Papers by Vincent Chen
Re-Invoke: Tool Invocation Rewriting for Zero-Shot Tool Retrieval (2024.findings-emnlp)
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Yanfei Chen, Jinsung Yoon, Devendra Sachan, Qingze Wang, Vincent Cohen-Addad, Mohammadhossein Bateni, Chen-Yu Lee, Tomas Pfister
| Challenge: | Recent advances in large language models have enabled autonomous agents with complex reasoning and task-fulfillment capabilities using a wide range of tools. |
| Approach: | They propose an unsupervised tool retrieval method that leverages LLM’s query understanding capabilities to extract key tool-related context and underlying intents from user queries. |
| Outcome: | The proposed method significantly outperforms state-of-the-art tools in single-tool and multi-tool scenarios, all within a fully unsupervised setting. |
RARR: Researching and Revising What Language Models Say, Using Language Models (2023.acl-long)
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Luyu Gao, Zhuyun Dai, Panupong Pasupat, Anthony Chen, Arun Tejasvi Chaganty, Yicheng Fan, Vincent Zhao, Ni Lao, Hongrae Lee, Da-Cheng Juan, Kelvin Guu
| Challenge: | Language models (LMs) excel at many tasks but often produce unsupported or misleading content. |
| Approach: | They propose a system that finds attribution for any text generation model and post-edits it to fix unsupported content. |
| Outcome: | The proposed system improves attribution while preserving the original output. |
Learning to Rank Visual Stories From Human Ranking Data (2022.acl-long)
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| Challenge: | Existing studies on visual storytelling (VIST) use automated evaluation metrics for text generation. |
| Approach: | They develop a Vrank metric that repurposes human evaluation results for automatic evaluation. |
| Outcome: | The proposed model is more accurate than existing metrics and is generalizable to textual stories. |
SParC: Cross-Domain Semantic Parsing in Context (P19-1)
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Tao Yu, Rui Zhang, Michihiro Yasunaga, Yi Chern Tan, Xi Victoria Lin, Suyi Li, Heyang Er, Irene Li, Bo Pang, Tao Chen, Emily Ji, Shreya Dixit, David Proctor, Sungrok Shim, Jonathan Kraft, Vincent Zhang, Caiming Xiong, Richard Socher, Dragomir Radev
| Challenge: | Xu et al., 2017): a dataset for cross-domain semantic parsing in context with 4,298 question sequences. |
| Approach: | They present a dataset for cross-domainSemanticParsing inContext that consists of 4,298 coherent question sequences. |
| Outcome: | The proposed dataset demonstrates that it has greater semantic diversity and can be generalized to unseen domains due to its cross-domain nature and the unseened databases at test time. |
Reverse Thinking Makes LLMs Stronger Reasoners (2025.naacl-long)
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Justin Chen, Zifeng Wang, Hamid Palangi, Rujun Han, Sayna Ebrahimi, Long Le, Vincent Perot, Swaroop Mishra, Mohit Bansal, Chen-Yu Lee, Tomas Pfister
| Challenge: | Reverse-Enhanced Thinking (RevThink) is a framework for large language models to perform reverse thinking. |
| Approach: | They propose a framework for enhancing forward-backward reasoning by collecting data from a teacher model and employing three objectives to train a student model in a multi-task learning fashion. |
| Outcome: | The proposed framework outperforms a fine-tuning method trained on 10x more forward reasoning on 12 datasets covering commonsense, math, and logical reasoning. |
Multi-VQG: Generating Engaging Questions for Multiple Images (2022.emnlp-main)
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| Challenge: | Traditional visual question generation (VQG) focuses on single images, resulting in a limited ability to comprehend time-series information of the underlying event. |
| Approach: | They propose to generate engaging questions from multiple images using a visual question generation dataset and establish a series of baselines. |
| Outcome: | The proposed model builds stories behind the image sequence to allow for creativity and experience sharing and hence draw attention to downstream applications. |
LOFT: Scalable and More Realistic Long-Context Evaluation (2025.findings-naacl)
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Jinhyuk Lee, Anthony Chen, Zhuyun Dai, Dheeru Dua, Devendra Singh Sachan, Michael Boratko, Yi Luan, Séb Arnold, Vincent Perot, Siddharth Dalmia, Hexiang Hu, Xudong Lin, Panupong Pasupat, Aida Amini, Jeremy R. Cole, Sebastian Riedel, Iftekhar Naim, Ming-Wei Chang, Kelvin Guu
| Challenge: | Long-context language models (LCLMs) can be used to perform tasks traditionally reliant on external tools like retrieval systems or databases. |
| Approach: | They propose a benchmark to evaluate LCLMs' performance on in-context retrieval and reasoning tasks using a set of tokens. |
| Outcome: | The proposed model outperforms state-of-the-art retrieval and RAG systems on in-context retrieval tasks while still requiring prompting strategies. |
LOCOST: State-Space Models for Long Document Abstractive Summarization (2024.eacl-long)
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Florian Le Bronnec, Song Duong, Mathieu Ravaut, Alexandre Allauzen, Nancy Chen, Vincent Guigue, Alberto Lumbreras, Laure Soulier, Patrick Gallinari
| Challenge: | State-space models are a low-complexity alternative to transformers for text generation . however, the quadratic complexity of the input length restricts the application of large pretrained models to long texts. |
| Approach: | They propose an encoder-decoder architecture based on state-space models for conditional text generation with long context inputs. |
| Outcome: | The proposed model saves memory and memory during training and inference time while saving 50% and 87% of memory. |
LawBench: Benchmarking Legal Knowledge of Large Language Models (2024.emnlp-main)
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Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, Fengzhe Zhou, Zhuo Han, Alan Huang, Songyang Zhang, Kai Chen, Zhixin Yin, Zongwen Shen, Jidong Ge, Vincent Ng
| Challenge: | LegalBench evaluated 20 LLMs in 162 legal tasks in 20 countries and jurisdictions. |
| Approach: | They present a comprehensive evaluation of 21 popular Large Language Models and the first comparative analysis of the empirical results. |
| Outcome: | The proposed benchmarks are based on the Bloom’s cognitive taxonomy and are compared to 21 popular LLMs. |
Large Dual Encoders Are Generalizable Retrievers (2022.emnlp-main)
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Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernandez Abrego, Ji Ma, Vincent Zhao, Yi Luan, Keith Hall, Ming-Wei Chang, Yinfei Yang
| Challenge: | Experimental results show that dual encoders outperform sparse and dense retrievers on the BEIR dataset significantly. |
| Approach: | They challenge belief that bottleneck layer is too limited for out-of-domain generalization . they scale up the model while keeping bottleneck as a single dot-product with a fixed size . |
| Outcome: | The proposed model outperforms sparse and dense retrievers on the BEIR dataset significantly. |
CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases (D19-1)
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Tao Yu, Rui Zhang, Heyang Er, Suyi Li, Eric Xue, Bo Pang, Xi Victoria Lin, Yi Chern Tan, Tianze Shi, Zihan Li, Youxuan Jiang, Michihiro Yasunaga, Sungrok Shim, Tao Chen, Alexander Fabbri, Zifan Li, Luyao Chen, Yuwen Zhang, Shreya Dixit, Vincent Zhang, Caiming Xiong, Richard Socher, Walter Lasecki, Dragomir Radev
| Challenge: | CoSQL is a corpus for building cross-domain, general-purpose database querying dialogue systems. |
| Approach: | They present a corpus for building cross-domain, general-purpose database querying dialogue systems . they use a Wizard-of-Oz collection of 3k turns plus 10k+ annotated SQL queries . |
| Outcome: | The proposed corpus is based on a Wizard-of-Oz dataset of 3k dialogues querying 200 complex DBs spanning 138 domains. |