Papers by Luo Ji
When to Trust Tools? Adaptive Tool Trust Calibration For Tool-Integrated Math Reasoning (2026.findings-acl)
Copied to clipboard
| Challenge: | Large reasoning models have performance enhancements but still suffer from shortcomings due to limitations of the underlying language models. |
| Approach: | They propose a framework that allows the model to choose when to trust or ignore the tool results based on the confidence score of generated code blocks. |
| Outcome: | The proposed framework reduces the "Tool Ignored" issue by 4.1% to 7.5%. |
Model Composition for Multimodal Large Language Models (2024.acl-long)
Copied to clipboard
Chi Chen, Yiyang Du, Zheng Fang, Ziyue Wang, Fuwen Luo, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Maosong Sun, Yang Liu
| Challenge: | Existing methods for creating versatile MLLMs rely on joint training with paired instruction data, which is resource-intensive and challenging to extend to new modalities. |
| Approach: | They propose a new paradigm for multimodal large language models by reusing modality encoders and merging LLM parameters. |
| Outcome: | The proposed model retains the modal understanding capabilities of each original model. |
Hashing based Efficient Inference for Image-Text Matching (2021.findings-acl)
Copied to clipboard
| Challenge: | Recent work on image-text matching has focused on exploring interactions between images and sentences to improve performance without considering inference efficiency. |
| Approach: | They propose a hashing-based efficient inference module which can be plugged into existing frameworks to speed up inference step without reducing retrieval performance. |
| Outcome: | The proposed module can be plugged into existing framework to speed up inference step without reducing retrieval performance. |
MMedAgent: Learning to Use Medical Tools with Multi-modal Agent (2024.findings-emnlp)
Copied to clipboard
Binxu Li, Tiankai Yan, Yuanting Pan, Jie Luo, Ruiyang Ji, Jiayuan Ding, Zhe Xu, Shilong Liu, Haoyu Dong, Zihao Lin, Yixin Wang
| Challenge: | Multi-modal Large Language Models (MLLMs) exhibit limited generality and often fall short when compared to specialized models. |
| Approach: | They propose a multi-modal medical agent that picks the most suitable medical tools based on user inputs. |
| Outcome: | The proposed agent performs better than open-source models and the closed-source model, GPT-4o. |
Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentences (2020.acl-main)
Copied to clipboard
| Challenge: | a monolingual speaker can learn to translate by looking up a bilingual dictionary . a novel task of machine translation (MT) is based on no parallel sentences but can refer to a ground-truth bilingual dictionary and large-scale monolingual corpora. |
| Approach: | They propose a task of machine translation that uses a bilingual dictionary and large-scale monolingual corpora to translate a monolingual speaker. |
| Outcome: | The proposed task is based on a bilingual dictionary and large scale monolingual corpora, while being independent on parallel sentences. |
Reaction Miner: An Integrated System for Chemical Reaction Extraction from Textual Data (2023.emnlp-demo)
Copied to clipboard
Ming Zhong, Siru Ouyang, Yizhu Jiao, Priyanka Kargupta, Leo Luo, Yanzhen Shen, Bobby Zhou, Xianrui Zhong, Xuan Liu, Hongxiang Li, Jinfeng Xiao, Minhao Jiang, Vivian Hu, Xuan Wang, Heng Ji, Martin Burke, Huimin Zhao, Jiawei Han
| Challenge: | Reaction Miner is a system designed to extract chemical reactions from raw scientific PDFs. |
| Approach: | They propose a system that extracts chemical reactions directly from raw scientific PDFs. |
| Outcome: | The proposed system can extract chemical reactions from raw scientific PDFs. |
GEM: A General Evaluation Benchmark for Multimodal Tasks (2021.findings-acl)
Copied to clipboard
Lin Su, Nan Duan, Edward Cui, Lei Ji, Chenfei Wu, Huaishao Luo, Yongfei Liu, Ming Zhong, Taroon Bharti, Arun Sacheti
| Challenge: | Existing datasets that focus on natural language tasks are not considered as a general evaluation benchmark for multimodal tasks. |
| Approach: | They present a general evaluation benchmark for multimodal tasks, GEM 1 . they compare it with existing multimodal vision-language datasets . |
| Outcome: | The proposed model is compared with existing vision-language datasets focusing on natural language tasks . it is the largest vision-linguistic dataset covering image-language tasks and video-language task at the same time . |
Browse and Concentrate: Comprehending Multimodal Content via Prior-LLM Context Fusion (2024.acl-long)
Copied to clipboard
Ziyue Wang, Chi Chen, Yiqi Zhu, Fuwen Luo, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Maosong Sun, Yang Liu
| Challenge: | Multimodal Large Language Models (MLLMs) lack understanding of multi-image and interleaved inputs due to the visual features encoded by frozen encoders before being fed into the LLM backbone. |
| Approach: | They propose a two phase paradigm to enable in-depth multimodal context fusion prior to feeding the features into LLMs. |
| Outcome: | The proposed paradigm boosts the performance on 7 multi-image scenarios, contributing to increments on average accuracy by 2.13% and 7.60% against strong MLLMs baselines with 3B and 11B LLMs, respectively. |
Control Image Captioning Spatially and Temporally (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods to generate image captions with user intention are still under exploration. |
| Approach: | They propose a model that connects Contrastive constraints and Attention Guidance in a loop manner and engages explicit spatial and temporal constraints to the generating process. |
| Outcome: | The proposed model improves performance on a trace-controlled image captioning task. |
Convert Language Model into a Value-based Strategic Planner (2025.acl-industry)
Copied to clipboard
| Challenge: | Emotional support conversation (ESC) aims to alleviate the emotional distress of individuals through effective conversations. |
| Approach: | They propose a framework that bootstraps the planning during ESC and determines the optimal strategy based on long-term returns. |
| Outcome: | The proposed framework outperforms baseline models on ESC datasets and can be used to guide the LLM to response. |
EscapeBench: Towards Advancing Creative Intelligence of Language Model Agents (2025.acl-long)
Copied to clipboard
Cheng Qian, Peixuan Han, Qinyu Luo, Bingxiang He, Xiusi Chen, Yuji Zhang, Hongyi Du, Jiarui Yao, Xiaocheng Yang, Denghui Zhang, Yunzhu Li, Heng Ji
| Challenge: | Existing language model agents excel in planning and reasoning, but lack creativity in unfamiliar environments. |
| Approach: | They propose a benchmark suite of room escape game environments to challenge agents with creative reasoning, unconventional tool use and iterative problem-solving to uncover implicit goals. |
| Outcome: | The proposed framework can perform with 40% fewer steps and hints and performs robustly across difficulty levels. |
Resource-Friendly Dynamic Enhancement Chain for Multi-Hop Question Answering (2025.findings-acl)
Copied to clipboard
Binquan Ji, Haibo Luo, YifeiLu YifeiLu, Lei Hei, Jiaqi Wang, Tingjing Liao, Wang Lingyu, Shichao Wang, Feiliang Ren
| Challenge: | Existing approaches to solve multi-hop question answering challenges require multiple rounds of retrieval and iterative generation. |
| Approach: | They propose a framework that decomposes complex questions into coherent subquestions . it then iteratively refines these subquests through context-aware rewriting to generate effective query formulations. |
| Outcome: | The proposed framework performs on par with or surpasses state-of-the-art benchmarks while significantly reducing token consumption. |
BattleAgent: Multi-modal Dynamic Emulation on Historical Battles to Complement Historical Analysis (2024.emnlp-demo)
Copied to clipboard
Shuhang Lin, Wenyue Hua, Lingyao Li, Che-Jui Chang, Lizhou Fan, Jianchao Ji, Hang Hua, Mingyu Jin, Jiebo Luo, Yongfeng Zhang
| Challenge: | Recent advances in large language models have demonstrated impressive reasoning capabilities, indicating their potential to serve as the foundation for agents. |
| Approach: | They propose a detailed emulation system that combines large vision-language model and multi-agent system to emulate dynamic interactions between multiple agents over a period of time. |
| Outcome: | The proposed system combines large vision-language model and multi-agent system to emulate dynamic interactions between agents and their environments over a period of time. |
ActionIE: Action Extraction from Scientific Literature with Programming Languages (2024.acl-long)
Copied to clipboard
Xianrui Zhong, Yufeng Du, Siru Ouyang, Ming Zhong, Tingfeng Luo, Qirong Ho, Hao Peng, Heng Ji, Jiawei Han
| Challenge: | a method that extracts experimental procedures from human language into actionable sequences in robotics language is challenging given the complexity of the instructions and context-dependent nature of the instruction. |
| Approach: | They propose a method that converts actions written in natural language into Python code that can be easily translated into robotics language. |
| Outcome: | The proposed method can extract experimental procedures from human language into actionable sequences in robotics language. |
Dream to Chat: Model-based Reinforcement Learning on Dialogues with User Belief Modeling (2025.findings-emnlp)
Copied to clipboard
Yue Zhao, Xiaoyu Wang, Dan Wang, Zhonglin Jiang, Qingqing Gu, Teng Chen, Ningyuan Xi, Jinxian Qu, Yong Chen, Luo Ji
| Challenge: | a framework for constructing dialogue world models for natural language tasks is currently lacking. |
| Approach: | They propose a framework that can be used to train a dialogue world model. |
| Outcome: | The proposed framework can predict future utterances and user beliefs . it can achieve state-of-the-art performance on emotion classification and sentiment identification . |
Current Agents Fail to Leverage World Model as Tool for Foresight (2026.acl-long)
Copied to clipboard
Cheng Qian, Emre Can Acikgoz, Bingxuan Li, Xiusi Chen, Yuji Zhang, Bingxiang He, Qinyu Luo, Gokhan Tur, Dilek Hakkani-Tür, Yunzhu Li, Heng Ji
| Challenge: | Generative world models could be used to enhance agents' cognition . agents are expected to operate in settings where tasks unfold over long horizons and involve intricate chains of interdependent decisions. |
| Approach: | They propose to use vision-language models as external simulators to enhance cognition . they find that agents rarely invoke simulation and misuse predicted rollouts . |
| Outcome: | The proposed model could be used to predict future states rather than short-horizon reasoning . the model could also be used for real-world planning and robotics . |
Distinguish Before Answer: Generating Contrastive Explanation as Knowledge for Commonsense Question Answering (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing knowledge-enhanced methods have trouble obtaining knowledge from different knowledge bases . a concept-centric model can be used to generate a contrastive explanation for QA tasks . |
| Approach: | They propose a Concept-centric Prompt-bAsed Contrastive Explanation Generation model which converts obtained symbolic knowledge into the contrastive explanation for better distinguishing the differences among given candidates. |
| Outcome: | The proposed model achieves new SOTA on CSQA, QASC, and OBQA. |
GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies ignore aspect terms interaction when labeling polarities . aspect terms extraction and aspect sentiment classification are two fundamental tasks . |
| Approach: | They propose a GRadient hArmonized and CascadEd labeling model to solve the imbalance issue . they extend the gradient harmonized mechanism used in object detection to aspect-based sentiment analysis . |
| Outcome: | The proposed model achieves consistency improvement on multiple benchmark datasets and generates state-of-the-art results. |
Training Long-Context LLMs Efficiently via Chunk-wise Optimization (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in long-context large language models have demonstrated superior retrieval quality compared to retrievalaugmented generation (RAG) approaches. |
| Approach: | They propose a memory-efficient training paradigm that partitions lengthy inputs into manageable chunks. |
| Outcome: | The proposed model expands maximum sequence length from 1K to 16K tokens on a single RTX 3090 GPU, while SpaCO achieves accelerated training speed. |
MUSEG: Reinforcing Video Temporal Understanding via Timestamp-Aware Multi-Segment Grounding (2026.acl-long)
Copied to clipboard
Fuwen Luo, Shengfeng Lou, Chi Chen, Ziyue Wang, Chenliang Li, Weizhou Shen, Jiyue Guo, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Yang Liu
| Challenge: | Existing methods for MLLMs struggle with fine-grained temporal reasoning . despite advances in video understanding, current methods struggle with time-sensitive tasks . |
| Approach: | They propose a time-stamp-aware multi-segment grounding method that enhances temporal understanding by introducing timestamps. |
| Outcome: | The proposed method outperforms existing methods on time-sensitive tasks and generalizes well across diverse temporal understanding scenarios. |
Combining Static Word Embeddings and Contextual Representations for Bilingual Lexicon Induction (2021.findings-acl)
Copied to clipboard
| Challenge: | Bilingual Lexicon Induction (BLI) aims to map words in one language to their translations in another. |
| Approach: | They propose a mechanism to combine static word embeddings and contextual representations to utilize the advantages of both paradigms. |
| Outcome: | The proposed method improves performance on supervised and unsupervised BLI benchmarks on all language pairs by average improving 3.2 points over baselines. |
EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association (2025.acl-long)
Copied to clipboard
Weiqi Wang, Limeng Cui, Xin Liu, Sreyashi Nag, Wenju Xu, Chen Luo, Sheikh Muhammad Sarwar, Yang Li, Hansu Gu, Hui Liu, Changlong Yu, Jiaxin Bai, Yifan Gao, Haiyang Zhang, Qi He, Shuiwang Ji, Yangqiu Song
| Challenge: | Goal-oriented script planning is used by humans to plan for typical activities . however, this capability remains underexplored due to several challenges . |
| Approach: | They propose a framework that enables product-enriched scripts by associating products with each step based on the semantic similarity between the actions and their purchase intentions. |
| Outcome: | The proposed framework can generate product-enriched scripts from 2.4 million scripts . human annotations are conducted to provide gold labels for a sampled subset . |
WebSRC: A Dataset for Web-Based Structural Reading Comprehension (2021.emnlp-main)
Copied to clipboard
| Challenge: | Using a web page and a question, a machine can't understand the contents of web pages. |
| Approach: | They propose a novel dataset for web-based structural reading comprehension that consists of 400K question-answer pairs and a dataset of 6.4K web pages. |
| Outcome: | The proposed dataset consists of 400K question-answer pairs, collected from 6.4K web pages with corresponding HTML source code, screenshots, and metadata. |
Dynamic Emotion and Personality Profiling for Multimodal Deception Detection (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for deception detection lack sample-level dynamic annotations for emotions and personality. |
| Approach: | They propose a multi-model multi-prompt annotation scheme and a strict label quality evaluation standard for deception, emotion, and personality annotations. |
| Outcome: | The proposed framework outperforms state-of-the-art models on the MDPE and DDEP datasets. |
StructBreak: Structural Cognitive Overload-Induced Safety Failures in MLLMs (2026.findings-acl)
Copied to clipboard
| Challenge: | Prior work focused on typographic and pixel-level perturbations, leaving the study of SCO unexplored. |
| Approach: | They propose a framework that exploits MLLMs' diagrammatic reasoning capabilities to bypass safety guardrails. |
| Outcome: | The proposed framework exploits the model's reasoning capabilities to bypass safety guardrails. |
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections (2022.emnlp-main)
Copied to clipboard
Chenliang Li, Haiyang Xu, Junfeng Tian, Wei Wang, Ming Yan, Bin Bi, Jiabo Ye, He Chen, Guohai Xu, Zheng Cao, Ji Zhang, Songfang Huang, Fei Huang, Jingren Zhou, Luo Si
| Challenge: | Existing pre-trained vision-language models suffer from inefficiency and linguistic signal overwhelmed by long visual sequences in cross-modal alignment. |
| Approach: | They propose a vision-language foundation model with cross-modal skip-connections that can be pre-trained end-to-end on large-scale image-text pairs with both discriminative and generative objectives. |
| Outcome: | The proposed model achieves state-of-the-art results on a wide range of vision-language downstream tasks, including image captioning, image-text retrieval, visual grounding and visual question answering. |
Unlocking Instructive In-Context Learning with Tabular Prompting for Relational Triple Extraction (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for relational triple extraction (RTE) are unnatural and recast RTE tasks to text-to-text prompting formats. |
| Approach: | They propose a tabular prompting for RTE which frames RTE task into a table generation task and propose an instructive in-context learning which only selects and annotates samples considering triple semantics in massive unlabeled samples. |
| Outcome: | The proposed prompting for RTE with TableIE achieves state-of-the-art performance compared to other methods . the proposed prompts are based on off-the shelf LLMs and are scalable to multiple scenarios . |
GlotEval: A Test Suite for Massively Multilingual Evaluation of Large Language Models (2025.emnlp-demos)
Copied to clipboard
Hengyu Luo, Zihao Li, Joseph Attieh, Sawal Devkota, Ona de Gibert, Xu Huang, Shaoxiong Ji, Peiqin Lin, Bhavani Sai Praneeth Varma Mantina, Ananda Sreenidhi, Raúl Vázquez, Mengjie Wang, Samea Yusofi, Fei Yuan, Jörg Tiedemann
| Challenge: | Existing evaluation frameworks focus on English and a handful of high-resource languages, thereby overlooking the realistic performance of large language models in multilingual and lower-resourced scenarios. |
| Approach: | They propose a unified and lightweight framework that integrates 27 benchmarks under a standard ISO 639-3 language identifier system to enable seamless incorporation of new benchmarks. |
| Outcome: | The proposed framework integrates 27 benchmarks under a standard ISO 639-3 language identifier system, allowing for seamless incorporation of new benchmarks. |
Data-Centric Continual Pre-training for 500+ Languages: A New Bilingual Translation Corpus and Multilingual Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models pre-trained on massive data have promoted multilingual natural language processing (NLP). |
| Approach: | They construct a bilingual translation corpus with 2,500 language pairs and develop a suite of four models with parallel data. |
| Outcome: | The proposed model suites are evaluated across 7 tasks and 12 benchmarks. |
Reasoning with Graphs: Structuring Implicit Knowledge to Enhance LLMs Reasoning (2025.findings-acl)
Copied to clipboard
Haoyu Han, Yaochen Xie, Hui Liu, Xianfeng Tang, Sreyashi Nag, William Headden, Yang Li, Chen Luo, Shuiwang Ji, Qi He, Jiliang Tang
| Challenge: | Large language models (LLMs) have demonstrated remarkable success across a wide range of tasks, however, they still face challenges in reasoning tasks that require understanding and inferring relationships between distinct pieces of information within text sequences. |
| Approach: | They propose to construct explicit graphs from context and leverage them to enhance LLM reasoning performance on reasoning tasks. |
| Outcome: | Extensive experiments show that the proposed method improves both logical reasoning and multi-hop question answering tasks. |
Self-EmoQ: Plutchik-Guided Value-based Planning to Drive Streaming Emotional TTS (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing systems lack a self-emotion determination mechanism to drive the streaming text-to-speech (TTS) synthesis. |
| Approach: | They propose an emotion-planning framework that determines the emotion prior to the textual generation, grounding the downstream emotional TTS in a streaming manner. |
| Outcome: | The proposed framework outperforms baselines on DailyDialog, EmoryNLP, IMEOCAP, and MELD on emotional alignment, contextual coherence, and expressive fluency. |
LexGenius: An Expert-Level Benchmark for Large Language Models in Legal General Intelligence (2026.findings-acl)
Copied to clipboard
Wenjin Liu, Haoran Luo, Xin Feng, Xiang Ji, Lijuan Zhou, Rui Mao, Jiapu Wang, Shirui Pan, Erik Cambria
| Challenge: | Existing benchmarks for legal general intelligence (GI) are result-oriented and do not evaluate the legal intelligence of large language models (LLMs). |
| Approach: | They propose a Chinese legal benchmark for evaluating legal GI in large language models . they use recent legal cases and exam questions to create multiple-choice questions . |
| Outcome: | The proposed benchmarks lack a systematic evaluation of the legal intelligence of large language models (LLMs) the results show that even the best LLMs lagging behind human legal professionals. |
FinMME: Benchmark Dataset for Financial Multi-Modal Reasoning Evaluation (2025.acl-long)
Copied to clipboard
Junyu Luo, Zhizhuo Kou, Liming Yang, Xiao Luo, Jinsheng Huang, Zhiping Xiao, Jingshu Peng, Chengzhong Liu, Jiaming Ji, Xuanzhe Liu, Sirui Han, Ming Zhang, Yike Guo
| Challenge: | Multimodal Large Language Models (MLLMs) have experienced rapid development in recent years, but there is a notable lack of effective and specialized multimodal evaluation datasets in the financial domain. |
| Approach: | They introduce FinMME, a multimodal large language model with 11,000 financial research samples and 20 annotators. |
| Outcome: | The proposed model performs better than state-of-the-art models, highlighting its challenging nature. |