EMNLP

Context-Aware Adapter Tuning for Few-Shot Relation Learning in Knowledge Graphs

Ran Liu, Zhongzhou Liu, Xiaoli Li, Yuan Fang

Comments Accepted by EMNLP 2024

Cross-modality Information Check for Detecting Jailbreaking in Multimodal Large Language Models

Yue Xu, Xiuyuan Qi, Zhan Qin, Wenjie Wang

Comments 12 pages, 9 figures, EMNLP 2024 Findings

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

Zhuowan Li, Cheng Li, Mingyang Zhang, Qiaozhu Mei, Michael Bendersky

Comments Accepted to EMNLP 2024 industry track

RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models

Peng Xia, Kangyu Zhu, Haoran Li, Hongtu Zhu, Yun Li, Gang Li, Linjun Zhang, Huaxiu Yao

Comments EMNLP 2024 main

Granular Privacy Control for Geolocation with Vision Language Models

Ethan Mendes, Yang Chen, James Hays, Sauvik Das, Wei Xu, Alan Ritter

Comments Accepted to EMNLP 2024 main conference

Learning to Ask Informative Questions: Enhancing LLMs with Preference Optimization and Expected Information Gain

Davide Mazzaccara, Alberto Testoni, Raffaella Bernardi

Comments Accepted to EMNLP 2024 (Findings)

ShadowLLM: Predictor-based Contextual Sparsity for Large Language Models

Yash Akhauri, Ahmed F AbouElhamayed, Jordan Dotzel, Zhiru Zhang, Alexander M Rush, Safeen Huda, Mohamed S Abdelfattah

Comments Accepted to EMNLP 2024 (Main, Long Paper)

Modeling Human Subjectivity in LLMs Using Explicit and Implicit Human Factors in Personas

Salvatore Giorgi, Tingting Liu, Ankit Aich, Kelsey Isman, Garrick Sherman, Zachary Fried, João Sedoc, Lyle H. Ungar, Brenda Curtis

Comments Accepted at Findings of EMNLP 2024

A Systematic Analysis of Large Language Models as Soft Reasoners: The Case of Syllogistic Inferences

Leonardo Bertolazzi, Albert Gatt, Raffaella Bernardi

Comments Accepted to EMNLP 2024 (main conference)

Instruction Matters: A Simple yet Effective Task Selection for Optimized Instruction Tuning of Specific Tasks

Changho Lee, Janghoon Han, Seonghyeon Ye, Stanley Jungkyu Choi, Honglak Lee, Kyunghoon Bae

Comments EMNLP 2024 (Camera-ready), by Janghoon Han and Changho Lee, with equal contribution

LLoCO: Learning Long Contexts Offline

Sijun Tan, Xiuyu Li, Shishir Patil, Ziyang Wu, Tianjun Zhang, Kurt Keutzer, Joseph E. Gonzalez, Raluca Ada Popa

Comments EMNLP 2024. The first two authors contributed equally to this work

"Let's Argue Both Sides": Argument Generation Can Force Small Models to Utilize Previously Inaccessible Reasoning Capabilities

Kaveh Eskandari Miandoab, Vasanth Sarathy

Comments Accepted to Workshop on Customizable NLP: Progress and Challenges in Customizing NLP for a Domain, Application, Group, or Individual at EMNLP 2024

Merge to Learn: Efficiently Adding Skills to Language Models with Model Merging

Jacob Morrison, Noah A. Smith, Hannaneh Hajishirzi, Pang Wei Koh, Jesse Dodge, Pradeep Dasigi

Comments Findings of EMNLP 2024

TextLap: Customizing Language Models for Text-to-Layout Planning

Jian Chen, Ruiyi Zhang, Yufan Zhou, Jennifer Healey, Jiuxiang Gu, Zhiqiang Xu, Changyou Chen

Comments Accepted to the EMNLP Findings

LitSearch: A Retrieval Benchmark for Scientific Literature Search

Anirudh Ajith, Mengzhou Xia, Alexis Chevalier, Tanya Goyal, Danqi Chen, Tianyu Gao

Comments Accepted by EMNLP 2024. Dataset and code are available at https://github.com/princeton-nlp/LitSearch

Experimental Contexts Can Facilitate Robust Semantic Property Inference in Language Models, but Inconsistently

Kanishka Misra, Allyson Ettinger, Kyle Mahowald

Comments EMNLP 2024 (main) camera-ready

ProSA: Assessing and Understanding the Prompt Sensitivity of LLMs

Jingming Zhuo, Songyang Zhang, Xinyu Fang, Haodong Duan, Dahua Lin, Kai Chen

Comments EMNLP 2024, Findings

Model Balancing Helps Low-data Training and Fine-tuning

Zihang Liu, Yuanzhe Hu, Tianyu Pang, Yefan Zhou, Pu Ren, Yaoqing Yang

Comments EMNLP 2024 Oral. First two authors contributed equally

Boosting Logical Fallacy Reasoning in LLMs via Logical Structure Tree

Yuanyuan Lei, Ruihong Huang

Comments Accepted to EMNLP 2024

Pixology: Probing the Linguistic and Visual Capabilities of Pixel-based Language Models

Kushal Tatariya, Vladimir Araujo, Thomas Bauwens, Miryam de Lhoneux

Comments 9 pages, Accepted to EMNLP 2025 Main

Toolken+: Improving LLM Tool Usage with Reranking and a Reject Option

Konstantin Yakovlev, Sergey Nikolenko, Andrey Bout

Comments EMNLP 2024 Findings

Semantic Token Reweighting for Interpretable and Controllable Text Embeddings in CLIP

Eunji Kim, Kyuhong Shim, Simyung Chang, Sungroh Yoon

Comments Accepted at EMNLP 2024 Findings

DyVo: Dynamic Vocabularies for Learned Sparse Retrieval with Entities

Thong Nguyen, Shubham Chatterjee, Sean MacAvaney, Iain Mackie, Jeff Dalton, Andrew Yates

Comments https://github.com/thongnt99/DyVo

Journal ref EMNLP 2024

The Accuracy Paradox in RLHF: When Better Reward Models Don't Yield Better Language Models

Yanjun Chen, Dawei Zhu, Yirong Sun, Xinghao Chen, Wei Zhang, Xiaoyu Shen

Comments 10 pages, 27 figures (including 18 in the appendix), submitted to EMNLP 2024

Adaptation Odyssey in LLMs: Why Does Additional Pretraining Sometimes Fail to Improve?

Fırat Öncel, Matthias Bethge, Beyza Ermis, Mirco Ravanelli, Cem Subakan, Çağatay Yıldız

Comments Accepted to EMNLP 2024 Main Conference

SciDoc2Diagrammer-MAF: Towards Generation of Scientific Diagrams from Documents guided by Multi-Aspect Feedback Refinement

Ishani Mondal, Zongxia Li, Yufang Hou, Anandhavelu Natarajan, Aparna Garimella, Jordan Boyd-Graber

Comments Code and data available at https://github.com/Ishani-Mondal/SciDoc2DiagramGeneration

Journal ref Empirical Methods in Natural Language Processing 2024

Robust ASR Error Correction with Conservative Data Filtering

Takuma Udagawa, Masayuki Suzuki, Masayasu Muraoka, Gakuto Kurata

Comments Accepted to EMNLP 2024 Industry Track

MERLIN: Multimodal Embedding Refinement via LLM-based Iterative Navigation for Text-Video Retrieval-Rerank Pipeline

Donghoon Han, Eunhwan Park, Gisang Lee, Adam Lee, Nojun Kwak

Comments EMNLP 2024 Industry Track Accepted (Camera-Ready Version)

PromptIntern: Saving Inference Costs by Internalizing Recurrent Prompt during Large Language Model Fine-tuning

Jiaru Zou, Mengyu Zhou, Tao Li, Shi Han, Dongmei Zhang

Comments This paper has been accepted by EMNLP 2024

Beyond Demographics: Aligning Role-playing LLM-based Agents Using Human Belief Networks

Yun-Shiuan Chuang, Krirk Nirunwiroj, Zach Studdiford, Agam Goyal, Vincent V. Frigo, Sijia Yang, Dhavan Shah, Junjie Hu, Timothy T. Rogers

Journal ref Findings of the Association for Computational Linguistics (ACL): EMNLP 2024