Lei Li

Affiliations:
  • University of Hong Kong, Hong Kong
  • Peking University, School of EECS, Beijing, China (former)


According to our database1, Lei Li authored at least 51 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

2019
2020
2021
2022
2023
2024
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Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Next Token Prediction Towards Multimodal Intelligence: A Comprehensive Survey.
CoRR, 2024

VLRewardBench: A Challenging Benchmark for Vision-Language Generative Reward Models.
CoRR, 2024

ProReason: Multi-Modal Proactive Reasoning with Decoupled Eyesight and Wisdom.
CoRR, 2024

Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models.
CoRR, 2024

Temporal Reasoning Transfer from Text to Video.
CoRR, 2024

Jailbreaking as a Reward Misspecification Problem.
CoRR, 2024

Vibe-Eval: A hard evaluation suite for measuring progress of multimodal language models.
CoRR, 2024

Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models.
CoRR, 2024

Unleashing the Potential of Large Language Models for Predictive Tabular Tasks in Data Science.
CoRR, 2024

Towards Multimodal Video Paragraph Captioning Models Robust to Missing Modality.
CoRR, 2024

ImgTrojan: Jailbreaking Vision-Language Models with ONE Image.
CoRR, 2024

VLFeedback: A Large-Scale AI Feedback Dataset for Large Vision-Language Models Alignment.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

A Survey on In-context Learning.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations.
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024

Large Language Models are not Fair Evaluators.
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024

Red Teaming Visual Language Models.
Proceedings of the Findings of the Association for Computational Linguistics, 2024

Multimodal ArXiv: A Dataset for Improving Scientific Comprehension of Large Vision-Language Models.
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024

2023
Silkie: Preference Distillation for Large Visual Language Models.
CoRR, 2023

Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations.
CoRR, 2023

Making Large Language Models Better Reasoners with Alignment.
CoRR, 2023

M<sup>3</sup>IT: A Large-Scale Dataset towards Multi-Modal Multilingual Instruction Tuning.
CoRR, 2023

Large Language Models are not Fair Evaluators.
CoRR, 2023

ImageNetVC: Zero-Shot Visual Commonsense Evaluation on 1000 ImageNet Categories.
CoRR, 2023

A Survey for In-context Learning.
CoRR, 2023

Can We Edit Factual Knowledge by In-Context Learning?
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

ImageNetVC: Zero- and Few-Shot Visual Commonsense Evaluation on 1000 ImageNet Categories.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning.
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

Can Language Models Understand Physical Concepts?
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

Communication Efficient Federated Learning for Multilingual Neural Machine Translation with Adapter.
Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023

2022
Alleviating the Knowledge-Language Inconsistency: A Study for Deep Commonsense Knowledge.
IEEE ACM Trans. Audio Speech Lang. Process., 2022

Gradient Knowledge Distillation for Pre-trained Language Models.
CoRR, 2022

Rethinking the Openness of CLIP.
CoRR, 2022

Distributional Correlation-Aware Knowledge Distillation for Stock Trading Volume Prediction.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

Rethinking the Promotion Brought by Contrastive Learning to Semi-Supervised Node Classification.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

From Mimicking to Integrating: Knowledge Integration for Pre-Trained Language Models.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2022, 2022

Well-Classified Examples Are Underestimated in Classification with Deep Neural Networks.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
Model Uncertainty-Aware Knowledge Amalgamation for Pre-Trained Language Models.
CoRR, 2021

Well-classified Examples are Underestimated in Classification with Deep Neural Networks.
CoRR, 2021

Decompose, Fuse and Generate: A Formation-Informed Method for Chinese Definition Generation.
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021

Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models.
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021

Leveraging Word-Formation Knowledge for Chinese Word Sense Disambiguation.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2021, 2021

Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification.
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021

Dynamic Knowledge Distillation for Pre-trained Language Models.
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021

CascadeBERT: Accelerating Inference of Pre-trained Language Models via Calibrated Complete Models Cascade.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2021, 2021

2020
Accelerating Pre-trained Language Models via Calibrated Cascade.
CoRR, 2020

Distance-wise Graph Contrastive Learning.
CoRR, 2020

2019
Knowledgeable Storyteller: A Commonsense-Driven Generative Model for Visual Storytelling.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Pun-GAN: Generative Adversarial Network for Pun Generation.
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019

Automatic Generation of Personalized Comment Based on User Profile.
Proceedings of the 57th Conference of the Association for Computational Linguistics, 2019

Cross-Modal Commentator: Automatic Machine Commenting Based on Cross-Modal Information.
Proceedings of the 57th Conference of the Association for Computational Linguistics, 2019

Enhancing Topic-to-Essay Generation with External Commonsense Knowledge.
Proceedings of the 57th Conference of the Association for Computational Linguistics, 2019


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