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Metric Hub

Precision In CS.CL Papers

Updated from current HFEPX corpus (Feb 27, 2026). 29 papers are grouped in this metric page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Ranking. Frequent quality control: Calibration. Frequently cited benchmark: Retrieval. 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.

Papers: 29 Last published: Feb 26, 2026 Global RSS

Research Narrative

Grounded narrative Model: deterministic-grounded Source: persisted

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 29 papers for Precision In CS.CL Papers. Dominant protocol signals include automatic metrics, simulation environments, with frequent benchmark focus on Retrieval, GSM8K 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.

Why This Matters For Eval Research

Protocol Takeaways

Benchmark Interpretation

  • Retrieval appears in 10.3% of hub papers (3/29); use this cohort for benchmark-matched comparisons.
  • GSM8K appears in 6.9% of hub papers (2/29); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • precision is reported in 100% of hub papers (29/29); compare with a secondary metric before ranking methods.
  • accuracy is reported in 34.5% of hub papers (10/29); compare with a secondary metric before ranking methods.

Researcher Checklist

  • Close gap on Papers with explicit human feedback. Coverage is a replication risk (10.3% vs 45% target).
  • Close gap on Papers reporting quality controls. Coverage is a replication risk (17.2% vs 30% target).
  • Close gap on Papers naming benchmarks/datasets. Coverage is a replication risk (20.7% vs 35% target).
  • Maintain strength on Papers naming evaluation metrics. Coverage is strong (100% vs 35% target).
  • Close gap on Papers with known rater population. Coverage is a replication risk (13.8% vs 35% target).
  • Close gap on Papers with known annotation unit. Coverage is a replication risk (3.4% vs 35% target).

Papers with explicit human feedback

Coverage is a replication risk (10.3% vs 45% target).

Papers reporting quality controls

Coverage is a replication risk (17.2% vs 30% target).

Papers naming benchmarks/datasets

Coverage is a replication risk (20.7% vs 35% target).

Papers naming evaluation metrics

Coverage is strong (100% vs 35% target).

Papers with known rater population

Coverage is a replication risk (13.8% vs 35% target).

Papers with known annotation unit

Coverage is a replication risk (3.4% vs 35% target).

Suggested Reading Order

  1. 1. An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  2. 2. InnerQ: Hardware-aware Tuning-free Quantization of KV Cache for Large Language Models

    High citation traction makes this a useful baseline for method and protocol context.

  3. 3. pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training

    High citation traction makes this a useful baseline for method and protocol context.

  4. 4. A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection

    High citation traction makes this a useful baseline for method and protocol context.

  5. 5. Dynamic Personality Adaptation in Large Language Models via State Machines

    Adds simulation environments for broader coverage within this hub.

  6. 6. Small Language Models for Privacy-Preserving Clinical Information Extraction in Low-Resource Languages

    Adds automatic metrics for broader coverage within this hub.

  7. 7. An Expert Schema for Evaluating Large Language Model Errors in Scholarly Question-Answering Systems

    Adds automatic metrics with expert verification for broader coverage within this hub.

  8. 8. Case-Aware LLM-as-a-Judge Evaluation for Enterprise-Scale RAG Systems

    Adds automatic metrics for broader coverage within this hub.

Known Limitations

  • Only 17.2% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (13.8% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.

Research Utility Links

automatic_metrics vs simulation_env

both=1, left_only=27, right_only=1

1 papers use both Automatic Metrics and Simulation Env.

Benchmark Brief

Retrieval

Coverage: 3 papers (10.3%)

3 papers (10.3%) mention Retrieval.

Examples: Case-Aware LLM-as-a-Judge Evaluation for Enterprise-Scale RAG Systems , CUICurate: A GraphRAG-based Framework for Automated Clinical Concept Curation for NLP applications , Quecto-V1: Empirical Analysis of 8-bit Quantized Small Language Models for On-Device Legal Retrieval

Benchmark Brief

GSM8K

Coverage: 2 papers (6.9%)

2 papers (6.9%) mention GSM8K.

Examples: InnerQ: Hardware-aware Tuning-free Quantization of KV Cache for Large Language Models , Pyramid MoA: A Probabilistic Framework for Cost-Optimized Anytime Inference

Benchmark Brief

NyayaBench

Coverage: 1 papers (3.4%)

1 papers (3.4%) mention NyayaBench.

Examples: Why Agent Caching Fails and How to Fix It: Structured Intent Canonicalization with Few-Shot Learning

Metric Brief

precision

Coverage: 29 papers (100%)

29 papers (100%) mention precision.

Examples: InnerQ: Hardware-aware Tuning-free Quantization of KV Cache for Large Language Models , 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

Metric Brief

accuracy

Coverage: 10 papers (34.5%)

10 papers (34.5%) mention accuracy.

Examples: InnerQ: Hardware-aware Tuning-free Quantization of KV Cache for Large Language Models , 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

Metric Brief

recall

Coverage: 6 papers (20.7%)

6 papers (20.7%) mention recall.

Examples: A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection , An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models , 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: InnerQ: Hardware-aware Tuning-free Quantization of KV Cache for Large Language Models , 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

Most Recent

Fast path to latest protocol changes and newly published evaluation setups.

Papers: InnerQ: Hardware-aware Tuning-free Quantization of KV Cache for Large Language Models , 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

Best Protocol Detail

Papers with explicit rater/unit metadata and quality-control signals for reproducibility.

Papers: An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models

Top Papers Reporting This Metric

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