12 citations · 49 across the 21 of their papers we have counts for
22 papers
Investigating VLM Hallucination from a Cognitive Psychology Perspective: A First Step Toward Interpretation with Intriguing Observations
Xiangrui Liu, Man Luo, Agneet Chatterjee +3
Hallucination is a long-standing problem that has been actively investigated in Vision-Language Models (VLMs). Existing research commonly attributes hallucinations to technical lim…
LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models
Mihir Parmar, Nisarg Patel, Neeraj Varshney +5
Recently developed large language models (LLMs) have been shown to perform remarkably well on a wide range of language understanding tasks. But, can they really "reason" over the n…
Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models
Man Luo, Shrinidhi Kumbhar, Ming shen +5
Logical reasoning is fundamental for humans yet presents a substantial challenge in the domain of Artificial Intelligence. Initially, researchers used Knowledge Representation and…
MDDial: A Multi-turn Differential Diagnosis Dialogue Dataset with Reliability Evaluation
Srija Macherla, Man Luo, Mihir Parmar +1
Dialogue systems for Automatic Differential Diagnosis (ADD) have a wide range of real-life applications. These dialogue systems are promising for providing easy access and reducing…
End-to-end Knowledge Retrieval with Multi-modal Queries
Man Luo, Zhiyuan Fang, Tejas Gokhale +2
We investigate knowledge retrieval with multi-modal queries, i.e. queries containing information split across image and text inputs, a challenging task that differs from previous w…
Dr.ICL: Demonstration-Retrieved In-context Learning
Man Luo, Xin Xu, Zhuyun Dai +5
In-context learning (ICL), teaching a large language model (LLM) to perform a task with few-shot demonstrations rather than adjusting the model parameters, has emerged as a strong…
Can NLP Models Correctly Reason Over Contexts that Break the Common Assumptions?
Neeraj Varshney, Mihir Parmar, Nisarg Patel +4
Pre-training on large corpora of text enables the language models to acquire a vast amount of factual and commonsense knowledge which allows them to achieve remarkable performance…
Can Open-Domain QA Reader Utilize External Knowledge Efficiently like Humans?
Neeraj Varshney, Man Luo, Chitta Baral
Recent state-of-the-art open-domain QA models are typically based on a two stage retriever-reader approach in which the retriever first finds the relevant knowledge/passages and th…
A Study on the Efficiency and Generalization of Light Hybrid Retrievers
Man Luo, Shashank Jain, Anchit Gupta +6
Hybrid retrievers can take advantage of both sparse and dense retrievers. Previous hybrid retrievers leverage indexing-heavy dense retrievers. In this work, we study "Is it possibl…
BioTABQA: Instruction Learning for Biomedical Table Question Answering
Man Luo, Sharad Saxena, Swaroop Mishra +2
Table Question Answering (TQA) is an important but under-explored task. Most of the existing QA datasets are in unstructured text format and only few of them use tables as the cont…
Neural Retriever and Go Beyond: A Thesis Proposal
Man Luo
Information Retriever (IR) aims to find the relevant documents (e.g. snippets, passages, and articles) to a given query at large scale. IR plays an important role in many tasks suc…
In-BoXBART: Get Instructions into Biomedical Multi-Task Learning
Mihir Parmar, Swaroop Mishra, Mirali Purohit +3
Single-task models have proven pivotal in solving specific tasks; however, they have limitations in real-world applications where multi-tasking is necessary and domain shifts are e…
Improving Contrastive Learning with Model Augmentation
Zhiwei Liu, Yongjun Chen, Jia Li +3
The sequential recommendation aims at predicting the next items in user behaviors, which can be solved by characterizing item relationships in sequences. Due to the data sparsity a…
Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness
Tejas Gokhale, Swaroop Mishra, Man Luo +2
Data modification, either via additional training datasets, data augmentation, debiasing, and dataset filtering, has been proposed as an effective solution for generalizing to out-…
Choose Your QA Model Wisely: A Systematic Study of Generative and Extractive Readers for Question Answering
Man Luo, Kazuma Hashimoto, Semih Yavuz +3
While both extractive and generative readers have been successfully applied to the Question Answering (QA) task, little attention has been paid toward the systematic comparison of…
Improving Biomedical Information Retrieval with Neural Retrievers
Man Luo, Arindam Mitra, Tejas Gokhale +1
Information retrieval (IR) is essential in search engines and dialogue systems as well as natural language processing tasks such as open-domain question answering. IR serve an impo…
Weakly-Supervised Visual-Retriever-Reader for Knowledge-based Question Answering
Man Luo, Yankai Zeng, Pratyay Banerjee +1
Knowledge-based visual question answering (VQA) requires answering questions with external knowledge in addition to the content of images. One dataset that is mostly used in evalua…
A Simple Approach to Jointly Rank Passages and Select Relevant Sentences in the OBQA Context
Man Luo, Shuguang Chen, Chitta Baral
In the open book question answering (OBQA) task, selecting the relevant passages and sentences from distracting information is crucial to reason the answer to a question. HotpotQA…
'Just because you are right, doesn't mean I am wrong': Overcoming a Bottleneck in the Development and Evaluation of Open-Ended Visual Question Answering (VQA) Tasks
Man Luo, Shailaja Keyur Sampat, Riley Tallman +4
GQA~\citep{hudson2019gqa} is a dataset for real-world visual reasoning and compositional question answering. We found that many answers predicted by the best vision-language models…
Can Transformers Reason About Effects of Actions?
Pratyay Banerjee, Chitta Baral, Man Luo +4
A recent work has shown that transformers are able to "reason" with facts and rules in a limited setting where the rules are natural language expressions of conjunctions of conditi…
Strong Equivalence for LPMLN Programs
Joohyung Lee, Man Luo
LPMLN is a probabilistic extension of answer set programs with the weight scheme adapted from Markov Logic. We study the concept of strong equivalence in LPMLN, which is a useful m…
Strong equivalence for programs
Man Luo
Strong equivalence is a well-studied and important concept in answer set programming (ASP). is a probabilistic extension of answer set programs with the weight schem…