Papers by Fengran Mo
RAG-Studio: Towards In-Domain Adaptation of Retrieval Augmented Generation Through Self-Alignment (2024.findings-emnlp)
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| Challenge: | Existing RAG systems that use pre-trained LLMs and retrievers often fail in specialized domains and applications. |
| Approach: | They propose a self-aligned training framework that adapts general RAG models to specific domains solely through synthetic data. |
| Outcome: | Experiments on specialized domain corpus, general LLM, and general retriever show that the self-aligned training framework outperforms human-annotated training data in specialized fields. |
A User-Centric Multi-Intent Benchmark for Evaluating Large Language Models (2024.emnlp-main)
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| Challenge: | Existing benchmarks focus on specific predefined model abilities, such as world knowledge, reasoning, etc., making it difficult for users to determine which LLM best suits their particular needs. |
| Approach: | They propose to evaluate large language models from a user-centric perspective and use real-world use cases to identify their effectiveness under distinct intents. |
| Outcome: | The proposed benchmarks achieve a correlation between human preference and the user-reported scenarios and human intents. |
MTMCS-Bench: Evaluating Contextual Safety of Multimodal Large Language Models in Multi-Turn Dialogues (2026.findings-acl)
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| Challenge: | Existing contextual safety benchmarks are mostly single-turn and miss how malicious intent can emerge gradually or how the same scene can support both benign and exploitative goals. |
| Approach: | They propose a benchmark that evaluates contextual safety in multimodal large language models . they observe persistent trade-offs between contextual safety and utility . |
| Outcome: | The proposed model combines multi-turn and multi-switch scenarios to evaluate safety in multimodal large language models. |
ReAttn: Improving Attention-based Re-ranking via Attention Re-weighting (2026.findings-eacl)
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| Challenge: | Attention-based re-ranking methods are highly concentrated a small subset of tokens within a few documents, making others indistinguishable. |
| Approach: | They propose a post-hoc re-weighting strategy that uses attention weights to reduce lexical bias and emphasize distinctive terms. |
| Outcome: | The proposed method reduces lexical bias and emphasizes distinctive terms across documents, while maintaining a balanced distribution across informative tokens. |
Instant Personalized Large Language Model Adaptation via Hypernetwork (2026.acl-long)
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Zhaoxuan Tan, Zixuan Zhang, Haoyang Wen, Zheng Li, Rongzhi Zhang, Pei Chen, Fengran Mo, Zheyuan Liu, Qingkai Zeng, Qingyu Yin, Meng Jiang
| Challenge: | Existing parameter-efficient fine-tuning methods require training a separate adapter for each user, making them computationally expensive and impractical for real-time updates. |
| Approach: | They propose a scalable framework that maps a user's profile directly to a full set of adapter parameters. |
| Outcome: | The proposed framework outperforms prompt-based personalization and OPPU while using substantially fewer computational resources at deployment. |
UniConv: Unifying Retrieval and Response Generation for Large Language Models in Conversations (2025.acl-long)
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Fengran Mo, Yifan Gao, Chuan Meng, Xin Liu, Zhuofeng Wu, Kelong Mao, Zhengyang Wang, Pei Chen, Zheng Li, Xian Li, Bing Yin, Meng Jiang
| Challenge: | Existing conversational search systems are usually built with two different models . this separation restricts the system from leveraging the model's intrinsic knowledge simultaneously . Existing studies for developing unified models cannot fully address the aspects of understanding conversational context, managing retrieval independently, and generating responses. |
| Approach: | They propose to unify dense retrieval and response generation for large language models in conversation by fine-tuning and mitigating data discrepancy. |
| Outcome: | The proposed model can outperform existing models on five conversational search datasets and reduce inconsistency risks while mitigating data discrepancy. |
MoqaGPT : Zero-Shot Multi-modal Open-domain Question Answering with Large Language Model (2023.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) like GPT-4 are not able to handle multi-modal open-domain question answering in a zero-shot manner. |
| Approach: | MoqaGPT uses divide-and-conquer strategy to extract answers from each modality separately. |
| Outcome: | MoqaGPT improves on MMCoQA dataset by +37.91 points and EM by +34.07 points. |
ChatRetriever: Adapting Large Language Models for Generalized and Robust Conversational Dense Retrieval (2024.emnlp-main)
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| Challenge: | a conversational search system requires accurate interpretation of user intent from complex multi-turn contexts. |
| Approach: | They propose a dual-learning approach that adapts LLMs for retrieval via contrastive learning while enhancing the complex session understanding through masked instruction tuning. |
| Outcome: | The proposed approach outperforms existing retrieval methods on five conversational search benchmarks. |
A Joint Multiple Criteria Model in Transfer Learning for Cross-domain Chinese Word Segmentation (2020.emnlp-main)
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| Challenge: | Existing methods for word-level segmentation (CWS) for the Chinese language have been successful in large-scale annotated corpora. |
| Approach: | They propose a method that integrates different segmentation criteria into one model . they use a transfer learning method to improve the performance of OOV words . |
| Outcome: | The proposed method achieves state-of-the-art performance on multiple benchmark datasets . it shows a competitive practicability and generalization ability for the CWS task . |
A Dual-View Analysis of Multiple Languages in Colonial Newspapers (2026.findings-acl)
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Zhan Su, Xiaoya Chen, Fengran Mo, Ida L. Vos, Prayag Tiwari, Yazhou Zhang, Qian Zheng, Natália da Silva Perez
| Challenge: | Historical newspapers from the colonial period offer valuable evidence of how racializing language evolved over time. |
| Approach: | They propose a contextual question answering and visual question answering task from colonial newspapers . they propose linguistic training for temporal word embedding with a compass to study racialization . |
| Outcome: | The proposed tasks are limited for low-resource tasks, the authors show . the authors compare the results of two QA pairs from colonial newspapers to a compass . |
Blind Spot Navigation in Large Language Model Reasoning with Thought Space Explorer (2026.findings-eacl)
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Jinghan Zhang, Fengran Mo, Tharindu Cyril Weerasooriya, Xinyue Ye, Dongjie Wang, Yanjie Fu, Kunpeng Liu
| Challenge: | Existing studies show that large language models have strong reasoning capabilities through chain-structured methods. |
| Approach: | They propose a framework for navigating and expanding thought structures to overcome blind spots in LLM reasoning. |
| Outcome: | The proposed framework overcomes blind spots in large language models by expanding thought structures . the proposed framework improves accuracy of the final answer and intermediate reasoning steps . |
Language-Coupled Reinforcement Learning for Multilingual Retrieval-Augmented Generation (2026.findings-acl)
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Rui Qi, Fengran Mo, Yufeng Chen, Xue Zhang, Shuo Wang, Hongliang Li, Xu Jinan, Meng Jiang, Jian-Yun Nie, Kaiyu Huang
| Challenge: | Existing approaches to multilingual retrieval-augmented generation (MRAG) use a single-turn retrieval and subsequent optimization to acquire and integrate beneficial external knowledge from multilingual collections. |
| Approach: | They propose a multilingual search-augmented reinforcement learning framework that integrates a language-coupled Group Relative Policy Optimization into the policy and reward models. |
| Outcome: | The proposed framework achieves competitive performance and is appropriate for various practical scenarios such as constrained training data and retrieval over collections encompassing a large number of languages. |
A Customized Text Sanitization Mechanism with Differential Privacy (2023.findings-acl)
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| Challenge: | Existing methods to sanitize texts subject to differential privacy do not work for non-metric semantic similarity measures. |
| Approach: | They propose a customized text sanitization mechanism based on a metric local differential privacy definition. |
| Outcome: | The proposed mechanism achieves better privacy-utility trade-offs than existing mechanisms on benchmark datasets. |
Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learning (2026.findings-acl)
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Fengran Mo, Yifan Gao, Sha Li, Hansi Zeng, Xin Liu, Zhaoxuan Tan, Xian Li, Jianshu Chen, Dakuo Wang, Meng Jiang
| Challenge: | Existing studies focus on single-turn scenarios, which might lack the ability to handle multi-turn interactions. |
| Approach: | They propose a conversational agent that interleaves search and reasoning across turns and provides tailored rewards towards evolving user goals. |
| Outcome: | The proposed agent interleaves search and reasoning across turns, enabling exploratory and adaptive behaviors learned through reinforcement learning (RL) training with tailored rewards towards evolving user goals. |
Large Language Models Know Your Contextual Search Intent: A Prompting Framework for Conversational Search (2023.findings-emnlp)
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| Challenge: | Existing methods for understanding users’ contextual search intent show unsatisfactory effectiveness and robustness to handle real conversational search scenarios. |
| Approach: | They propose to use large language models to generate multiple query rewrites and hypothetical responses and to aggregate them into an integrated representation that can robustly represent the user’s real contextual search intent. |
| Outcome: | The proposed framework can generate multiple query rewrites and hypothetical responses and can be used to represent the user’s real contextual search intent. |
SoT: Structured-of-Thought Prompting Guides Multilingual Reasoning in Large Language Models (2025.findings-emnlp)
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| Challenge: | Large Language Models struggle with multilingual reasoning tasks due to resource constraints . a training-free method improves performance on multilingual thinking tasks . |
| Approach: | They propose a training-free method that transforms language-specific semantic information into language-agnostic structured representations. |
| Outcome: | The proposed method outperforms strong baselines on multilingual reasoning tasks. |
ConvGQR: Generative Query Reformulation for Conversational Search (2023.acl-long)
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| Challenge: | Existing methods to determine a good search query from the whole conversation context are expensive and often lead to sub-optimal results. |
| Approach: | They propose a framework to reformulate conversational queries based on generative pre-trained language models (PLMs) they propose generative knowledge infusion mechanism to optimize query reformulation and retrieval. |
| Outcome: | Extensive experiments on four conversational search datasets demonstrate the effectiveness of ConvGQR. |
Boosting Data Utilization for Multilingual Dense Retrieval (2025.emnlp-main)
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| Challenge: | Existing studies focus on fine-tuning multilingual dense retrieval models, but data scarcity for low-resource languages makes it difficult to align representations in a shared vector space. |
| Approach: | They propose to obtain high-quality hard negative samples and effective mini-batch data to boost data utilization for multilingual dense retrieval by obtaining high-quality negative samples. |
| Outcome: | The proposed method outperforms existing baselines on a multilingual retrieval benchmark, MIRACL, with 16 languages. |
ConvTrans: Transforming Web Search Sessions for Conversational Dense Retrieval (2022.emnlp-main)
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| Challenge: | Recent studies show that conversational dense retrieval is a promising technique for realizing conversational search, but its implementation is severely hindered by the lack of data. |
| Approach: | They propose a method that transforms easily-accessible web search sessions into conversational search sessions to alleviate the data scarcity problem. |
| Outcome: | The proposed method can transform easily-accessible web search sessions into conversational search sessions. |
Multilingual Collaborative Defense for Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing safeguards for Large Language Models are vulnerable to "jailbreaking" harmful queries. |
| Approach: | They propose a learning method that optimizes a continuous soft safety prompt automatically to facilitate multilingual safeguarding of LLMs. |
| Outcome: | The proposed method outperforms previous approaches in multilingual jailbreak defense while exhibiting strong cross-lingual generalization. |
When Helpers Become Hazards: A Benchmark for Analyzing Multimodal LLM-Powered Safety in Daily Life (2026.findings-acl)
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Xinyue Lou, Xu Jinan, Jingyi Yin, Xiaolong Wang, Zhaolu Kang, null Liaoyouwei, Yixuan Wang, Xiangyu Shi, Fengran Mo, SU Yao, Kaiyu Huang
| Challenge: | Safety impact of Multimodal Large Language Models (MLLMs) on human behavior is evaluated in this study. |
| Approach: | They propose a safety-warning-based evaluation framework that encourages models to provide clear and informative safety warnings, rather than generic refusals. |
| Outcome: | The proposed safety-warning-based evaluation framework encourages models to provide clear and informative safety warnings, rather than generic refusals. |
Entropy-based Exploration Conduction for Multi-step Reasoning (2025.findings-acl)
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| Challenge: | Existing methods to automatically decide the depth of exploration of the reasoning procedure lead to high cost and a lack of flexibility. |
| Approach: | They propose a method that dynamically adjusts the exploration depth during multi-step reasoning by monitoring LLM’s output entropy and variance entropic. |
| Outcome: | The proposed method captures the uncertainty of the current step and the fluctuation of uncertainty across consecutive reasoning steps and then selects whether to deepen, expand, or stop exploration according to the probability. |
DoRA: Enhancing Parameter-Efficient Fine-Tuning with Dynamic Rank Distribution (2024.acl-long)
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| Challenge: | Existing parameter-efficient fine-tuning methods such as Low-Rank Adaptation ignore the differential parameter budget requirements across weight matrices, which may lead to suboptimal fine-uning outcomes. |
| Approach: | They propose a parameter-efficient low-rank Adaptation method that decomposes high-rank LoRA layers into structured single-rank components and allows dynamic pruning of parameter budget . |
| Outcome: | The proposed method outperforms LoRA and LoRA with the same parameter budget and performance. |
WXImpactBench: A Disruptive Weather Impact Understanding Benchmark for Evaluating Large Language Models (2025.findings-acl)
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| Challenge: | Climate change adaptation requires the understanding of disruptive weather impacts on society. |
| Approach: | They propose a large language model to evaluate the capacity of LLMs on disruptive weather impacts by using a four-stage construction pipeline. |
| Outcome: | The proposed model is based on a four-stage well-crafted construction pipeline and requires two evaluation tasks, multi-label classification and ranking-based question answering. |
Search-Oriented Conversational Query Editing (2023.findings-acl)
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| Challenge: | Existing CQR models are not learned toward improving the downstream search performance . existing models generate the rewrite token-by-token from scratch . |
| Approach: | They propose a text editing-based CQR model tailored for conversational search . they propose rewrite tokens are selected from the dialogue in a non-autoregressive fashion . |
| Outcome: | The proposed model outperforms state-of-the-art models on three conversational search benchmarks while having low rewriting latency. |
CHIQ: Contextual History Enhancement for Improving Query Rewriting in Conversational Search (2024.emnlp-main)
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| Challenge: | Recent advances in task-solving capabilities of Large Language Models (LLMs) have motivated researchers to integrate these models into existing conversational search systems. |
| Approach: | They propose a method that leverages the capabilities of large language models to resolve ambiguities in conversation history before query rewriting. |
| Outcome: | The proposed method leads to state-of-the-art results across most settings compared with closed-source LLMs. |
History-Aware Conversational Dense Retrieval (2024.findings-acl)
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| Challenge: | Current approaches for conversational dense retrieval rely on fine-tuning a pre-trained ad-hoc retriever, which can be lengthy and noisy. |
| Approach: | They propose a context-denoised query reformulation and automatic mining of supervision signals based on historical turns. |
| Outcome: | The proposed system improves on two public conversational search datasets. |