8 citations · 32 across the 17 of their papers we have counts for
18 papers
Searching for What Matters: Open-Ended Discovery of Multiple Hypotheses with Agentic Data Exploration
Sanchit Kabra, Nurendra Choudhary, Jiangshu Du +3
Discovering what drives an outcome from data is central to science and often requires deciding what is worth investigating before a specific hypothesis is known. Yet most existing…
Evaluating Large Language Models in Scientific Discovery
Zhangde Song, Jieyu Lu, Yuanqi Du +53
Large language models (LLMs) are increasingly applied to scientific research, yet prevailing science benchmarks probe decontextualized knowledge and overlook the iterative reasonin…
Discovering Heuristics with Large Language Models (LLMs) for Mixed-Integer Programs: Single-Machine Scheduling
İbrahim Oğuz Çetinkaya, İ. Esra Büyüktahtakın, Parshin Shojaee +1
Our study contributes to the scheduling and combinatorial optimization literature with new heuristics discovered by leveraging the power of Large Language Models (LLMs). We focus o…
Sycophancy Mitigation Through Reinforcement Learning with Uncertainty-Aware Adaptive Reasoning Trajectories
Mohammad Beigi, Ying Shen, Parshin Shojaee +5
Despite the remarkable capabilities of large language models, current training paradigms inadvertently foster \textit{sycophancy}, i.e., the tendency of a model to agree with or re…
LLM-SRBench: A New Benchmark for Scientific Equation Discovery with Large Language Models
Parshin Shojaee, Ngoc-Hieu Nguyen, Kazem Meidani +3
Scientific equation discovery is a fundamental task in the history of scientific progress, enabling the derivation of laws governing natural phenomena. Recently, Large Language Mod…
LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers
Nikhil Abhyankar, Parshin Shojaee, Chandan K. Reddy
Automated feature engineering plays a critical role in improving predictive model performance for tabular learning tasks. Traditional automated feature engineering methods are limi…
Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges
Chandan K Reddy, Parshin Shojaee
Scientific discovery is a complex cognitive process that has driven human knowledge and technological progress for centuries. While artificial intelligence (AI) has made significan…
Biased or Flawed? Mitigating Stereotypes in Generative Language Models by Addressing Task-Specific Flaws
Akshita Jha, Sanchit Kabra, Chandan K. Reddy
Recent studies have shown that generative language models often reflect and amplify societal biases in their outputs. However, these studies frequently conflate observed biases wit…
Mitigating Selection Bias with Node Pruning and Auxiliary Options
Hyeong Kyu Choi, Weijie Xu, Chi Xue +2
Large language models (LLMs) often exhibit systematic preferences for certain answer choices when responding to multiple-choice questions-a behavior known as selection bias. This b…
H-STAR: LLM-driven Hybrid SQL-Text Adaptive Reasoning on Tables
Nikhil Abhyankar, Vivek Gupta, Dan Roth +1
Tabular reasoning involves interpreting natural language queries about tabular data, which presents a unique challenge of combining language understanding with structured data anal…
Synthesizing Conversations from Unlabeled Documents using Automatic Response Segmentation
Fanyou Wu, Weijie Xu, Chandan K. Reddy +1
In this study, we tackle the challenge of inadequate and costly training data that has hindered the development of conversational question answering (ConvQA) systems. Enterprises h…
Context-Aware Clustering using Large Language Models
Sindhu Tipirneni, Ravinarayana Adkathimar, Nurendra Choudhary +5
Despite the remarkable success of Large Language Models (LLMs) in text understanding and generation, their potential for text clustering tasks remains underexplored. We observed th…
LLM-SR: Scientific Equation Discovery via Programming with Large Language Models
Parshin Shojaee, Kazem Meidani, Shashank Gupta +2
Mathematical equations have been unreasonably effective in describing complex natural phenomena across various scientific disciplines. However, discovering such insightful equation…
An Interpretable Ensemble of Graph and Language Models for Improving Search Relevance in E-Commerce
Nurendra Choudhary, Edward W Huang, Karthik Subbian +1
The problem of search relevance in the E-commerce domain is a challenging one since it involves understanding the intent of a user's short nuanced query and matching it with the ap…
Discovery of Generalizable TBI Phenotypes Using Multivariate Time-Series Clustering
Hamid Ghaderi, Brandon Foreman, Chandan K. Reddy +1
Traumatic Brain Injury (TBI) presents a broad spectrum of clinical presentations and outcomes due to its inherent heterogeneity, leading to diverse recovery trajectories and varied…
ViSAGe: A Global-Scale Analysis of Visual Stereotypes in Text-to-Image Generation
Akshita Jha, Vinodkumar Prabhakaran, Remi Denton +5
Recent studies have shown that Text-to-Image (T2I) model generations can reflect social stereotypes present in the real world. However, existing approaches for evaluating stereotyp…
Hyperbolic Graph Neural Networks at Scale: A Meta Learning Approach
Nurendra Choudhary, Nikhil Rao, Chandan K. Reddy
The progress in hyperbolic neural networks (HNNs) research is hindered by their absence of inductive bias mechanisms, which are essential for generalizing to new tasks and facilita…
SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training
Kazem Meidani, Parshin Shojaee, Chandan K. Reddy +1
In an era where symbolic mathematical equations are indispensable for modeling complex natural phenomena, scientific inquiry often involves collecting observations and translating…