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Amir Hassan Shariatmadari

2 accepted papers

2025

InfAL: Inference Time Adversarial Learning for Improving Research Ideation

EMNLP 2025

Advancements in Large Language Models (LLMs) have opened new opportunities for scientific discovery by assisting researchers in generating novel hypotheses and ideas. In this process, a major challenge is how to optimally and efficiently utilize LLMs’ parametric knowledge obtained from their pretrai

Cited by 0SourcePDFScholar
2025

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models

IJCAI 2025

Large language models (LLMs) have shown significant potential in scientific disciplines such as biomedicine, particularly in hypothesis generation, where they can analyze vast literature, identify patterns, and suggest research directions. However, a key challenge lies in evaluating the truthfulness