
Approach:	They propose a Reason-of-Select distillation method that enhances smaller models with a novel "meta-reasoning" capability.
Outcome:	Experiments show that the proposed method significantly improves the performance and generalization capabilities of existing models.

AdaSwitch: Adaptive Switching between Small and Large Agents for Effective Cloud-Local Collaborative Learning (2024.emnlp-main)

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Hao Sun, Jiayi Wu, Hengyi Cai, Xiaochi Wei, Yue Feng, Bo Wang, Shuaiqiang Wang, Yan Zhang, Dawei Yin

Attribution-Based Analysis and Optimization of Modular Agentic Workflows (2026.findings-acl)

Challenge:	Recent advances in large language models have been remarkable . users face a choice between using cloud-based LLMs for generation quality or local-based ones for lower computational cost .
Approach:	They propose a new LLM utilization paradigm that facilitates collaborative operation . they evaluate AdaSwitch across 7 benchmarks and compare it to other LLMs .
Outcome:	The proposed model improves performance of local and cloud agents across 7 benchmarks . it achieves competitive results compared to the cloud agent while utilizing less computational overhead.

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Yingxuan Yang, Bo Huang, Siyuan Qi, Chao Feng, Haoyi Hu, Yuxuan Zhu, Jinbo Hu, Haoran Zhao, Ziyi He, Xiao Liu, ZongYu Wang, Muning Wen, Lin Qiu, Xuezhi Cao, Xunliang Cai, Yong Yu, Weinan Zhang

Challenge:	Large Language Models (LLMs) have driven the rise of agentic workflows . yet, how can we attribute performance gains to individual upgrades and their interactions?
Approach:	They propose a game-theoretic framework that models component upgrades as players and evaluates component coalitions to compute Shapley values.
Outcome:	The proposed framework provides interaction-aware attribution and recommendation for model allocation under a fixed workflow structure.

EvoNarrator: Modeling Scientific Evolution for Feasible Hypothesis Generation (2026.acl-long)

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Xiaoying Le, Pengfei Qian, Yuanzhao Zhai, Xu Zhang, Qian Liu, Feng Dawei, Bo Ding

Challenge:	Scientific discovery evolution does not occur ex nihilo but is characterized by structural deepening and reconfiguration of existing functionalities.
Approach:	They propose a framework for hypothesis generation based on evolutionary narratives . they extract structured P-M-L-F quadruples from citation networks and introduce a mechanism to assess their semantic compatibility.
Outcome:	The proposed framework reduces logical disconnects by evaluating its semantic compatibility.

C2PO: Diagnosing and Disentangling Bias Shortcuts in LLMs (2026.findings-acl)

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Xuan Feng, Bo An, Tianlong Gu, Liang Chang, Fengrui Hao, Peipeng Yu, Shuai Zhao

SURVEYFORGE : On the Outline Heuristics, Memory-Driven Generation, and Multi-dimensional Evaluation for Automated Survey Writing (2025.acl-long)

Challenge:	Existing approaches to solve large language models address stereotypical and structural biases in isolation . however, prior paradigms address these in isolation, often at the expense of exacerbating the other .
Approach:	They propose a framework to tackle latent spurious feature correlations within input that drive erroneous reasoning shortcuts.
Outcome:	The proposed framework mitigates stereotypical and structural biases while preserving robust general reasoning capabilities.

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Xiangchao Yan, Shiyang Feng, Jiakang Yuan, Renqiu Xia, Bin Wang, Lei Bai, Bo Zhang

Information Retrieval

Challenge:	SURVEYFORGE automates survey paper writing, but quality gap between LLM-generated and human-written surveys remains significant.
Approach:	They propose a survey tool that automatically generates and refines human-written surveys.
Outcome:	Experiments show that SURVEYFORGE outperforms previous work such as AutoSurvey in outline quality and content quality.

AgentOCR: Reimagining Agent History via Optical Self-Compression (2026.acl-long)

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Lang Feng, Fuchao Yang, Feng Chen, Xin Cheng, Haiyang Xu, Zhenglin Wan, Ming Yan, Bo An

Visual Data in NLP

Challenge:	Recent advances in large language models (LLMs) have enabled agentic systems trained with reinforcement learning over multi-turn interaction, but practical deployment is bottlenecked by rapidly growing textual histories that inflate token and memory costs.
Approach:	They propose a framework that represents the accumulated observation-action history as a compact rendered image.
Outcome:	The proposed framework preserves over 95% of text-based agent performance while significantly reducing token consumption (>50%), yielding consistent token and memory efficiency.

BayesFlow: A Probability Inference Framework for Meta-Agent Assisted Workflow Generation (2026.findings-eacl)

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Bo Yuan, Yun Zhou, Zhichao Xu, Kiran Ramnath, Aosong Feng, Balasubramaniam Srinivasan

Challenge:	Large Language Models (LLMs) have demonstrated remarkable generality, often solving tasks with a single carefully engineered prompt.
Approach:	They propose to cast automatic workflow generation as Bayesian inference over a posterior distribution on workflows and instantiate BayesFlow as Bayer-based workflow generation framework.
Outcome:	The proposed framework improves accuracy by 9 percentage points over baselines and 65 percentage points on pool-wide benchmarks.

IGenBench: Benchmarking the Reliability of Text-to-Infographic Generation (2026.acl-long)

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Yinghao Tang, Xueding Liu, Boyuan Zhang, Tingfeng Lan, Yupeng Xie, Jiale Lao, Yiyao Wang, Haoxuan Li, Tingting Gao, Bo Pan, Luoxuan Weng, Xiuqi Huang, Minfeng Zhu, Yingchaojie Feng, Yuyu Luo, Wei Chen

Good Arguments Against the People Pleasers: How Reasoning Mitigates (Yet Masks) LLM Sycophancy (2026.acl-long)

Challenge:	Generated infographics may appear correct at first glance but contain easily overlooked issues, such as distorted data encoding or incorrect textual content.
Approach:	They propose to evaluate reliability of text-to-infographic generation using IGenBench . they employ multimodal large language models to verify each question .
Outcome:	The proposed framework decomposes reliability verification into atomic yes/no questions based on a taxonomy of 10 question types.

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Zhaoxin Feng, Zheng Chen, Jianfei Ma, Yip Tin Po, Emmanuele Chersoni, Bo Li

Towards LLM-driven Dialogue State Tracking (2023.emnlp-main)

Challenge:	Recent studies have identified a critical drawback of aligning models with human judgments and outputs that are flawed or incorrect.
Approach:	They evaluate a range of LLMs to examine whether CoT reasoning mitigates sycophancy . they find that reasoning masks a tendency to scophage in some cases .
Outcome:	The proposed model models show that CoT reasoning reduces sycophancy but masks it in some cases.

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Yujie Feng, Zexin Lu, Bo Liu, Liming Zhan, Xiao-Ming Wu

Dialogue Systems & Conversational Agents

Challenge:	emergence of large language models (LLMs) such as GPT3 and ChatGPT has sparked considerable interest in assessing their efficacy across diverse applications.
Approach:	They present a framework for a domain-slot instruction tuning method that allows LDST to achieve performance on par with ChatGPT.
Outcome:	The proposed framework performs better in zero-shot and few-shot settings than previous SOTA methods.

FlowSearch: Advancing Deep Research with Dynamic Structured Knowledge Flow (2026.acl-long)

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Yusong Hu, Runmin Ma, Yue Fan, Jinxin Shi, Zongsheng Cao, Yuhao Zhou, Jiakang Yuan, Shuaiyu Zhang, Shiyang Feng, Xiangchao Yan, Shufei Zhang, Wenlong Zhang, Lei Bai, Bo Zhang

Rose-SQL: Role-State Evolution Guided Structured Reasoning for Multi-Turn Text-to-SQL (2026.acl-long)

Challenge:	FlowSearch is a multi-agent framework that actively constructs and evolves a dynamic structured knowledge flow to drive subtask execution and reasoning.
Approach:	They propose a multi-agent framework that actively constructs and evolves a dynamic structured knowledge flow to drive subtask execution and reasoning.
Outcome:	The proposed framework achieves competitive performance on GAIA, HLE, GPQA and TRQA benchmarks and is available to download.

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Le Zhou, Feng Yao, Fengcai Qiao, Bo Xu, Fangyuan Wang, Boyan Xu

Programming Languages in NLP

Challenge:	Existing approaches to multi-turn Text-to-SQL tasks rely on unstable APIs or expensive fine-tuning.
Approach:	They propose a training-free framework that leverages small-scale LRMs through in-context learning to enable accurate context-dependent parsing.
Outcome:	The proposed framework outperforms in-context learning baselines at the 4B scale and surpasses state-of-the-art models at the 8B and 14B scales.

Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets (N18-1)

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Zhen Yang, Wei Chen, Feng Wang, Bo Xu

Text Generation

Challenge:	Experimental results show that the proposed model consistently outperforms the traditional RNNSearch and the newly emerged state-of-the-art Transformer on English-German and Chinese-English translation tasks.
Approach:	They propose an approach for applying GANs to NMT by building a conditional sequence generative adversarial net with two adversarials.
Outcome:	The proposed model outperforms the existing RNNSearch and Transformer on English-German and Chinese-English translation tasks.

Improving Retrospective Language Agents via Joint Policy Gradient Optimization (2025.naacl-long)

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Xueyang Feng, Bo Lan, Quanyu Dai, Lei Wang, Jiakai Tang, Xu Chen, Zhenhua Dong, Ji-Rong Wen

MGPO: Thinking with Images via Multi-Turn Grounding-Based Reinforcement Learning (2026.findings-acl)

Challenge:	Recent advances in large language models have sparked interest in creating autonomous agents.
Approach:	They propose a framework that jointly optimizes both task-planning and self-reflective evolution capabilities in language agents.
Outcome:	The proposed framework improves task planning and self-reflective evolution capabilities in language agents.

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Xinyu Huang, Yuhao Dong, Weiwei Tian, Bo Li, Rui Feng, Ziwei Liu

Boosting Event Extraction with Denoised Structure-to-Text Augmentation (2023.findings-acl)

Challenge:	State-of-the-art large multimodal models face challenges when processing high-resolution images, as these inputs are converted into enormous visual tokens, many of which are irrelevant to the downstream task.
Approach:	They propose a multi-turn grounding-based policy optimization framework that enables LMMs to iteratively focus on key visual regions by automatically cropping sub-images based on model-predicted grounding coordinates within a multiple-turn conversation framework.
Outcome:	The proposed framework improves on Qwen2.5-VL-7B with 21K samples and surpasses OpenAI’s o1 and GPT-4o models on the out-of-distribution (OOD) V* Bench.

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Bo Wang, Heyan Huang, Xiaochi Wei, Ge Shi, Xiao Liu, Chong Feng, Tong Zhou, Shuaiqiang Wang, Dawei Yin

Information Extraction & Text Mining

Challenge:	Existing methods for event extraction neglect grammatical incorrectness, structure misalignment, and semantic drifting . et al., 2004; Ahn, 2006) show that the proposed method generates more diverse text representations for event extracting compared with the state-of-the-art.
Approach:	They propose a framework for event extraction that generates additional training data and iteratively selects the effective subset from the generated training data.
Outcome:	The proposed method generates more diverse representations of training data and achieves comparable results with the state-of-the-art.

Semi-Supervised Disfluency Detection (C18-1)

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Feng Wang, Wei Chen, Zhen Yang, Qianqian Dong, Shuang Xu, Bo Xu

TaSL: Continual Dialog State Tracking via Task Skill Localization and Consolidation (2024.acl-long)

Challenge:	Detecting disfluency can be difficult because of the flexible nature of reparandum structure and the lack of a nested structure.
Approach:	They propose a semi-supervised approach which extracts hidden features from self-attention without any Recurrent Neural Network (RNN) or Convolutional Neural Net (CNN).
Outcome:	The proposed approach improves over baselines by using unlabelled data . identifying and removing non-fluent factors would help to improve spontaneous speech quality .

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Yujie Feng, Xu Chu, Yongxin Xu, Guangyuan Shi, Bo Liu, Xiao-Ming Wu

Dialogue Systems & Conversational Agents

Challenge:	Current methods for Continual Dialogue State Tracking (DST) struggle with catastrophic forgetting and knowledge transfer between tasks.
Approach:	They propose a framework for task skill localization and consolidation that enables effective knowledge transfer without relying on memory replay.
Outcome:	The proposed framework shows a 7.6% increase in Avg. JGA and 11% rise in BWT metrics over existing state-of-the-art methods.

Cascaded Mutual Modulation for Visual Reasoning (D18-1)

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Yiqun Yao, Jiaming Xu, Feng Wang, Bo Xu

CRYPTOGRU: Low Latency Privacy-Preserving Text Analysis With GRU (2021.emnlp-main)

Challenge:	Visual reasoning is a multi-step and compositional problem that requires intensive text-vision interactions.
Approach:	They propose a visual reasoning model that uses a feature-wise linear modulation technique to enable textual/visual pipelines to mutually control each other.
Outcome:	The proposed model outperforms existing models on visual reasoning benchmarks CLEVR and NLVR . it can generate a textual answer to a visual question answering problem with images .

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Bo Feng, Qian Lou, Lei Jiang, Geoffrey Fox

How Good Are LLMs at Out-of-Distribution Detection? (2024.lrec-main)

Challenge:	Existing approaches to combine HE and GC in RNNs suffer from long inference latency due to the slow activation functions.
Approach:	They propose a hybrid structure of HE and GC gated recurrent unit network, for low-latency secure inferences.
Outcome:	The proposed structure improves the secure inference latency by up to 138 over one of the state-of-the-art secure networks on the Penn Treebank dataset.

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Bo Liu, Li-Ming Zhan, Zexin Lu, Yujie Feng, Lei Xue, Xiao-Ming Wu

MTRouter: Cost-Aware Multi-Turn LLM Routing with History–Model Joint Embeddings (2026.acl-long)

Challenge:	Out-of-distribution (OOD) detection is crucial for ensuring AI safety . large language models (LLMs) are becoming more prevalent due to their scale, pre-training objectives, and paradigms used for inference.
Approach:	They propose to use large language models to investigate out-of-distribution (OOD) detection in machine learning.
Outcome:	The proposed method outperforms other OOD detectors in zero-grad and fine-tuning scenarios.

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Yiqun Zhang, Hao Li, Zihan Wang, Shi Feng, Xiaocui Yang, Daling Wang, Bo Zhang, Lei Bai, Shuyue Hu

Semantic Text Processing

Challenge:	Multi-turn, long-horizon tasks require dozens of sequential model calls per episode.
Approach:	They propose a cost-aware multi-turn LLM routing tool which encodes interaction history and candidate models into joint history–model embeddings and learns an outcome estimator from logged trajectories to predict turn-level model utility.
Outcome:	The proposed model reduces cost and performance by 58.7% on ScienceWorld and on Humanity’s Last Exam (HLE) and even reduces costs for held-out tasks.

QRMeM: Unleash the Length Limitation through Question then Reflection Memory Mechanism (2024.findings-emnlp)

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Bo Wang, Heyan Huang, Yixin Cao, Jiahao Ying, Wei Tang, Chong Feng

Question Answering

Challenge:	Existing methods for processing large textual content face insufficient adaptation to task-specific needs and missing multi-segmentation relationships.
Approach:	They propose a question then reflection memory mechanism which integrates a dual-structured memory pool and a structured graph guidance to facilitate a reflective trial-and-error approach for navigating and identifying relevant segments.
Outcome:	The proposed model achieves superior performance on multiple-choice questions and multi-doc QA.

CARE-STaR: Constraint-aware Self-taught Reasoner (2025.findings-acl)

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Zhiliang Li, Bo Tang, Yijun Niu, Beihong Jin, Qiwen Shi, Yuchen Feng, Zhiyu Li, Jie Hu, Mingchuan Yang, Feiyu Xiong

Reasoning

Challenge:	Recent research on instruction following has demonstrated that LLMs can handle complex instructions.
Approach:	They propose to assign constraints to different levels of constraints in instructions . they use chain-of-thought and self-taught reasoner methods to identify constraints .
Outcome:	The proposed method outperforms supervised fine-tuning (SFT) on three instruction-following benchmarks.

Mitigating the Discrepancy Between Video and Text Temporal Sequences: A Time-Perception Enhanced Video Grounding method for LLM (2025.coling-main)

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Xuefen Li, Bo Wang, Ge Shi, Chong Feng, Jiahao Teng