Source

Conference on Empirical Methods in Natural Language Processing: Industry Track (EMNLP)

Source type: conference

Alternate names: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track

Fully OA: No

DOAJ: No

OpenAlex ID: S7407088282

mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval

2024 · Xin Zhang, Yanzhao Zhang, et al. · Conference on Empirical Methods in Natural Language Processing: Industry Track (EMNLP) · 101

Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks

2023 · Xianzhi Li, Samuel Chan, et al. · Conference on Empirical Methods in Natural Language Processing: Industry Track (EMNLP) · 86

Harnessing LLMs for Temporal Data - A Study on Explainable Financial Time Series Forecasting

2023 · Xinli Yu, Zheng Chen, et al. · Conference on Empirical Methods in Natural Language Processing: Industry Track (EMNLP) · 82

Let Me Speak Freely? A Study On The Impact Of Format Restrictions On Large Language Model Performance.

2024 · Zhi Rui Tam, Cheng-Kuang Wu, et al. · Conference on Empirical Methods in Natural Language Processing: Industry Track (EMNLP) · 57

Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approach

2024 · Zhuowan Li, Cheng Li, et al. · Conference on Empirical Methods in Natural Language Processing: Industry Track (EMNLP) · 49

Batch Prompting: Efficient Inference with Large Language Model APIs

2023 · Zhoujun Cheng, Jungo Kasai, et al. · Conference on Empirical Methods in Natural Language Processing: Industry Track (EMNLP) · 43

Depression Detection on Social Media with Large Language Models

2025 · Xiaochong Lan, Zhiguang Han, et al. · Conference on Empirical Methods in Natural Language Processing: Industry Track (EMNLP) · 42

Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering

2023 · Fangkai Yang, Pu Zhao, et al. · Conference on Empirical Methods in Natural Language Processing: Industry Track (EMNLP) · 39

ItiNera: Integrating Spatial Optimization with Large Language Models for Open-domain Urban Itinerary Planning

2024 · Yihong Tang, Zhaokai Wang, et al. · Conference on Empirical Methods in Natural Language Processing: Industry Track (EMNLP) · 37

LLM4Vis: Explainable Visualization Recommendation using ChatGPT

2023 · Lei Wang, Songheng Zhang, et al. · Conference on Empirical Methods in Natural Language Processing: Industry Track (EMNLP) · 36

RAG-HAT: A Hallucination-Aware Tuning Pipeline for LLM in Retrieval-Augmented Generation

2024 · Juntong Song, Xingguang Wang, et al. · Conference on Empirical Methods in Natural Language Processing: Industry Track (EMNLP) · 35

Arcee’s MergeKit: A Toolkit for Merging Large Language Models

2024 · Charles Goddard, Shamane Siriwardhana, et al. · Conference on Empirical Methods in Natural Language Processing: Industry Track (EMNLP) · 34

AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model

2024 · Seungwhan Moon, Andrea Madotto, et al. · Conference on Empirical Methods in Natural Language Processing: Industry Track (EMNLP) · 33

Building Real-World Meeting Summarization Systems using Large Language Models: A Practical Perspective

2023 · Md Tahmid Rahman Laskar, Xue-Yong Fu, et al. · Conference on Empirical Methods in Natural Language Processing: Industry Track (EMNLP) · 28

Reinforced Question Rewriting for Conversational Question Answering

2022 · Zhiyu Chen, Jie Zhao, et al. · Conference on Empirical Methods in Natural Language Processing: Industry Track (EMNLP) · 27

SimANS: Simple Ambiguous Negatives Sampling for Dense Text Retrieval

2022 · Kun Zhou, Yeyun Gong, et al. · Conference on Empirical Methods in Natural Language Processing: Industry Track (EMNLP) · 25

Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks

2024 · Ibrahim Abdelaziz, Kinjal Basu, et al. · Conference on Empirical Methods in Natural Language Processing: Industry Track (EMNLP) · 22

JarviX: A LLM No code Platform for Tabular Data Analysis and Optimization

2023 · Shang-Ching Liu, ShengKun Wang, et al. · Conference on Empirical Methods in Natural Language Processing: Industry Track (EMNLP) · 22

Augmenting Operations Research with Auto-Formulation of Optimization Models From Problem Descriptions

2022 · Rindra Ramamonjison, Haley Li, et al. · Conference on Empirical Methods in Natural Language Processing: Industry Track (EMNLP) · 22

Intent Detection in the Age of LLMs

2024 · Gaurav Arora, Shreya Jain, et al. · Conference on Empirical Methods in Natural Language Processing: Industry Track (EMNLP) · 20

AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications

2023 · Bhaktipriya Radharapu, Kevin Robinson, et al. · Conference on Empirical Methods in Natural Language Processing: Industry Track (EMNLP) · 20

BeautifulPrompt: Towards Automatic Prompt Engineering for Text-to-Image Synthesis

2023 · Tingfeng Cao, Chengyu Wang, et al. · Conference on Empirical Methods in Natural Language Processing: Industry Track (EMNLP) · 19

A Pretrained Language Model for Cyber Threat Intelligence

2023 · Youngja Park, Weiqiu You · Conference on Empirical Methods in Natural Language Processing: Industry Track (EMNLP) · 18

TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Industry Systems

2024 · Yilun Kong, Jingqing Ruan, et al. · Conference on Empirical Methods in Natural Language Processing: Industry Track (EMNLP) · 17

Prompt Leakage effect and mitigation strategies for multi-turn LLM Applications

2024 · Divyansh Agarwal, Alexander Fabbri, et al. · Conference on Empirical Methods in Natural Language Processing: Industry Track (EMNLP) · 17

Works count: 458

Citation count: 1,774

H-index: 20

I10-index: 37

2yr mean citedness: 3.003