Metric Hub
Updated from current HFEPX corpus (Feb 27, 2026). 15 papers are grouped in this metric page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Frequent quality control: Calibration. Common metric signal: precision. Use this page to compare protocol setup, judge behavior, and labeling design decisions before running new eval experiments. Newest paper in this set is from Feb 26, 2026.
Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 15 papers for Precision + General Metric Papers. Dominant protocol signals include automatic metrics, simulation environments, with frequent benchmark focus on multiple benchmark families and metric focus on precision, accuracy. Use the grounded sections below to prioritize reproducible protocol choices, benchmark-matched comparisons, and judge-vs-human evaluation checks.
6.7% of papers report explicit human-feedback signals, led by expert verification.
Evidence: An Expert Schema for Evaluating Large Language Model Errors in Scholarly Question-Answering Systems , pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training , A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection , Voices of the Mountains: Deep Learning-Based Vocal Error Detection System for Kurdish Maqams
automatic metrics appears in 100% of papers in this hub.
Evidence: pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training , A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection , An Expert Schema for Evaluating Large Language Model Errors in Scholarly Question-Answering Systems , Voices of the Mountains: Deep Learning-Based Vocal Error Detection System for Kurdish Maqams
precision is a repeated reporting metric here, enabling more consistent cross-paper score interpretation.
Evidence: pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training , A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection , An Expert Schema for Evaluating Large Language Model Errors in Scholarly Question-Answering Systems , Voices of the Mountains: Deep Learning-Based Vocal Error Detection System for Kurdish Maqams
Most common quality-control signal is rater calibration (13.3% of papers).
Evidence: MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Elastic LLMs , WISE: Web Information Satire and Fakeness Evaluation , A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection , pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training
Rater context is mostly domain experts, and annotation is commonly mixed annotation units; use this to scope replication staffing.
Evidence: pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training , An Expert Schema for Evaluating Large Language Model Errors in Scholarly Question-Answering Systems , A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection , Voices of the Mountains: Deep Learning-Based Vocal Error Detection System for Kurdish Maqams
Track metric sensitivity by reporting both precision and accuracy.
Evidence: pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training , A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection , An Expert Schema for Evaluating Large Language Model Errors in Scholarly Question-Answering Systems , Voices of the Mountains: Deep Learning-Based Vocal Error Detection System for Kurdish Maqams
Direct snippets from paper abstracts to ground protocol and benchmark interpretation.
Human-eval abstract signal: Quantization-Aware Training from scratch has emerged as a promising approach for building efficient large language models (LLMs) with extremely low-bit weights (sub 2-bit), which can offer substantial advantages for edge deployment.
Human-eval abstract signal: Cyberbullying has become a serious and growing concern in todays virtual world.
precision metric signal: However, existing methods still fail to achieve satisfactory accuracy and scalability.
precision metric signal: Developing a generalized model with moderate accuracy remains challenging.
rater calibration quality-control signal: However, it has been observed that the calibration parameters for quantization are typically linked to specific precisions, which presents challenges during elastic-precision calibration and precision switching at runtime.
rater calibration quality-control signal: Using stratified 5-fold cross-validation, we evaluate models across comprehensive metrics including accuracy, precision, recall, F1-score, ROC-AUC, PR-AUC, MCC, Brier score, and Expected Calibration Error.
Protocol abstract signal: Large Language Models (LLMs) are transforming scholarly tasks like search and summarization, but their reliability remains uncertain.
Protocol abstract signal: Maqam, a singing type, is a significant component of Kurdish music.
Papers with explicit human feedback
Coverage is a replication risk (6.7% vs 45% target).
Papers reporting quality controls
Coverage is usable but incomplete (20% vs 30% target).
Papers naming benchmarks/datasets
Coverage is a replication risk (0% vs 35% target).
Papers naming evaluation metrics
Coverage is strong (100% vs 35% target).
Papers with known rater population
Coverage is a replication risk (13.3% vs 35% target).
Papers with known annotation unit
Coverage is a replication risk (0% vs 35% target).
1. pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training
Start here for detailed protocol reporting, including rater and quality-control evidence.
Start here for detailed protocol reporting, including rater and quality-control evidence.
3. An Expert Schema for Evaluating Large Language Model Errors in Scholarly Question-Answering Systems
Start here for detailed protocol reporting, including rater and quality-control evidence.
4. Voices of the Mountains: Deep Learning-Based Vocal Error Detection System for Kurdish Maqams
Adds automatic metrics for broader coverage within this hub.
5. PerSoMed: A Large-Scale Balanced Dataset for Persian Social Media Text Classification
Adds automatic metrics for broader coverage within this hub.
6. TriTopic: Tri-Modal Graph-Based Topic Modeling with Iterative Refinement and Archetypes
Adds automatic metrics for broader coverage within this hub.
7. MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Elastic LLMs
Adds automatic metrics for broader coverage within this hub.
8. AAVGen: Precision Engineering of Adeno-associated Viral Capsids for Renal Selective Targeting
Adds automatic metrics for broader coverage within this hub.
automatic_metrics vs simulation_env
both=1, left_only=14, right_only=0
1 papers use both Automatic Metrics and Simulation Env.
Metric Brief
precision
Coverage: 15 papers (100%)
15 papers (100%) mention precision.
Examples: pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training , A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection , An Expert Schema for Evaluating Large Language Model Errors in Scholarly Question-Answering Systems
Metric Brief
accuracy
Coverage: 6 papers (40%)
6 papers (40%) mention accuracy.
Examples: pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training , A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection , Voices of the Mountains: Deep Learning-Based Vocal Error Detection System for Kurdish Maqams
Metric Brief
f1
Coverage: 5 papers (33.3%)
5 papers (33.3%) mention f1.
Examples: A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection , Voices of the Mountains: Deep Learning-Based Vocal Error Detection System for Kurdish Maqams , PerSoMed: A Large-Scale Balanced Dataset for Persian Social Media Text Classification
Most Cited In This Hub
Fast path to methods with the strongest citation traction in this scope.
Papers: pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training , A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection , An Expert Schema for Evaluating Large Language Model Errors in Scholarly Question-Answering Systems
Most Recent
Fast path to latest protocol changes and newly published evaluation setups.
Papers: pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training , A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection , An Expert Schema for Evaluating Large Language Model Errors in Scholarly Question-Answering Systems
Best Protocol Detail
Papers with explicit rater/unit metadata and quality-control signals for reproducibility.
Papers: pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training , A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection , An Expert Schema for Evaluating Large Language Model Errors in Scholarly Question-Answering Systems
Wenzheng Zhang, Bingzheng Liu, Yang Hu, Xiaoying Bai, Wentao Zhang · Feb 26, 2026
Quantization-Aware Training from scratch has emerged as a promising approach for building efficient large language models (LLMs) with extremely low-bit weights (sub 2-bit), which can offer substantial advantages for edge deployment.
Mirza Raquib, Asif Pervez Polok, Kedar Nath Biswas, Rahat Uddin Azad, Saydul Akbar Murad · Feb 25, 2026
Evaluation uses multiple metrics, including accuracy, precision, recall, F1-score, Hamming loss, Cohens kappa, and AUC-ROC.
Anna Martin-Boyle, William Humphreys, Martha Brown, Cara Leckey, Harmanpreet Kaur · Feb 24, 2026
Current evaluation metrics for testing LLM reliability are primarily automated approaches that prioritize efficiency and scalability, but lack contextual nuance and fail to reflect how scientific domain experts assess LLM outputs in practic
Darvan Shvan Khairaldeen, Hossein Hassani · Feb 24, 2026
On the full 50-song evaluation at a 0.750 threshold, recall was 39.4% and precision 25.8% .
Isun Chehreh, Ebrahim Ansari · Feb 22, 2026
Data collection involved 60,000 raw posts from various Persian social media platforms, followed by rigorous preprocessing and hybrid annotation combining ChatGPT-based few-shot prompting with human verification.
Roman Egger · Feb 22, 2026
In benchmarks across 20 Newsgroups, BBC News, AG News, and Arxiv, TriTopic achieves the highest NMI on every dataset (mean NMI 0.575 vs.
Dongwei Wang, Jinhee Kim, Seokho Han, Denis Gudovskiy, Yohei Nakata · Feb 21, 2026
Changing runtime complexity on cloud and edge devices necessitates elastic large language model (LLM) deployment, where an LLM can be inferred with various quantization precisions based on available computational resources.
Mohammadreza Ghaffarzadeh-Esfahani, Yousof Gheisari · Feb 21, 2026
Adeno-associated viruses (AAVs) are promising vectors for gene therapy, but their native serotypes face limitations in tissue tropism, immune evasion, and production efficiency.
Muhammad Tayyab Khan, Lequn Chen, Wenhe Feng, Seung Ki Moon · Feb 20, 2026
When deterministic scoring cannot resolve an ambiguity, the system escalates to multimodal and constrained large-language-model reasoning, followed by a single human-in-the-loop (HITL) review step.
Peter Balogh · Feb 19, 2026
Some transformer attention heads appear to function as membership testers, dedicating themselves to answering the question "has this token appeared before in the context?" We identify these heads across four language models (GPT-2 small, me
Txell Amigó, Shawn Zheng Kai Tan, Angel Luu Phanthanourak, Sebastian Schulz, Pasquale D. Colaianni · Feb 18, 2026
Precision fermentation relies on microbial cell factories to produce sustainable food, pharmaceuticals, chemicals, and biofuels.
Tahir Hussain, Saddam Hussain Khan · Feb 16, 2026
The qualitative evaluation noted better extraction and discrimination and theological precision.
Pengxiang Zhao, Hui-Ling Zhen, Xing Li, Han Bao, Weizhe Lin · Feb 13, 2026
As LLMs scale, low-bit floating-point formats like MXFP and NVFP4 offer new opportunities for precision and efficiency.
Gaurab Chhetri, Subasish Das, Tausif Islam Chowdhury · Dec 30, 2025
This study develops WISE (Web Information Satire and Fakeness Evaluation) framework which benchmarks eight lightweight transformer models alongside two baseline models on a balanced dataset of 20,000 samples from Fakeddit, annotated as eith
Elisabeth Fittschen, Sabrina Li, Tom Lippincott, Leshem Choshen, Craig Messner · Apr 7, 2025
This has engendered growing interest in their use in humanistic disciplines, such as historical linguistics and literary studies.
