NLP Model

Natural Language Processing (NLP) models aim to enable computers to understand, interpret, and generate human language. Current research focuses on improving model robustness to noisy or user-generated content, enhancing explainability and interpretability through techniques like counterfactual explanations and latent concept attribution, and addressing biases related to fairness and privacy. These advancements are crucial for building reliable and trustworthy NLP systems with broad applications across various domains, including legal tech, healthcare, and social media analysis.

Papers

October 26, 2023

The Validity of Evaluation Results: Assessing Concurrence Across Compositionality Benchmarks
Kaiser Sun, Adina Williams, Dieuwke Hupkes
Synthetic Data Compositional Generalization NLP Model Evaluation Datasets Concurrent Validity Compositionality Benchmark

October 21, 2023

Finite-context Indexing of Restricted Output Space for NLP Models Facing Noisy Input
Minh Nguyen, Nancy F. Chen
Natural Language Processing NLP Model Structured Output Noisy Input

October 13, 2023

PerturbScore: Connecting Discrete and Continuous Perturbations in NLP
Linyang Li, Ke Ren, Yunfan Shao, Pengyu Wang, Xipeng Qiu
Native Robustness NLP Field NLP Model Model Robustness Additive Perturbation Discrete Environment

October 11, 2023

Faithfulness Measurable Masked Language Models
Andreas Madsen, Siva Reddy, Sarath Chandar
NLP Model Masked Language Faithfulness Test Importance Score Token Masking

October 9, 2023

InterroLang: Exploring NLP Models and Datasets through Dialogue-based Explanations
Nils Feldhus, Qianli Wang, Tatiana Anikina, Sahil Chopra, Cennet Oguz, Sebastian Möller
Natural Language Processing Data Set NLP Model Conversational Interface Dialogue Based Explanation Explanation Framework

October 2, 2023

It's MBR All the Way Down: Modern Generation Techniques Through the Lens of Minimum Bayes Risk
Amanda Bertsch, Alex Xie, Graham Neubig, Matthew R. Gormley
Camera Lens NLP Model Machine Learning System New Way Minimum Bayes Risk Simultaneous Improvement Generation Technique

September 29, 2023

Interpretable Long-Form Legal Question Answering with Retrieval-Augmented Large Language Models
Antoine Louis, Gijs van Dijck, Gerasimos Spanakis
Large Language Model NLP Model Legal Reasoning Long Form Answer Legal Question Answering

September 8, 2023

Can NLP Models 'Identify', 'Distinguish', and 'Justify' Questions that Don't have a Definitive Answer?
Ayushi Agarwal, Nisarg Patel, Neeraj Varshney, Mihir Parmar, Pavan Mallina, Aryan Bhavin Shah, Srihari Raju Sangaraju, Tirth Patel, Nihar Thakkar, Chitta Baral
Yes No Question NLP Model Top Two Answer Language Understanding Task

August 31, 2023

Thesis Distillation: Investigating The Impact of Bias in NLP Models on Hate Speech Detection
Fatma Elsafoury
Global Impact High Explainability Absolute Stance Bias Hate Speech Detection NLP Model NLP Community

July 11, 2023

June 27, 2023

Large Language Models as Annotators: Enhancing Generalization of NLP Models at Minimal Cost
Parikshit Bansal, Amit Sharma
Large Language Model Strong Generalization Active Learning NLP Model Individual Annotator LLM Annotation Minimal Cost

June 18, 2023

Efficiently Measuring the Cognitive Ability of LLMs: An Adaptive Testing Perspective
Yan Zhuang, Qi Liu, Yuting Ning, Weizhe Huang, Rui Lv, Zhenya Huang, Guanhao Zhao, Zheng Zhang, Qingyang Mao, Shijin Wang, Enhong Chen
Large Language Model Human Like NLP Model Cognitive Capability Adaptive Testing

June 8, 2023

June 7, 2023

World Models for Math Story Problems
Andreas Opedal, Niklas Stoehr, Abulhair Saparov, Mrinmaya Sachan
Language Model World Model NLP Model Math Word Problem Geometric Relationship

June 4, 2023

Taught by the Internet, Exploring Bias in OpenAIs GPT3
Ali Ayaz, Aditya Nawalgaria, Ruilian Yin
Absolute Stance Bias Gender Bias Natural Language Processing Model NLP Model

June 3, 2023

MultiLegalPile: A 689GB Multilingual Legal Corpus
Joel Niklaus, Veton Matoshi, Matthias Stürmer, Ilias Chalkidis, Daniel E. Ho
Multilingual Model NLP Model Monolingual Model

May 31, 2023

Measuring the Robustness of NLP Models to Domain Shifts
Nitay Calderon, Naveh Porat, Eyal Ben-David, Alexander Chapanin, Zorik Gekhman, Nadav Oved, Vitaly Shalumov, Roi Reichart
Native Robustness Domain Shift NLP Model Domain Performance Domain Robustness Challenging Domain

May 30, 2023

Fighting Bias with Bias: Promoting Model Robustness by Amplifying Dataset Biases
Yuval Reif, Roy Schwartz
Absolute Stance Bias NLP Model Model Robustness Dataset Bias Common Bias Space Bias Addressing Bias

NLP Model

Papers

The Validity of Evaluation Results: Assessing Concurrence Across Compositionality Benchmarks

Finite-context Indexing of Restricted Output Space for NLP Models Facing Noisy Input

PerturbScore: Connecting Discrete and Continuous Perturbations in NLP

Faithfulness Measurable Masked Language Models

InterroLang: Exploring NLP Models and Datasets through Dialogue-based Explanations

It's MBR All the Way Down: Modern Generation Techniques Through the Lens of Minimum Bayes Risk

Interpretable Long-Form Legal Question Answering with Retrieval-Augmented Large Language Models

Can NLP Models 'Identify', 'Distinguish', and 'Justify' Questions that Don't have a Definitive Answer?

Thesis Distillation: Investigating The Impact of Bias in NLP Models on Hate Speech Detection

Empowering Cross-lingual Behavioral Testing of NLP Models with Typological Features

BLUEX: A benchmark based on Brazilian Leading Universities Entrance eXams

Large Language Models as Annotators: Enhancing Generalization of NLP Models at Minimal Cost

Efficiently Measuring the Cognitive Ability of LLMs: An Adaptive Testing Perspective

Overview of the Problem List Summarization (ProbSum) 2023 Shared Task on Summarizing Patients' Active Diagnoses and Problems from Electronic Health Record Progress Notes

Assessing Phrase Break of ESL Speech with Pre-trained Language Models and Large Language Models

World Models for Math Story Problems

Taught by the Internet, Exploring Bias in OpenAIs GPT3

MultiLegalPile: A 689GB Multilingual Legal Corpus

Measuring the Robustness of NLP Models to Domain Shifts

Fighting Bias with Bias: Promoting Model Robustness by Amplifying Dataset Biases