16 citations · 51 across the 18 of their papers we have counts for
8 papers · 1 filter
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…
SURFACEBENCH: A Geometry-Aware Benchmark for Symbolic Surface Discovery
Sanchit Kabra, Shobhnik Kriplani, Parshin Shojaee +1
Equation discovery from data is a central challenge in machine learning for science, which requires the recovery of concise symbolic expressions that govern complex physical and ge…
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…
The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity
Parshin Shojaee, Iman Mirzadeh, Keivan Alizadeh +3
Recent generations of language models have introduced Large Reasoning Models (LRMs) that generate detailed thinking processes before providing answers. While these models demonstra…
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…
Federated Retrieval Augmented Generation for Multi-Product Question Answering
Parshin Shojaee, Sai Sree Harsha, Dan Luo +3
Recent advancements in Large Language Models and Retrieval-Augmented Generation have boosted interest in domain-specific question-answering for enterprise products. However, AI Ass…