Enabling Large Language Models to Generate Text with Citations
Comments Accepted by EMNLP 2023. Code and data are available at https://github.com/princeton-nlp/ALCE
期刊&会议
EMNLP
Conference on Empirical Methods in Natural Language Processing · 会议 · Natural Language Processing
GLEN: General-Purpose Event Detection for Thousands of Types
Comments Accepted to EMNLP 2023. The first two authors contributed equally. (16 pages)
EffEval: A Comprehensive Evaluation of Efficiency for MT Evaluation Metrics
Comments Findings of EMNLP 2023; note the title and author change
Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning
Comments EMNLP 2020 Findings
Video-Helpful Multimodal Machine Translation
Comments Accepted by EMNLP 2023 Main Conference (long paper)
Unlearn What You Want to Forget: Efficient Unlearning for LLMs
Comments EMNLP 2023
EELBERT: Tiny Models through Dynamic Embeddings
Comments EMNLP 2023, Industry Track 9 pages, 2 figures, 5 tables
Improving Prompt Tuning with Learned Prompting Layers
Comments Accepted by EMNLP-2023
Ling-CL: Understanding NLP Models through Linguistic Curricula
Comments EMNLP 2023
Making Large Language Models Better Data Creators
Comments Accepted to EMNLP 2023 main conference. 12 pages, 5 figures, 6 tables. Code is available at https://github.com/microsoft/llm-data-creation
Large Language Models: The Need for Nuance in Current Debates and a Pragmatic Perspective on Understanding
Comments 15 pages, 0 figures, Forthcoming in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
LLMaAA: Making Large Language Models as Active Annotators
Comments Findings of EMNLP 2023 camera ready
Towards a Better Understanding of Variations in Zero-Shot Neural Machine Translation Performance
Comments This paper is accepted by the EMNLP 2023 Main Conference
Investigating Table-to-Text Generation Capabilities of LLMs in Real-World Information Seeking Scenarios
Comments Camera-ready version for EMNLP 2023 industry track
Counterfactual Augmentation for Multimodal Learning Under Presentation Bias
Comments Accepted to Findings of EMNLP 2023
What's "up" with vision-language models? Investigating their struggle with spatial reasoning
Comments EMNLP 2023
Chain-of-Thought Embeddings for Stance Detection on Social Media
Comments Accepted at EMNLP-2023, 8 pages
Mean BERTs make erratic language teachers: the effectiveness of latent bootstrapping in low-resource settings
Comments submission to the BabyLM shared at CoNLL (EMNLP)
A Lightweight Method to Generate Unanswerable Questions in English
Comments Accepted to Findings of EMNLP 2023
ROME: Evaluating Pre-trained Vision-Language Models on Reasoning beyond Visual Common Sense
Comments This is the camera-ready version of the paper that will be published in the EMNLP 2023 Findings (Singapore, 6-10 December 2023)
Fusing Temporal Graphs into Transformers for Time-Sensitive Question Answering
Comments EMNLP 2023 Findings
Learning to love diligent trolls: Accounting for rater effects in the dialogue safety task
Comments Accept-Findings at EMNLP 2023
Sound of Story: Multi-modal Storytelling with Audio
Comments Findings of EMNLP 2023, project: https://github.com/Sosdatasets/SoS_Dataset/
Adapter Pruning using Tropical Characterization
Comments Accepted at EMNLP 2023, Findings
Poisoning Retrieval Corpora by Injecting Adversarial Passages
Comments EMNLP 2023. Our code is available at https://github.com/princeton-nlp/corpus-poisoning
Learning to Follow Object-Centric Image Editing Instructions Faithfully
Comments Findings of EMNLP 2023 (Long paper)
Unified Representation for Non-compositional and Compositional Expressions
Comments This work is accepted to EMNLP 2023 Findings
Pushdown Layers: Encoding Recursive Structure in Transformer Language Models
Comments Accepted at EMNLP 2023 (Long Papers)
Bipartite Graph Pre-training for Unsupervised Extractive Summarization with Graph Convolutional Auto-Encoders
Comments Accepted by the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023)
EtiCor: Corpus for Analyzing LLMs for Etiquettes
Comments Accepted at EMNLP 2023, Main Conference
