EMNLP 2023 Conference Submissions

Probing LLMs for hate speech detection: strengths and vulnerabilities
Sarthak Roy, Ashish Harshvardhan, Animesh Mukherjee, Punyajoy Saha
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
COUNT: COntrastive UNlikelihood Text Style Transfer for Text Detoxification
Mohammad Mahdi Abdollah Pour, Parsa Farinneya, Manasa Bharadwaj, Nikhil Verma, Ali Pesaranghader, Scott Sanner
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
Attention-Enhancing Backdoor Attacks Against BERT-based Models
Weimin Lyu, Songzhu Zheng, Lu Pang, Haibin Ling, Chao Chen
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
Text encoders bottleneck compositionality in contrastive vision-language models
Amita Kamath, Jack Hessel, Kai-Wei Chang
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
UniMath: A Foundational and Multimodal Mathematical Reasoner
Zhenwen Liang, Tianyu Yang, Jipeng Zhang, Xiangliang Zhang
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
Statistical Depth for Ranking and Characterizing Transformer-Based Text Embeddings
Parker Seegmiller, Sarah Masud Preum
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation
Zhenwen Liang, Wenhao Yu, Tanmay Rajpurohit, Peter Clark, Xiangliang Zhang, Ashwin Kalyan
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
InfoDiffusion: Information Entropy Aware Diffusion Process for Non-Autoregressive Text Generation
Renzhi Wang, Jing Li, Piji Li
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
Harnessing the power of LLMs: Evaluating human-AI text co-creation through the lens of news headline generation
Zijian Ding, Alison Smith-Renner, Wenjuan Zhang, Joel R. Tetreault, Alejandro Jaimes
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
Mitigating Backdoor Poisoning Attacks through the Lens of Spurious Correlation
Xuanli He, Qiongkai Xu, Jun Wang, Benjamin I. P. Rubinstein, Trevor Cohn
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning
Liangming Pan, Alon Albalak, Xinyi Wang, William Yang Wang
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
Absolute Position Embedding Learns Sinusoid-like Waves for Attention Based on Relative Position
Yuji Yamamoto, Takuya Matsuzaki
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
Accuracy is not enough: Evaluating Personalization in Summarizers
Rahul Vansh, Darsh Rank, Sourish Dasgupta, Tanmoy Chakraborty
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
Interactive Text Generation
Felix Faltings, Michel Galley, Kianté Brantley, Baolin Peng, Weixin Cai, Yizhe Zhang, Jianfeng Gao, Bill Dolan
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
Inverse Reinforcement Learning for Text Summarization
Yu Fu, Deyi Xiong, Yue Dong
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
TaskWeb: Selecting Better Source Tasks for Multi-task NLP
Joongwon Kim, Akari Asai, Gabriel Ilharco, Hannaneh Hajishirzi
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
When Do Decompositions Help for Machine Reading?
Kangda Wei, Dawn Lawrie, Benjamin Van Durme, Yunmo Chen, Orion Weller
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
MILDSum: A Novel Benchmark Dataset for Multilingual Summarization of Indian Legal Case Judgments
Debtanu Datta, Shubham Soni, Rajdeep Mukherjee, Saptarshi Ghosh
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
Code-Switching with Word Senses for Pretraining in Neural Machine Translation
Vivek Iyer, Edoardo Barba, Alexandra Birch, Jeff Z. Pan, Roberto Navigli
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
Enhancing Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies
Linyong Nan, Yilun Zhao, Weijin Zou, Narutatsu Ri, Jaesung Tae, Ellen Zhang, Arman Cohan, Dragomir Radev
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
DUMB: A Dutch Model Benchmark
Wietse de Vries, Martijn Wieling, Malvina Nissim
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
Speech-enriched Memory for Inference-time Adaptation of ASR Models to Word Dictionaries
Ashish Mittal, Sunita Sarawagi, Preethi Jyothi, George Saon, Gakuto Kurata
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
RWKV: Reinventing RNNs for the Transformer Era
Bo Peng, Eric Alcaide, Quentin Gregory Anthony, Alon Albalak, Samuel Arcadinho, Stella Biderman, Huanqi Cao, Xin Cheng, Michael Nguyen Chung, Leon Derczynski, Xingjian Du, Matteo Grella, Kranthi Kiran GV, Xuzheng He, Haowen Hou, Przemyslaw Kazienko, Jan Kocon, Jiaming Kong, Bartłomiej Koptyra, Hayden Lau et al. (12 additional authors not shown)
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
Comparing Biases and the Impact of Multilingual Training across Multiple Languages
Sharon Levy, Neha Anna John, Ling Liu, Yogarshi Vyas, Jie Ma, Yoshinari Fujinuma, Miguel Ballesteros, Vittorio Castelli, Dan Roth
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
BanglaAbuseMeme: A Dataset for Bengali Abusive Meme Classification
Mithun Das, Animesh Mukherjee
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone