Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- California Southern UniversityUS710 papers
- California Institute of TechnologyUS316 papers
- Centre National de la Recherche ScientifiqueFR302 papers
- University of California, BerkeleyUS250 papers
- Stanford UniversityUS230 papers
- Commissariat à l'Énergie Atomique et aux Énergies AlternativesFR221 papers
- Princeton UniversityUS220 papers
- University of CambridgeGB180 papers
- University of Illinois Urbana-ChampaignUS178 papers
- Lawrence Berkeley National LaboratoryUS172 papers
- University of California, Santa BarbaraUS172 papers
- CEA Paris-SaclayFR170 papers
142 papers · 1 filter
Side-by-side Comparison Amplifies Dialect Bias in Language Models
Kritee Kondapally, Claire J. Smerdon, Pooja C. Patel +5
Language models (LMs) can exhibit biases based on variations in their dialects, even in the absence of a dialect label, a behavior known as covert dialect bias. In this work, we qu…
Geometric Deviation as an Unsupervised Pre-Generation Reliability Signal: Probing LLM Representations for Answerability
Yucheng Du
A reliable language model should be able to signal, prior to generation, when a query falls outside its knowledge. We investigate whether representation geometry can provide such a…
No Free Lunch in Language Model Bias Mitigation? Targeted Bias Reduction Can Exacerbate Unmitigated LLM Biases
Shireen Chand, Faith Baca, Emilio Ferrara
Large Language Models (LLMs) inherit societal biases from their training data, potentially leading to harmful or unfair outputs. While various techniques aim to mitigate these bias…
Large Language Models Do Multi-Label Classification Differently
Marcus Ma, Georgios Chochlakis, Niyantha Maruthu Pandiyan +2
Multi-label classification is prevalent in real-world settings, but the behavior of Large Language Models (LLMs) in this setting is understudied. We investigate how autoregressive…
Humans Hallucinate Too: Language Models Identify and Correct Subjective Annotation Errors With Label-in-a-Haystack Prompts
Georgios Chochlakis, Peter Wu, Arjun Bedi +3
Modeling complex subjective tasks in Natural Language Processing, such as recognizing emotion and morality, is considerably challenging due to significant variation in human annota…
Limited Effectiveness of LLM-based Data Augmentation for COVID-19 Misinformation Stance Detection
Eun Cheol Choi, Ashwin Balasubramanian, Jinhu Qi +1
Misinformation surrounding emerging outbreaks poses a serious societal threat, making robust countermeasures essential. One promising approach is stance detection (SD), which ident…
Network-informed Prompt Engineering against Organized Astroturf Campaigns under Extreme Class Imbalance
Nikos Kanakaris, Heng Ping, Xiongye Xiao +4
Detecting organized political campaigns is of paramount importance in fighting against disinformation on social media. Existing approaches for the identification of such organized…
The Shrinking Landscape of Linguistic Diversity in the Age of Large Language Models
Zhivar Sourati, Farzan Karimi-Malekabadi, Meltem Ozcan +7
Language is far more than a communication tool; it encodes a wealth of information about a person's identity, psychological state, and social context, providing valuable insights f…
Aggregation Artifacts in Subjective Tasks Collapse Large Language Models' Posteriors
Georgios Chochlakis, Alexandros Potamianos, Kristina Lerman +1
In-context Learning (ICL) has become the primary method for performing natural language tasks with Large Language Models (LLMs). The knowledge acquired during pre-training is cruci…
Retrieval-Reasoning Large Language Model-based Synthetic Clinical Trial Generation
Zerui Xu, Fang Wu, Yingzhou Lu +2
Machine learning (ML) holds great promise for clinical applications but is often hindered by limited access to high-quality data due to privacy concerns, high costs, and long timel…
Overview of the First Shared Task on Clinical Text Generation: RRG24 and "Discharge Me!"
Justin Xu, Zhihong Chen, Andrew Johnston +9
Recent developments in natural language generation have tremendous implications for healthcare. For instance, state-of-the-art systems could automate the generation of sections in…
Improving the Capabilities of Large Language Model Based Marketing Analytics Copilots With Semantic Search And Fine-Tuning
Yilin Gao, Sai Kumar Arava, Yancheng Li +1
Artificial intelligence (AI) is widely deployed to solve problems related to marketing attribution and budget optimization. However, AI models can be quite complex, and it can be d…
The Strong Pull of Prior Knowledge in Large Language Models and Its Impact on Emotion Recognition
Georgios Chochlakis, Alexandros Potamianos, Kristina Lerman +1
In-context Learning (ICL) has emerged as a powerful paradigm for performing natural language tasks with Large Language Models (LLM) without updating the models' parameters, in cont…
Towards a clinically accessible radiology foundation model: open-access and lightweight, with automated evaluation
Juan Manuel Zambrano Chaves, Shih-Cheng Huang, Yanbo Xu +24
The scaling laws and extraordinary performance of large foundation models motivate the development and utilization of such models in biomedicine. However, despite early promising r…
Remember what you did so you know what to do next
Manuel R. Ciosici, Alex Hedges, Yash Kankanampati +3
We explore using a moderately sized large language model (GPT-J 6B parameters) to create a plan for a simulated robot to achieve 30 classes of goals in ScienceWorld, a text game si…
CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation
Minzhi Li, Taiwei Shi, Caleb Ziems +4
Annotated data plays a critical role in Natural Language Processing (NLP) in training models and evaluating their performance. Given recent developments in Large Language Models (L…
Factuality Challenges in the Era of Large Language Models
Isabelle Augenstein, Timothy Baldwin, Meeyoung Cha +15
The emergence of tools based on Large Language Models (LLMs), such as OpenAI's ChatGPT, Microsoft's Bing Chat, and Google's Bard, has garnered immense public attention. These incre…
Finding Pragmatic Differences Between Disciplines
Lee Kezar, Jay Pujara
Scholarly documents have a great degree of variation, both in terms of content (semantics) and structure (pragmatics). Prior work in scholarly document understanding emphasizes sem…
The Sem-Lex Benchmark: Modeling ASL Signs and Their Phonemes
Lee Kezar, Elana Pontecorvo, Adele Daniels +6
Sign language recognition and translation technologies have the potential to increase access and inclusion of deaf signing communities, but research progress is bottlenecked by a l…
The Unequal Opportunities of Large Language Models: Revealing Demographic Bias through Job Recommendations
Abel Salinas, Parth Vipul Shah, Yuzhong Huang +2
Large Language Models (LLMs) have seen widespread deployment in various real-world applications. Understanding these biases is crucial to comprehend the potential downstream conseq…
An Overview on Language Models: Recent Developments and Outlook
Chengwei Wei, Yun-Cheng Wang, Bin Wang +1
Language modeling studies the probability distributions over strings of texts. It is one of the most fundamental tasks in natural language processing (NLP). It has been widely used…
Multimodal Speech Recognition for Language-Guided Embodied Agents
Allen Chang, Xiaoyuan Zhu, Aarav Monga +3
Benchmarks for language-guided embodied agents typically assume text-based instructions, but deployed agents will encounter spoken instructions. While Automatic Speech Recognition…
Using Emotion Embeddings to Transfer Knowledge Between Emotions, Languages, and Annotation Formats
Georgios Chochlakis, Gireesh Mahajan, Sabyasachee Baruah +3
The need for emotional inference from text continues to diversify as more and more disciplines integrate emotions into their theories and applications. These needs include inferrin…
BEBERT: Efficient and Robust Binary Ensemble BERT
Jiayi Tian, Chao Fang, Haonan Wang +1
Pre-trained BERT models have achieved impressive accuracy on natural language processing (NLP) tasks. However, their excessive amount of parameters hinders them from efficient depl…