Papers by Weinan E

3 papers
An Iteratively Parallel Generation Method with the Pre-Filling Strategy for Document-level Event Extraction (2023.emnlp-main)

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Challenge: Existing methods to generate event roles require a given generation order . parallel methods suffer from inadequate training and manifest zero accuracies on some event roles.
Approach: They propose an iteratively parallel generation method with the Pre-Filling strategy to generate event roles in parallel to avoid order selection.
Outcome: The proposed method outperforms other entity-enhanced models and achieves state-of-the-art performance on two public datasets.
PaSa: An LLM Agent for Comprehensive Academic Paper Search (2025.acl-long)

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Challenge: We introduce PaSa, an advanced Paper Search agent powered by large language models . despite being trained on synthetic data, PaSA outperforms existing baselines on RealScholarQuery .
Approach: They introduce PaSa, an advanced Paper Search agent powered by large language models . they optimize PaSA using a synthetic dataset, AutoScholarQuery, which includes 35k fine-grained queries .
Outcome: The paper analyzes the performance of a paper search agent using a synthetic dataset . it significantly outperforms existing benchmarks on RealScholarQuery .
TeachMaster: Generative Teaching via Code (2026.acl-industry)

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Challenge: Existing methods for creating video content are limited by high costs and slow update cycles.
Approach: They propose a paradigm shifting educators from manual creators to high-level directors who focus on pedagogical intents while agents handle execution.
Outcome: The proposed framework reduces production costs to 0.3% of traditional course videos and provides a robust solution for scalable education.

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