Goal-Driven Explainable Clustering via Language Descriptions
Comments EMNLP 2023
期刊&会议
EMNLP
Conference on Empirical Methods in Natural Language Processing · 会议 · Natural Language Processing
Navigating the Grey Area: How Expressions of Uncertainty and Overconfidence Affect Language Models
Comments EMNLP 2023 (Oral)
Byte Pair Encoding for Symbolic Music
Comments EMNLP 2023, source code: https://github.com/Natooz/BPE-Symbolic-Music
SciRepEval: A Multi-Format Benchmark for Scientific Document Representations
Comments 19 pages, 2 figures, 11 tables. Accepted in EMNLP 2023 Main Conference
Tunable Soft Prompts are Messengers in Federated Learning
Comments Accepted by EMNLP-23
Learning Knowledge-Enhanced Contextual Language Representations for Domain Natural Language Understanding
Comments emnlp 2023
Sharing, Teaching and Aligning: Knowledgeable Transfer Learning for Cross-Lingual Machine Reading Comprehension
Comments emnlp 2023
From Complex to Simple: Unraveling the Cognitive Tree for Reasoning with Small Language Models
Comments emnlp 2023
BeautifulPrompt: Towards Automatic Prompt Engineering for Text-to-Image Synthesis
Comments emnlp 2023
Analyzing Modular Approaches for Visual Question Decomposition
Comments Published at EMNLP 2023 (Main Conference). Source code: https://github.com/brown-palm/visual-question-decomposition
Proto-lm: A Prototypical Network-Based Framework for Built-in Interpretability in Large Language Models
Comments Accepted to the Findings of EMNLP 2023
The Curious Case of Hallucinatory (Un)answerability: Finding Truths in the Hidden States of Over-Confident Large Language Models
Comments EMNLP 2023
Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs
Comments Paper published at Findings of the Association for Computational Linguistics: EMNLP, 2023
Prototype-based HyperAdapter for Sample-Efficient Multi-task Tuning
Comments Accepted by EMNLP 2023
Reading Books is Great, But Not if You Are Driving! Visually Grounded Reasoning about Defeasible Commonsense Norms
Comments Published as a conference paper at EMNLP 2023 (long)
ASSERT: Automated Safety Scenario Red Teaming for Evaluating the Robustness of Large Language Models
Comments In Findings of the 2023 Conference on Empirical Methods in Natural Language Processing
Transfer-Free Data-Efficient Multilingual Slot Labeling
Comments Accepted to EMNLP 2023
Interactive Text Generation
Comments EMNLP 2023
Are Language Models Worse than Humans at Following Prompts? It's Complicated
Comments EMNLP 2023
Bridging the Digital Divide: Performance Variation across Socio-Economic Factors in Vision-Language Models
Journal ref EMNLP 2023
Long-Horizon Dialogue Understanding for Role Identification in the Game of Avalon with Large Language Models
Comments Accepted to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP, Findings of the Association for Computational Linguistics)
Target-Agnostic Gender-Aware Contrastive Learning for Mitigating Bias in Multilingual Machine Translation
Comments Accepted to EMNLP 2023 Main Conference
E2E Spoken Entity Extraction for Virtual Agents
Comments Accepted at EMNLP 2023 Industry Track
The Iron(ic) Melting Pot: Reviewing Human Evaluation in Humour, Irony and Sarcasm Generation
Comments Accepted to Findings of EMNLP 2023
Text Representation Distillation via Information Bottleneck Principle
Comments Accepted to EMNLP 2023. The code and pre-trained models are available at [this https URL](https://github.com/Alibaba-NLP/IBKD)
Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation
Comments Accepted to EMNLP 2023
Memorisation Cartography: Mapping out the Memorisation-Generalisation Continuum in Neural Machine Translation
Comments Published in EMNLP 2023; 21 pages total (9 in the main paper, 3 pages with limitations, acknowledgments and references, 9 pages with appendices)
Don't Waste a Single Annotation: Improving Single-Label Classifiers Through Soft Labels
Comments Accepted to EMNLP 2023 (Findings)
Weakly-supervised Deep Cognate Detection Framework for Low-Resourced Languages Using Morphological Knowledge of Closely-Related Languages
Comments Accepted at EMNLP 2023 conference
GPT4All: An Ecosystem of Open Source Compressed Language Models
Comments Accepted at NLP-OSS at EMNLP 2023
