7 citations · 18 across the 28 of their papers we have counts for
19 papers · 1 filter
MMGR: Multi-Modal Generative Reasoning Benchmark and Evaluation
Zefan Cai, Haoyi Qiu, Tianyi Ma +13
Modern multimodal generative models can synthesize visually compelling images and videos, but it remains unclear whether this visual fluency reflects genuine reasoning: when prompt…
Extracting Probabilistic Knowledge from Large Language Models for Bayesian Network Parameterization
Aliakbar Nafar, Kristen Brent Venable, Zijun Cui +1
In this work, we evaluate the potential of Large Language Models (LLMs) in building Bayesian Networks (BNs) by approximating domain expert priors. LLMs have demonstrated potential…
Learning vs Retrieval: The Role of In-Context Examples in Regression with Large Language Models
Aliakbar Nafar, Kristen Brent Venable, Parisa Kordjamshidi
Generative Large Language Models (LLMs) are capable of being in-context learners. However, the underlying mechanism of in-context learning (ICL) is still a major research question,…
Prompt2DeModel: Declarative Neuro-Symbolic Modeling with Natural Language
Hossein Rajaby Faghihi, Aliakbar Nafar, Andrzej Uszok +2
This paper presents a conversational pipeline for crafting domain knowledge for complex neuro-symbolic models through natural language prompts. It leverages large language models t…
NavHint: Vision and Language Navigation Agent with a Hint Generator
Yue Zhang, Quan Guo, Parisa Kordjamshidi
Existing work on vision and language navigation mainly relies on navigation-related losses to establish the connection between vision and language modalities, neglecting aspects of…
Reasoning over Uncertain Text by Generative Large Language Models
Aliakbar Nafar, Kristen Brent Venable, Parisa Kordjamshidi
This paper considers the challenges Large Language Models (LLMs) face when reasoning over text that includes information involving uncertainty explicitly quantified via probability…
Disentangling Extraction and Reasoning in Multi-hop Spatial Reasoning
Roshanak Mirzaee, Parisa Kordjamshidi
Spatial reasoning over text is challenging as the models not only need to extract the direct spatial information from the text but also reason over those and infer implicit spatial…
Teaching Probabilistic Logical Reasoning to Transformers
Aliakbar Nafar, Kristen Brent Venable, Parisa Kordjamshidi
In this paper, we evaluate the capability of transformer-based language models in making inferences over uncertain text that includes uncertain rules of reasoning. We cover both Pr…
VLN-Trans: Translator for the Vision and Language Navigation Agent
Yue Zhang, Parisa Kordjamshidi
Language understanding is essential for the navigation agent to follow instructions. We observe two kinds of issues in the instructions that can make the navigation task challengin…
The Role of Semantic Parsing in Understanding Procedural Text
Hossein Rajaby Faghihi, Parisa Kordjamshidi, Choh Man Teng +1
In this paper, we investigate whether symbolic semantic representations, extracted from deep semantic parsers, can help reasoning over the states of involved entities in a procedur…
Transfer Learning with Synthetic Corpora for Spatial Role Labeling and Reasoning
Roshanak Mirzaee, Parisa Kordjamshidi
Recent research shows synthetic data as a source of supervision helps pretrained language models (PLM) transfer learning to new target tasks/domains. However, this idea is less exp…
Dynamic Relevance Graph Network for Knowledge-Aware Question Answering
Chen Zheng, Parisa Kordjamshidi
This work investigates the challenge of learning and reasoning for Commonsense Question Answering given an external source of knowledge in the form of a knowledge graph (KG). We pr…
Relevant CommonSense Subgraphs for "What if..." Procedural Reasoning
Chen Zheng, Parisa Kordjamshidi
We study the challenge of learning causal reasoning over procedural text to answer "What if..." questions when external commonsense knowledge is required. We propose a novel multi-…
Relational Gating for "What If" Reasoning
Chen Zheng, Parisa Kordjamshidi
This paper addresses the challenge of learning to do procedural reasoning over text to answer "What if..." questions. We propose a novel relational gating network that learns to fi…
Towards Navigation by Reasoning over Spatial Configurations
Yue Zhang, Quan Guo, Parisa Kordjamshidi
We deal with the navigation problem where the agent follows natural language instructions while observing the environment. Focusing on language understanding, we show the importanc…
Time-Stamped Language Model: Teaching Language Models to Understand the Flow of Events
Hossein Rajaby Faghihi, Parisa Kordjamshidi
Tracking entities throughout a procedure described in a text is challenging due to the dynamic nature of the world described in the process. Firstly, we propose to formulate this t…
Latent Alignment of Procedural Concepts in Multimodal Recipes
Hossein Rajaby Faghihi, Roshanak Mirzaee, Sudarshan Paliwal +1
We propose a novel alignment mechanism to deal with procedural reasoning on a newly released multimodal QA dataset, named RecipeQA. Our model is solving the textual cloze task whic…
SRLGRN: Semantic Role Labeling Graph Reasoning Network
Chen Zheng, Parisa Kordjamshidi
This work deals with the challenge of learning and reasoning over multi-hop question answering (QA). We propose a graph reasoning network based on the semantic structure of the sen…
Cross-Modality Relevance for Reasoning on Language and Vision
Chen Zheng, Quan Guo, Parisa Kordjamshidi
This work deals with the challenge of learning and reasoning over language and vision data for the related downstream tasks such as visual question answering (VQA) and natural lang…