small language model

Characterizing LLM scientific concept generation: A multi-dimensional measurement study

A multi-dimensional characterization framework analyzing 9,666 outputs from seven models spanning 2020–2024 provides calibrated measurements and validated methodology for future research without making interpretive claims about novelty.

Rethinking Scale: The Efficacy of Fine-Tuned Open-Source LLMs in Large-Scale Reproducible Social Science Research

Large language models (LLMs) are distinguished by their architecture, parameter size, and performance capabilities. Social scientists have increasingly adopted LLMs to automate high-dimensional text classification tasks which are difficult to scale with human coders. Very large, closed-source models often deliver super...

PENGEMBANGAN MEDIA PETA INTERAKTIF BERBASIS STEAM BERBANTUAN GENIALLY PADA PEMBELAJARAN IPAS DI SEKOLAH DASAR

ABSTRACT Learning Social and Natural Sciences (IPAS) on the topic of the Association of Southeast Asian Nations (ASEAN) in fifth-grade elementary schools is still predominantly conventional and teacher-centered, limiting students’ opportunities to actively and meaningfully explore the geographical, economic, and cultur...

Evaluating large language models for domain-specific scholarly metadata classification

The findings indicate that open-source LLMs can support domain-specific scholarly metadata classification without task-specific fine-tuning, however, their moderate and dimension-dependent performance limits their suitability for fully automated fine-grained metadata enrichment.

Semantic labeling with fulfillers in multilingual contexts

This article introduces a novel approach to semantic labeling grounded in the fulfiller concept—a cognitive-semantic theory that highlights the role of absent but contextually inferred elements in meaning construction. We argue that the prevailing semantic labeling practices often neglect this essential aspect of m...

Improving Inference Efficiency in Deep Neural Networks

The increasing complexity of modern deep neural networks (DNNs), particularly Transformer architectures such as large language models (LLMs) and diffusion models, has made inference efficiency a central challenge across edge and datacenter platforms. The two regimes, however, impose sharply different constraints. At th...

PREreview of "What Does an LLM Learn from Reinforcement Learning? A Mechanistic Interpretability Perspective with Fixed-SAE Track"

This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/22775771. Summary The authors tackle a real obstacle in interpretability: you can't compare SAE features across training checkpoints, because separately trained dictionaries assign...

AMORE: Adaptive Multi-objective Reward Engineering for Socratic Math Tutoring with Small Language Models

A cross-sectional evaluation of large language model chatbot interfaces for patient-facing herpes zoster information: safety, information quality, and readability

In this standardised benchmark, LLM chatbot interfaces provided generally favourable accuracy and information-quality scores but showed clinically relevant safety limitations, sparse source attribution and disclosure, and readability challenges.

The effects of delayed auditory feedback depend on both the rhythm and content of the feedback signal

Purpose Delayed auditory feedback (DAF) has long been known to disrupt fluent speech, yet the underlying mechanisms remain a matter of debate. Some have hypothesized that interference occurs because of asynchronous sensory and motor rhythms, independent of the specific content of auditory inputs. The goal of this study...

Auditing sex/gender disparities in emergency triage with LLM-based paired comparisons

We present a domain-agnostic paired-comparison approach that uses Large Language Models (LLMs) to quantify sex/gender-related asymmetries in documented clinical decision-making. The method trains an LLM to emulate observed decisions, then evaluates sex-swapped pairs in which only sex is flipped, holding documented clin...

Open or Frontier? A Cost- and Energy-Aware Benchmark of Large Language Models for Software Vulnerability Detection

Efficiency is the robust finding: open-weight models occupy the quality-efficiency Pareto frontier in every configuration tested, and no frontier model is Pareto-optimal; no frontier model is Pareto-optimal.