2 citations · 5 across the 9 of their papers we have counts for
9 papers
Analyzing LLM Reasoning to Uncover Mental Health Stigma
Sreehari Sankar, Aliakbar Nafar, Mona Barman +8
While large language models (LLMs) are increasingly being explored for mental health applications, recent studies reveal that they can exhibit stigma toward individuals with psycho…
An Agentic Framework for Neuro-Symbolic Programming
Aliakbar Nafar, Chetan Chigurupati, Danial Kamali +2
Integrating symbolic constraints into deep learning models could make them more robust, interpretable, and data-efficient. Still, it remains a time-consuming and challenging task.…
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…
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…
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…
GLUECons: A Generic Benchmark for Learning Under Constraints
Hossein Rajaby Faghihi, Aliakbar Nafar, Chen Zheng +7
Recent research has shown that integrating domain knowledge into deep learning architectures is effective -- it helps reduce the amount of required data, improves the accuracy of t…
DomiKnowS: A Library for Integration of Symbolic Domain Knowledge in Deep Learning
Hossein Rajaby Faghihi, Quan Guo, Andrzej Uszok +3
We demonstrate a library for the integration of domain knowledge in deep learning architectures. Using this library, the structure of the data is expressed symbolically via graph d…