544 citations · 564 across the 14 of their papers we have counts for
14 papers
Who Judges the Judge? LLM Jury-on-Demand: Building Trustworthy LLM Evaluation Systems
Xiaochuan Li, Ke Wang, Girija Gouda +5
As Large Language Models (LLMs) become integrated into high-stakes domains, there is a growing need for evaluation methods that are both scalable for real-time deployment and relia…
Sequential Harmful Shift Detection Without Labels
Salim I. Amoukou, Tom Bewley, Saumitra Mishra +3
We introduce a novel approach for detecting distribution shifts that negatively impact the performance of machine learning models in continuous production environments, which requi…
Interpretable LLM-based Table Question Answering
Giang Nguyen, Ivan Brugere, Shubham Sharma +3
Interpretability in Table Question Answering (Table QA) is critical, especially in high-stakes domains like finance and healthcare. While recent Table QA approaches based on Large…
Interpreting Language Reward Models via Contrastive Explanations
Junqi Jiang, Tom Bewley, Saumitra Mishra +2
Reward models (RMs) are a crucial component in the alignment of large language models' (LLMs) outputs with human values. RMs approximate human preferences over possible LLM respons…
Graphusion: A RAG Framework for Knowledge Graph Construction with a Global Perspective
Rui Yang, Boming Yang, Aosong Feng +7
Knowledge Graphs (KGs) are crucial in the field of artificial intelligence and are widely used in downstream tasks, such as question-answering (QA). The construction of KGs typical…
Graphusion: Leveraging Large Language Models for Scientific Knowledge Graph Fusion and Construction in NLP Education
Rui Yang, Boming Yang, Sixun Ouyang +6
Knowledge graphs (KGs) are crucial in the field of artificial intelligence and are widely applied in downstream tasks, such as enhancing Question Answering (QA) systems. The constr…
Quantifying Prediction Consistency Under Fine-Tuning Multiplicity in Tabular LLMs
Faisal Hamman, Pasan Dissanayake, Saumitra Mishra +2
Fine-tuning LLMs on tabular classification tasks can lead to the phenomenon of fine-tuning multiplicity where equally well-performing models make conflicting predictions on the sam…
TacoERE: Cluster-aware Compression for Event Relation Extraction
Yong Guan, Xiaozhi Wang, Lei Hou +4
Event relation extraction (ERE) is a critical and fundamental challenge for natural language processing. Existing work mainly focuses on directly modeling the entire document, whic…
KnowHalu: Hallucination Detection via Multi-Form Knowledge Based Factual Checking
Jiawei Zhang, Chejian Xu, Yu Gai +3
This paper introduces KnowHalu, a novel approach for detecting hallucinations in text generated by large language models (LLMs), utilizing step-wise reasoning, multi-formulation qu…
Leveraging Large Language Models for Concept Graph Recovery and Question Answering in NLP Education
Rui Yang, Boming Yang, Sixun Ouyang +6
In the domain of Natural Language Processing (NLP), Large Language Models (LLMs) have demonstrated promise in text-generation tasks. However, their educational applications, partic…
Knowledge-Aware Neuron Interpretation for Scene Classification
Yong Guan, Freddy Lecue, Jiaoyan Chen +2
Although neural models have achieved remarkable performance, they still encounter doubts due to the intransparency. To this end, model prediction explanation is attracting more and…
Better Explain Transformers by Illuminating Important Information
Linxin Song, Yan Cui, Ao Luo +2
Transformer-based models excel in various natural language processing (NLP) tasks, attracting countless efforts to explain their inner workings. Prior methods explain Transformers…
Privacy-Preserving Algorithmic Recourse
Sikha Pentyala, Shubham Sharma, Sanjay Kariyappa +2
When individuals are subject to adverse outcomes from machine learning models, providing a recourse path to help achieve a positive outcome is desirable. Recent work has shown that…
Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions
Luca Longo, Mario Brcic, Federico Cabitza +16
As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In respo…