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20182023
most citedHarnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond

151 citations · 304 across the 10 of their papers we have counts for

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Showing cs.CLShow all

9 papers · 1 filter

cs.CL20233 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.CL2023151 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.CL20221 cited

Short Text Pre-training with Extended Token Classification for E-commerce Query Understanding

Haoming Jiang, Tianyu Cao, Zheng Li +6

E-commerce query understanding is the process of inferring the shopping intent of customers by extracting semantic meaning from their search queries. The recent progress of pre-tra…

cs.CL2022

SeqZero: Few-shot Compositional Semantic Parsing with Sequential Prompts and Zero-shot Models

Jingfeng Yang, Haoming Jiang, Qingyu Yin +3

Recent research showed promising results on combining pretrained language models (LMs) with canonical utterance for few-shot semantic parsing. The canonical utterance is often leng…

cs.CL2021

Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach

Haoming Jiang, Bo Dai, Mengjiao Yang +2

Reliable automatic evaluation of dialogue systems under an interactive environment has long been overdue. An ideal environment for evaluating dialog systems, also known as the Turi…

cs.CL2020

Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data

Lingkai Kong, Haoming Jiang, Yuchen Zhuang +3

Fine-tuned pre-trained language models can suffer from severe miscalibration for both in-distribution and out-of-distribution (OOD) data due to over-parameterization. To mitigate t…