AIConfPaper
← Search

Michael Glass

7 accepted papers

2025

SIMBA UQ: Similarity-Based Aggregation for Uncertainty Quantification in Large Language Models

Debarun Bhattacharjya, Balaji Ganesan, Junkyu Lee, Radu Marinescu, Katya Mirylenka, Michael Glass +1
EMNLP 2025

When does a large language model (LLM) know what it does not know? Uncertainty quantification (UQ) provides measures of uncertainty, such as an estimate of the confidence in an LLM’s generated output, and is therefore increasingly recognized as a crucial component of trusted AI systems. Black-box UQ

Cited by 0Source ↗PDF ↗Scholar ↗
2025

The Consistency Hypothesis in Uncertainty Quantification for Large Language Models

Quan Xiao, Debarun Bhattacharjya, Balaji Ganesan, Radu Marinescu, Katya Mirylenka, Nhan H Pham +2
UAI 2025

Estimating the confidence of large language model (LLM) outputs is essential for real-world applications requiring high user trust. Black-box uncertainty quantification (UQ) methods, relying solely on model API access, have gained popularity due to their practical benefits. In this paper, we examine

Cited by 0Source ↗PDF ↗Scholar ↗
2023

Retrieval-Based Transformer for Table Augmentation

Michael Glass, Xueqing Wu, Ankita Rajaram Naik, Gaetano Rossiello, Alfio Gliozzo
ACL 2023findings

Data preparation, also called data wrangling, is considered one of the most expensive and time-consuming steps when performing analytics or building machine learning models. Preparing data typically involves collecting and merging data from complex heterogeneous, and often large-scale data sources,…

Cited by 9Source ↗PDF ↗code ↗Scholar ↗
2022

AIT-QA: Question Answering Dataset over Complex Tables in the Airline Industry

Yannis Katsis, Saneem Chemmengath, Vishwajeet Kumar, Samarth Bharadwaj, Mustafa Canim, Michael Glass +5
NAACL 2022industry

Table Question Answering (Table QA) systems have been shown to be highly accurate when trained and tested on open-domain datasets built on top of Wikipedia tables. However, it is not clear whether their performance remains the same when applied to domain-specific scientific and business documents, e…

Cited by 40Source ↗PDF ↗code ↗Scholar ↗
2022

Re2G: Retrieve, Rerank, Generate

Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Ankita Naik, Pengshan Cai, Alfio Gliozzo
NAACL 2022long

As demonstrated by GPT-3 and T5, transformers grow in capability as parameter spaces become larger and larger. However, for tasks that require a large amount of knowledge, non-parametric memory allows models to grow dramatically with a sub-linear increase in computational cost and GPU memory require…

Cited by 145Source ↗PDF ↗code ↗Scholar ↗
2021

Capturing Row and Column Semantics in Transformer Based Question Answering over Tables

Michael Glass, Mustafa Canim, Alfio Gliozzo, Saneem Chemmengath, Vishwajeet Kumar, Rishav Chakravarti +4
NAACL 2021long

Transformer based architectures are recently used for the task of answering questions over tables. In order to improve the accuracy on this task, specialized pre-training techniques have been developed and applied on millions of open-domain web tables. In this paper, we propose two novel approaches…

Cited by 18Source ↗PDF ↗code ↗Scholar ↗
2021

Robust Retrieval Augmented Generation for Zero-shot Slot Filling

Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Alfio Gliozzo
EMNLP 2021main

Automatically inducing high quality knowledge graphs from a given collection of documents still remains a challenging problem in AI. One way to make headway for this problem is through advancements in a related task known as slot filling. In this task, given an entity query in form of [Entity, Slot,…

Cited by 39Source ↗PDF ↗code ↗Scholar ↗