large language models

Effects of Shot Size on Social Impressions in Humans and Multimodal AI

The same person can be judged differently depending on how tightly they are framed. As image- and video-based evaluations become increasingly common in settings such as hiring, screening, and interviews, this raises a practical question: does shot size change social-impression ratings in human observers and a multimoda...

Narrating Authority Through Storytelling in Entrepreneurial Podcast Interviews

How do entrepreneurs claim the authority to lead an enterprise that was never theirs alone? Although the discursive construction of leadership has attracted growing interest, most existing work is qualitative and case-limited, and leadership authority has rarely been examined as a discursive phenomenon at scale. Drawin...

Explainable Hallucination Detection in Large Language Models Using Evidence-Grounded Verification

This paper presents Evidence-Grounded Verification (EGV), a modular framework for explainable hallucination detection in large language models. EGV decomposes model outputs into atomic claims, retrieves independent evidence, performs fine-grained four-way verification (Supported, Partially Supported, Contradicted, Unve...

Narrating Authority Through Storytelling in Entrepreneurial Podcast Interviews

How do entrepreneurs claim the authority to lead an enterprise that was never theirs alone? Although the discursive construction of leadership has attracted growing interest, most existing work is qualitative and case-limited, and leadership authority has rarely been examined as a discursive phenomenon at scale. Drawin...

Explainable Hallucination Detection in Large Language Models Using Evidence-Grounded Verification

This paper presents Evidence-Grounded Verification (EGV), a modular framework for explainable hallucination detection in large language models. EGV decomposes model outputs into atomic claims, retrieves independent evidence, performs fine-grained four-way verification (Supported, Partially Supported, Contradicted, Unve...

Decoding protein mutational fitness through evolutionary sequence intelligence: a large-scale ESM-2 benchmark across 217 DMS assays

Development and benchmark validation of PubChat for PubMed-grounded multilingual biomedical literature retrieval

The rapid growth of biomedical literature has rendered traditional systematic reviews unsustainable. Although large language models (LLMs) offer automation potential, citation fabrication and unreliable evidence discrimination remain critical barriers. Here, using PubChat as a PubMed E-utilities-grounded retrieval fram...

Iterated Agent for Symbolic Regression

This paper introduces IdeaSearchFitter, a framework that employs Large Language Models (LLMs) as semantic operators within an evolutionary search that achieves competitive, noise-robust performance on the Feynman Symbolic Regression Database (FSReD), outperforming several strong baselines.

Fixing, Breaking, or Faking It? An Execution-Calibrated Evaluation of LLM Vulnerability Patching in JavaScript, Python, Go, and Java, and the Limits of LLM-as-Judge

Large language models (LLMs) increasingly repair software vulnerabilities, but most evaluations judge only similarity to a developer fix or removal of the weakness, leaving no single judge trustworthy.

Integrating large language models for automated structural analysis

Automated analysis for engineering structures offers considerable potential for boosting efficiency by minimizing repetitive tasks. Although AI-driven methods are increasingly common, no systematic framework yet leverages Large Language Models (LLMs) for automatic structural analysis. This paper proposes a framework th...

Toward scalable generative AI: efficient language model distillation via zero-shot rationales

Results indicate that near-optimal performance can be achieved even when rationales are provided for only a subset of the training data, and that shorter explanations are often sufficient.

Don’t Look up: Evaluating the Tradeoff Between Performance and Sustainability of Text Classification Using Open LLMs

The increasing adoption of Large Language Models (LLMs) as a text analysis method in social science presents a critical yet under-examined trade-off between model performance and environmental sustainability. This research provides a systematic evaluation comparing the performance, energy consumption, processing time...