activity
20202023
most citedEvaluating Large Language Models on Graphs: Performance Insights and Comparative Analysis

4 citations · 11 across the 7 of their papers we have counts for

collaborators

7 papers

cs.AI2023★ 4 cited

Evaluating Large Language Models on Graphs: Performance Insights and Comparative Analysis

Chang Liu, Bo Wu

Large Language Models (LLMs) have garnered considerable interest within both academic and industrial. Yet, the application of LLMs to graph data remains under-explored. In this stu…

cs.CL2023

More than Classification: A Unified Framework for Event Temporal Relation Extraction

Quzhe Huang, Yutong Hu, Shengqi Zhu +3

Event temporal relation extraction~(ETRE) is usually formulated as a multi-label classification task, where each type of relation is simply treated as a one-hot label. This formula…

cs.CL2022

Adam: Dense Retrieval Distillation with Adaptive Dark Examples

Chongyang Tao, Chang Liu, Tao Shen +4

To improve the performance of the dual-encoder retriever, one effective approach is knowledge distillation from the cross-encoder ranker. Existing works construct the candidate pas…

cs.CL2021★ 1 cited

Building an Efficient and Effective Retrieval-based Dialogue System via Mutual Learning

Chongyang Tao, Jiazhan Feng, Chang Liu +3

Establishing retrieval-based dialogue systems that can select appropriate responses from the pre-built index has gained increasing attention from researchers. For this task, the ad…

cs.CL2021★ 3 cited

Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots

Juntao Li, Chang Liu, Chongyang Tao +4

Existing multi-turn context-response matching methods mainly concentrate on obtaining multi-level and multi-dimension representations and better interactions between context uttera…

cs.CL2020

Cross-Lingual Low-Resource Set-to-Description Retrieval for Global E-Commerce

Juntao Li, Chang Liu, Jian Wang +5

With the prosperous of cross-border e-commerce, there is an urgent demand for designing intelligent approaches for assisting e-commerce sellers to offer local products for consumer…

cs.CL2020★ 3 cited

Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog

Shen Gao, Xiuying Chen, Chang Liu +3

Stickers with vivid and engaging expressions are becoming increasingly popular in online messaging apps, and some works are dedicated to automatically select sticker response by ma…