3 citations · 8 across the 10 of their papers we have counts for
10 papers
Be Consistent! Enhancing Robust Visual Reasoning in LVLMs with Consistency Constraints
Liqiang Jing, Xiong Zhou, Siddharth Varia +3
While Large Vision-Language Models (LVLMs) exhibit strong perceptual capabilities, they remain vulnerable in visual reasoning tasks. Existing benchmarks largely focus on symbolic m…
Towards Long Context Hallucination Detection
Siyi Liu, Kishaloy Halder, Zheng Qi +6
Large Language Models (LLMs) have demonstrated remarkable performance across various tasks. However, they are prone to contextual hallucination, generating information that is eith…
Unraveling and Mitigating Safety Alignment Degradation of Vision-Language Models
Qin Liu, Chao Shang, Ling Liu +7
The safety alignment ability of Vision-Language Models (VLMs) is prone to be degraded by the integration of the vision module compared to its LLM backbone. We investigate this phen…
Open Domain Question Answering with Conflicting Contexts
Siyi Liu, Qiang Ning, Kishaloy Halder +8
Open domain question answering systems frequently rely on information retrieved from large collections of text (such as the Web) to answer questions. However, such collections of t…
General Purpose Verification for Chain of Thought Prompting
Robert Vacareanu, Anurag Pratik, Evangelia Spiliopoulou +6
Many of the recent capabilities demonstrated by Large Language Models (LLMs) arise primarily from their ability to exploit contextual information. In this paper, we explore ways to…
Characterizing and Measuring Linguistic Dataset Drift
Tyler A. Chang, Kishaloy Halder, Neha Anna John +4
NLP models often degrade in performance when real world data distributions differ markedly from training data. However, existing dataset drift metrics in NLP have generally not con…
Taxonomy Expansion for Named Entity Recognition
Karthikeyan K, Yogarshi Vyas, Jie Ma +7
Training a Named Entity Recognition (NER) model often involves fixing a taxonomy of entity types. However, requirements evolve and we might need the NER model to recognize addition…
A Weak Supervision Approach for Few-Shot Aspect Based Sentiment
Robert Vacareanu, Siddharth Varia, Kishaloy Halder +5
We explore how weak supervision on abundant unlabeled data can be leveraged to improve few-shot performance in aspect-based sentiment analysis (ABSA) tasks. We propose a pipeline a…
Comparing Biases and the Impact of Multilingual Training across Multiple Languages
Sharon Levy, Neha Anna John, Ling Liu +6
Studies in bias and fairness in natural language processing have primarily examined social biases within a single language and/or across few attributes (e.g. gender, race). However…
Dynamic Benchmarking of Masked Language Models on Temporal Concept Drift with Multiple Views
Katerina Margatina, Shuai Wang, Yogarshi Vyas +3
Temporal concept drift refers to the problem of data changing over time. In NLP, that would entail that language (e.g. new expressions, meaning shifts) and factual knowledge (e.g.…