Papers by Weinan He
Exploring the Capacity of Pretrained Language Models for Reasoning about Actions and Change (2023.acl-long)
Copied to clipboard
| Challenge: | Recent transformer-based language models (LMs) provide reasoning over textual benchmarks . RAC is essential to understand and interact with the ever-changing environment . |
| Approach: | They propose to use a transformer-based language model to learn to reason over textual benchmarks. |
| Outcome: | The proposed model minimizes the influence of other linguistic requirements to focus on RAC. |
Attribution-Based Analysis and Optimization of Modular Agentic Workflows (2026.findings-acl)
Copied to clipboard
Yingxuan Yang, Bo Huang, Siyuan Qi, Chao Feng, Haoyi Hu, Yuxuan Zhu, Jinbo Hu, Haoran Zhao, Ziyi He, Xiao Liu, ZongYu Wang, Muning Wen, Lin Qiu, Xuezhi Cao, Xunliang Cai, Yong Yu, Weinan Zhang
| Challenge: | Large Language Models (LLMs) have driven the rise of agentic workflows . yet, how can we attribute performance gains to individual upgrades and their interactions? |
| Approach: | They propose a game-theoretic framework that models component upgrades as players and evaluates component coalitions to compute Shapley values. |
| Outcome: | The proposed framework provides interaction-aware attribution and recommendation for model allocation under a fixed workflow structure. |
WinoLogic: A Zero-Shot Logic-based Diagnostic Dataset for Winograd Schema Challenge (2021.emnlp-main)
Copied to clipboard
| Challenge: | Recent success of neural language models on the Winograd Schema Challenge has called for further investigation of commonsense reasoning ability of these models. |
| Approach: | They propose a logic-based framework that focuses on high-quality commonsense knowledge. |
| Outcome: | The proposed framework focuses on high-quality commonsense knowledge. |
PaSa: An LLM Agent for Comprehensive Academic Paper Search (2025.acl-long)
Copied to clipboard
| 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 . |
Improving Unsupervised Commonsense Reasoning Using Knowledge-Enabled Natural Language Inference (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Recent methods based on pre-trained language models have shown strong supervised performance on commonsense reasoning. |
| Approach: | They propose to use a common framework to solve commonsense reasoning tasks using a dataset from NLI. |
| Outcome: | The proposed method achieves state-of-the-art unsupervised performance on two commonsense reasoning tasks. |