12 citations · 29 across the 52 of their papers we have counts for
53 papers
ReHoPER: Receding-Horizon Planning for Enhanced Reasoning
Saeed Ahmadnia, Cornelia Caragea
We propose ReHoPER, an inference-only, zero-shot method that improves large language models' reasoning by generating and answering intermediate questions along multiple paths befor…
Scaling Performance and Low-Resource Annotation with Many-Shot In-Context Learning for Named Entity Recognition
Qi Zhang, Fangping Lan, Cornelia Caragea +2
In-context learning (ICL) with large language models (LLMs) has emerged as a powerful alternative to fine-tuning for Named Entity Recognition (NER), achieving strong performance wi…
CaliDist: Calibrating Large Language Models via Behavioral Robustness to Distraction
Mohammad Anas Jawad, Cornelia Caragea
Existing calibration methods for Large Language Models (LLMs) often overlook a critical dimension of trustworthiness: a model's behavioral robustness to irrelevant or misleading in…
LLM-guided Semi-Supervised Approaches for Social Media Crisis Data Classification
Jacob Ativo, Bharaneeshwar Balasubramaniyam, Anh Tran +4
Semi-supervised learning approaches have been investigated as a means to enhance the analysis of social media data in disaster management contexts. In this work, we present the fir…
BLooP: Zero-Shot Abstractive Summarization using Large Language Models with Bigram Lookahead Promotion
Varun Iyer, Cornelia Caragea
Abstractive summarization requires models to generate summaries that convey information in the source document. While large language models can generate summaries without fine-tuni…
The Shifting Landscape of Vaccine Discourse: Insights From a Decade of Pre- to Post-COVID-19 Vaccine Posts on Social Media
Nikesh Gyawali, Doina Caragea, Cornelia Caragea +1
In this work, we study English-language vaccine discourse in social media posts, specifically posts on X (formerly Twitter), in seven years before the COVID-19 outbreak (2013 to 20…
CalibrateMix: Guided-Mixup Calibration of Image Semi-Supervised Models
Mehrab Mustafy Rahman, Jayanth Mohan, Tiberiu Sosea +1
Semi-supervised learning (SSL) has demonstrated high performance in image classification tasks by effectively utilizing both labeled and unlabeled data. However, existing SSL metho…
Evaluating Large Language Models for Stance Detection on Financial Targets from SEC Filing Reports and Earnings Call Transcripts
Nikesh Gyawali, Doina Caragea, Alex Vasenkov +1
Financial narratives from U.S. Securities and Exchange Commission (SEC) filing reports and quarterly earnings call transcripts (ECTs) are very important for investors, auditors, an…
MADIAVE: Multi-Agent Debate for Implicit Attribute Value Extraction
Wei-Chieh Huang, Cornelia Caragea
Implicit Attribute Value Extraction (AVE) is essential for accurately representing products in e-commerce, as it infers latent attributes from multimodal data. Despite advances in…
LLM-Guided Co-Training for Text Classification
Md Mezbaur Rahman, Cornelia Caragea
In this paper, we introduce a novel weighted co-training approach that is guided by Large Language Models (LLMs). Namely, in our co-training approach, we use LLM labels on unlabele…
Towards AI-Assisted Research Writing: Benchmarking LLMs for AI/ML Introduction Generation
Krishna Garg, Firoz Shaik, Sambaran Bandyopadhyay +1
As researchers increasingly adopt LLMs as writing assistants, generating high-quality research paper introductions remains both challenging and essential. We introduce Scientific I…
A MISMATCHED Benchmark for Scientific Natural Language Inference
Firoz Shaik, Mobashir Sadat, Nikita Gautam +2
Scientific Natural Language Inference (NLI) is the task of predicting the semantic relation between a pair of sentences extracted from research articles. Existing datasets for this…
MultiMatch: Multihead Consistency Regularization Matching for Semi-Supervised Text Classification
Iustin Sirbu, Robert-Adrian Popovici, Cornelia Caragea +2
We introduce MultiMatch, a novel semi-supervised learning (SSL) algorithm combining the paradigms of co-training and consistency regularization with pseudo-labeling. At its core, M…
DynClean: Training Dynamics-based Label Cleaning for Distantly-Supervised Named Entity Recognition
Qi Zhang, Huitong Pan, Zhijia Chen +3
Distantly Supervised Named Entity Recognition (DS-NER) has attracted attention due to its scalability and ability to automatically generate labeled data. However, distant annotatio…
Zero-Shot Keyphrase Generation: Investigating Specialized Instructions and Multi-Sample Aggregation on Large Language Models
Jayanth Mohan, Jishnu Ray Chowdhury, Tomas Malik +1
Keyphrases are the essential topical phrases that summarize a document. Keyphrase generation is a long-standing NLP task for automatically generating keyphrases for a given documen…
Active Few-Shot Learning for Text Classification
Saeed Ahmadnia, Arash Yousefi Jordehi, Mahsa Hosseini Khasheh Heyran +3
The rise of Large Language Models (LLMs) has boosted the use of Few-Shot Learning (FSL) methods in natural language processing, achieving acceptable performance even when working w…
Zero-Shot Verification-guided Chain of Thoughts
Jishnu Ray Chowdhury, Cornelia Caragea
Previous works have demonstrated the effectiveness of Chain-of-Thought (COT) prompts and verifiers in guiding Large Language Models (LLMs) through the space of reasoning. However,…
SciER: An Entity and Relation Extraction Dataset for Datasets, Methods, and Tasks in Scientific Documents
Qi Zhang, Zhijia Chen, Huitong Pan +3
Scientific information extraction (SciIE) is critical for converting unstructured knowledge from scholarly articles into structured data (entities and relations). Several datasets…
Stanceformer: Target-Aware Transformer for Stance Detection
Krishna Garg, Cornelia Caragea
The task of Stance Detection involves discerning the stance expressed in a text towards a specific subject or target. Prior works have relied on existing transformer models that la…
How Hard is this Test Set? NLI Characterization by Exploiting Training Dynamics
Adrian Cosma, Stefan Ruseti, Mihai Dascalu +1
Natural Language Inference (NLI) evaluation is crucial for assessing language understanding models; however, popular datasets suffer from systematic spurious correlations that arti…
On the Design Space Between Transformers and Recursive Neural Nets
Jishnu Ray Chowdhury, Cornelia Caragea
In this paper, we study two classes of models, Recursive Neural Networks (RvNNs) and Transformers, and show that a tight connection between them emerges from the recent development…
FlowLearn: Evaluating Large Vision-Language Models on Flowchart Understanding
Huitong Pan, Qi Zhang, Cornelia Caragea +2
Flowcharts are graphical tools for representing complex concepts in concise visual representations. This paper introduces the FlowLearn dataset, a resource tailored to enhance the…
SciDMT: A Large-Scale Corpus for Detecting Scientific Mentions
Huitong Pan, Qi Zhang, Cornelia Caragea +2
We present SciDMT, an enhanced and expanded corpus for scientific mention detection, offering a significant advancement over existing related resources. SciDMT contains annotated s…
Co-training for Low Resource Scientific Natural Language Inference
Mobashir Sadat, Cornelia Caragea
Scientific Natural Language Inference (NLI) is the task of predicting the semantic relation between a pair of sentences extracted from research articles. The automatic annotation m…