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20192023
most citedHarnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond

151 citations · 240 across the 28 of their papers we have counts for

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Showing 2023Show all

9 papers · 1 filter

cs.CL2023

Situated Natural Language Explanations

Zining Zhu, Haoming Jiang, Jingfeng Yang +6

Natural language is among the most accessible tools for explaining decisions to humans, and large pretrained language models (PLMs) have demonstrated impressive abilities to genera…

cs.CV2023

Multimodal Prompt Learning for Product Title Generation with Extremely Limited Labels

Bang Yang, Fenglin Liu, Zheng Li +4

Generating an informative and attractive title for the product is a crucial task for e-commerce. Most existing works follow the standard multimodal natural language generation appr…

cs.IR2023★ 10 cited

Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation

Wei Jin, Haitao Mao, Zheng Li +17

Modeling customer shopping intentions is a crucial task for e-commerce, as it directly impacts user experience and engagement. Thus, accurately understanding customer preferences i…

cs.AI2023

Knowledge Graph Reasoning over Entities and Numerical Values

Jiaxin Bai, Chen Luo, Zheng Li +3

A complex logic query in a knowledge graph refers to a query expressed in logic form that conveys a complex meaning, such as where did the Canadian Turing award winner graduate fro…

cs.CL2023

Graph Reasoning for Question Answering with Triplet Retrieval

Shiyang Li, Yifan Gao, Haoming Jiang +5

Answering complex questions often requires reasoning over knowledge graphs (KGs). State-of-the-art methods often utilize entities in questions to retrieve local subgraphs, which ar…

cs.CL2023★ 3 cited

CCGen: Explainable Complementary Concept Generation in E-Commerce

Jie Huang, Yifan Gao, Zheng Li +7

We propose and study Complementary Concept Generation (CCGen): given a concept of interest, e.g., "Digital Cameras", generating a list of complementary concepts, e.g., 1) Camera Le…

cs.CL2023★ 2 cited

SCOTT: Self-Consistent Chain-of-Thought Distillation

Peifeng Wang, Zhengyang Wang, Zheng Li +3

Large language models (LMs) beyond a certain scale, demonstrate the emergent capability of generating free-text rationales for their predictions via chain-of-thought (CoT) promptin…

cs.CL2023★ 151 cited

Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond

Jingfeng Yang, Hongye Jin, Ruixiang Tang +5

This paper presents a comprehensive and practical guide for practitioners and end-users working with Large Language Models (LLMs) in their downstream natural language processing (N…

cs.LG2023

Mutually-paced Knowledge Distillation for Cross-lingual Temporal Knowledge Graph Reasoning

Ruijie Wang, Zheng Li, Jingfeng Yang +4

This paper investigates cross-lingual temporal knowledge graph reasoning problem, which aims to facilitate reasoning on Temporal Knowledge Graphs (TKGs) in low-resource languages b…