arXiv subjects

Emre Sezgin

Publications and source records attributed to Emre Sezgin.

3 recordsLinked to original sources

arXiv2026

Evaluating Large Language Models for Abstract Evaluation Tasks: An Empirical Study

Introduction: Large language models (LLMs) can process requests and generate texts, but their feasibility for assessing complex academic content needs further investigation. To explore LLM's potential in assisting scientific review, this study examined ChatGPT-5, Gemini-3-Pro, and Claude-Sonnet-4.5's consistency and reliability in evaluating abstracts compared to one another and to human reviewers. Methods: 160 abstracts from a local conference were graded by human reviewers and three LLMs using one rubric. Composite score distributions across three LLMs and fourteen reviewers were examined. Inter-rater reliability was calculated using intraclass correlation coefficients (ICCs) for within-AI reliability and AI-human concordance. Bland-Altman plots were examined for visual agreement patterns and systematic bias. Results: LLMs achieved good-to-excellent agreement with each other (ICCs: 0.59-0.87). ChatGPT and Claude reached moderate agreement with human reviewers on overall quality and content-specific criteria, with ICCs ~.45-.60 for composite, impression, clarity, objective, and results. They exhibited fair agreement on subjective dimensions, with ICC ranging from 0.23-0.38 for impact, engagement, and applicability. Gemini showed fair agreement on half criteria and no reliability on impact and applicability. Three LLMs showed acceptable or negligible mean difference (ChatGPT=0.24, Gemini=0.42, Claude=-0.02) from the human mean composite scores. Discussion: LLMs could process abstracts in batches with moderate agreement with human experts on overall quality and objective criteria. With appropriate process architecture, they can apply a rubric consistently across volumes of abstracts exceeding feasibility for a human rater. The weaker performance on subjective dimensions indicates that AI should serve a complementary role in evaluation, while human expertise remains essential.

cs.CL↗

arXiv2025

Simulated Self-Assessment in Large Language Models: A Psychometric Approach to AI Self-Efficacy

Large language model (LLM) proficiency in evaluating and quantifying their capacities remains uncertain. We conducted a controlled psychometric measurement study adapting the 10-item General Self-Efficacy Scale (GSES) to evaluate simulated self-assessment across 10 contemporary LLMs. Models completed the GSES under a no-task control condition and after three task-priming conditions: computational reasoning, social reasoning, and summarization. We examined between-model variation, task versus no-task differences, within-model stability, item-order robustness, internal consistency, and qualitative reasoning patterns. LLM-generated GSES responses were highly stable across repeated administrations, with nearly all model-task-item scores remaining identical across three runs. Internal consistency was high across task conditions, with Cronbachs alpha , and item-order effects were minimal, with intraclass correlation coefficients . Despite this stability, simulated self-efficacy differed significantly between models across all task and no-task conditions . Composite LLM GSES scores were lower and more variable than human norms (compared to the literature), and the latent response structure did not replicate human self-efficacy patterns. Qualitative analysis suggested that models differed in how they interpreted GSES items involving effort, coping, agency, and persistence. Some models rejected these constructs as inapplicable to artificial systems, while others translated them into task-oriented capability language. These findings indicate that LLMs can generate internally consistent psychometric self-assessments, but these outputs appear to reflect model-specific and context-sensitive communication behavior rather than human-like self-efficacy or metacognition. Psychometric prompting may help characterize how LLMs express capability, limitation, and agency under different prompting contexts.

cs.AI↗

arXiv2025

Zero-shot generation of synthetic neurosurgical data with large language models

Clinical data is fundamental to advance neurosurgical research, but access is often constrained by data availability, small sample sizes, privacy regulations, and resource-intensive preprocessing and de-identification procedures. Synthetic data offers a potential solution to challenges associated with accessing and using real-world data (RWD). This study aims to evaluate the capability of zero-shot generation of synthetic neurosurgical data with a large language model (LLM), GPT-4o, by benchmarking with the conditional tabular generative adversarial network (CTGAN). Synthetic datasets were compared to real-world neurosurgical data to assess fidelity (means, proportions, distributions, and bivariate correlations), utility (ML classifier performance on RWD), and privacy (duplication of records from RWD). The GPT-4o-generated datasets matched or exceeded CTGAN performance, despite no fine-tuning or access to RWD for pre-training. Datasets demonstrated high univariate and bivariate fidelity to RWD without directly exposing any real patient records, even at amplified sample size. Training an ML classifier on GPT-4o-generated data and testing on RWD for a binary prediction task showed an F1 score (0.706) with comparable performance to training on the CTGAN data (0.705) for predicting postoperative functional status deterioration. GPT-4o demonstrated a promising ability to generate high-fidelity synthetic neurosurgical data. These findings also indicate that data synthesized with GPT-4o can effectively augment clinical data with small sample sizes, and train ML models for prediction of neurosurgical outcomes. Further investigation is necessary to improve the preservation of distributional characteristics and boost classifier performance.

cs.CL↗