Papers by Victor Li

Agentic Economic Modeling (2026.acl-industry)

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Bohan Zhang, Jiaxuan Li, Ali Hortacsu, Xiaoyang Ye, Victor Chernozhukov, Anqi Ni, Edward W Huang

Challenge:	AEM is a framework that aligns synthetic LLM choices with small-sample human evidence for reliable econometric inference.
Approach:	They introduce a framework that aligns synthetic LLM choices with small-sample human evidence for reliable econometric inference.
Outcome:	The proposed framework improves RCT efficiency and establishes a foundation method for LLM-based counterfactual generation.

Doc-React: Multi-page Heterogeneous Document Question-answering (2025.acl-short)

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Junda Wu, Yu Xia, Tong Yu, Xiang Chen, Sai Sree Harsha, Akash V Maharaj, Ruiyi Zhang, Victor Bursztyn, Sungchul Kim, Ryan A. Rossi, Julian McAuley, Yunyao Li, Ritwik Sinha

Information Retrieval

Challenge:	Existing methods for integrating information across multiple modalities are suboptimal for multi-page, multimodal documents.
Approach:	They propose an adaptive iterative framework that balances information gain and uncertainty reduction at each step.
Outcome:	The proposed framework captures relevant multimodal content and achieves strong performance on complex QA tasks.

On the Importance of the Kullback-Leibler Divergence Term in Variational Autoencoders for Text Generation (D19-56)

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Victor Prokhorov, Ehsan Shareghi, Yingzhen Li, Mohammad Taher Pilehvar, Nigel Collier

Language Models

Challenge:	Variational Autoencoders suffer from learning uninformative latent representations due to issues such as approximated posterior collapse or entanglement of the latent space.
Approach:	They propose to impose an explicit constraint on the Kullback-Leibler divergence term inside the VAE objective function to understand the significance of the KL term in controlling the information transmitted through the VAe channel.
Outcome:	The proposed constraint avoids posterior collapse, but it also controls the information transmitted through the VAE channel.

Multi-view Contrastive Learning for Entity Typing over Knowledge Graphs (2023.emnlp-main)

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Zhiwei Hu, Victor Basulto, Zhiliang Xiang, Ru Li, Jeff Pan

Semantic Text Processing

Challenge:	Existing approaches to knowledge graph entity typing ignore the way types can be clustered together.
Approach:	They propose a method that effectively encodes coarse-grained knowledge from clusters into entity and type embeddings.
Outcome:	The proposed method encodes coarse-grained knowledge from clusters into entity and type embeddings.

MiCEval: Unveiling Multimodal Chain of Thought’s Quality via Image Description and Reasoning Steps (2025.naacl-long)

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Xiongtao Zhou, Jie He, Lanyu Chen, Jingyu Li, Haojing Chen, Victor Gutierrez Basulto, Jeff Z. Pan, Hanjie Chen

Multimodality

Challenge:	Existing methods for evaluating the quality of reasoning steps in multimodal chain-of-thought are lacking.
Approach:	They propose a framework to evaluate the correctness of reasoning chains by evaluating the quality of both the description and each reasoning step.
Outcome:	The proposed framework improves interpretability and human judgments on four state-of-the-art MLLMs.

CPO: Addressing Reward Ambiguity in Role-playing Dialogue via Comparative Policy Optimization (2025.findings-emnlp)

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Jing Ye, Rui Wang, Yuchuan Wu, Victor Ma, Feiteng Fang, Fei Huang, Yongbin Li

Dialogue Systems & Conversational Agents

Challenge:	Comparative Policy Optimization (CPO) redefines the reward evaluation paradigm by shifting from sample-wise scoring to comparative group-wise score.
Approach:	They propose a method to optimize subjective tasks by shifting from sample-wise to comparative group-wise scoring.
Outcome:	The proposed framework shifts from sample-wise scoring to comparative group-wise score . it minimizes contextual bias and enables more robust and fair performance evaluation.

Transformer-based Entity Typing in Knowledge Graphs (2022.emnlp-main)

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Zhiwei Hu, Victor Gutierrez-Basulto, Zhiliang Xiang, Ru Li, Jeff Pan

Semantic Text Processing

Challenge:	Existing knowledge graphs encoding entity types are far from complete, since in real-world applications they are continuously emerging.
Approach:	They propose a transformer-based approach to infer plausible entity types by encoding neighbours' information by a local transformer and a global transformer.
Outcome:	The proposed approach outperforms the state-of-the-art on two real-world datasets.

A Multi-Agent Framework for Quantitative Finance : An Application to Portfolio Management Analytics (2025.emnlp-industry)

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Sayani Kundu, Dushyant Sahoo, Victor Li, Jennifer Rabowsky, Amit Varshney

Text Generation

Challenge:	Recent advances in Large Language Models (LLMs) have opened up promising new avenues by enhancing reasoning and inference capabilities across diverse data and information sources.
Approach:	They propose a multi-agent framework that facilitates mathematical modeling and data analytics by dynamically generating executable code.
Outcome:	The proposed framework outperforms existing models on portfolio management tasks and provides human-readable explanations for its predictions.

On the Sparsity of Neural Machine Translation Models (2020.emnlp-main)

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Yong Wang, Longyue Wang, Victor Li, Zhaopeng Tu

Multilinguality

Challenge:	Modern neural machine translation models employ a large number of parameters, which leads to serious over-parameterization.
Approach:	They propose to prune parameters to improve the model by +0.8 BLEU points and to reallocate them to enhance the ability of modeling low-level lexical information.
Outcome:	The pruned parameters improve the model by +0.8 BLEU points and the rejuvenated parameters enhance the ability to model low-level lexical information.

UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models (2022.emnlp-main)

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Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer, Tao Yu

Semantic Text Processing

Challenge:	Structured knowledge grounding (SKG) uses structured knowledge to complete user requests . since inputs and outputs of SKG tasks are heterogeneous, they have been studied separately .
Approach:	They propose a framework that unifies 21 SKG tasks into a text-to-text format . they use unifiedSKG to benchmark T5 with different sizes .
Outcome:	The proposed framework unifies 21 SKG tasks into a text-to-text format . it achieves state-of-the-art performance on almost all of the 21 tasks, the authors show .

Think Twice, Generate Once: Safeguarding by Progressive Self-Reflection (2025.findings-emnlp)

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Hoang Phan, Victor Li, Qi Lei

Responsible & Trustworthy NLP

Challenge:	Large language models generate coherent and contextually relevant text, but their deployment raises significant concerns about the potential for harmful or inappropriate content.
Approach:	They propose a novel inference-time technique that empowers LLMs to self-monitor and correct their outputs dynamically.
Outcome:	The proposed method reduces the attack success rate from 77.47% to 5.86%, to Llama-3.1-8B base from 89.70% to 5.56%, and to Qwen2.5-7B-Instruct from 44.44% to 3.84%, without additional training.