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Benchmark Hub

DROP Benchmark Papers (Last 120 Days)

Updated from current HFEPX corpus (Feb 27, 2026). 11 papers are grouped in this benchmark page. Common evaluation modes: Automatic Metrics. Most common rater population: Domain Experts. Frequent quality control: Gold Questions. Frequently cited benchmark: DROP. Common metric signal: accuracy. 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: 11 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 11 papers for DROP Benchmark Papers (Last 120 Days). Dominant protocol signals include automatic metrics, with frequent benchmark focus on DROP, BIRD and metric focus on accuracy, bleu. 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

  • DROP appears in 100% of hub papers (11/11); use this cohort for benchmark-matched comparisons.
  • BIRD appears in 9.1% of hub papers (1/11); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 27.3% of hub papers (3/11); compare with a secondary metric before ranking methods.
  • bleu is reported in 9.1% of hub papers (1/11); compare with a secondary metric before ranking methods.

Researcher Checklist

  • Close gap on Papers with explicit human feedback. Coverage is a replication risk (9.1% vs 45% target).
  • Close gap on Papers reporting quality controls. Coverage is a replication risk (9.1% vs 30% target).
  • Maintain strength on Papers naming benchmarks/datasets. Coverage is strong (100% vs 35% target).
  • Maintain strength on Papers naming evaluation metrics. Coverage is strong (45.5% vs 35% target).
  • Close gap on Papers with known rater population. Coverage is a replication risk (9.1% vs 35% target).
  • Close gap on Papers with known annotation unit. Coverage is a replication risk (0% vs 35% target).

Papers with explicit human feedback

Coverage is a replication risk (9.1% vs 45% target).

Papers reporting quality controls

Coverage is a replication risk (9.1% vs 30% target).

Papers naming benchmarks/datasets

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

Papers naming evaluation metrics

Coverage is strong (45.5% vs 35% target).

Papers with known rater population

Coverage is a replication risk (9.1% vs 35% target).

Papers with known annotation unit

Coverage is a replication risk (0% vs 35% target).

Suggested Reading Order

  1. 1. SPARTA: Scalable and Principled Benchmark of Tree-Structured Multi-hop QA over Text and Tables

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

  2. 2. Bridging Latent Reasoning and Target-Language Generation via Retrieval-Transition Heads

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

  3. 3. Understanding Artificial Theory of Mind: Perturbed Tasks and Reasoning in Large Language Models

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

  4. 4. Mitigating Structural Noise in Low-Resource S2TT: An Optimized Cascaded Nepali-English Pipeline with Punctuation Restoration

    Adds automatic metrics for broader coverage within this hub.

  5. 5. Beyond Subtokens: A Rich Character Embedding for Low-resource and Morphologically Complex Languages

    Adds automatic metrics for broader coverage within this hub.

  6. 6. Online Algorithms with Unreliable Guidance

    Adds automatic metrics for broader coverage within this hub.

  7. 7. Subgroups of $U(d)$ Induce Natural RNN and Transformer Architectures

    Adds automatic metrics for broader coverage within this hub.

  8. 8. TFL: Targeted Bit-Flip Attack on Large Language Model

    Adds automatic metrics for broader coverage within this hub.

Known Limitations

  • Only 9.1% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (9.1% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.

Research Utility Links

Benchmark Brief

DROP

Coverage: 11 papers (100%)

11 papers (100%) mention DROP.

Examples: SPARTA: Scalable and Principled Benchmark of Tree-Structured Multi-hop QA over Text and Tables , Bridging Latent Reasoning and Target-Language Generation via Retrieval-Transition Heads , Understanding Artificial Theory of Mind: Perturbed Tasks and Reasoning in Large Language Models

Benchmark Brief

BIRD

Coverage: 1 papers (9.1%)

1 papers (9.1%) mention BIRD.

Examples: CricBench: A Multilingual Benchmark for Evaluating LLMs in Cricket Analytics

Benchmark Brief

Cricbench

Coverage: 1 papers (9.1%)

1 papers (9.1%) mention Cricbench.

Examples: CricBench: A Multilingual Benchmark for Evaluating LLMs in Cricket Analytics

Metric Brief

accuracy

Coverage: 3 papers (27.3%)

3 papers (27.3%) mention accuracy.

Examples: Understanding Artificial Theory of Mind: Perturbed Tasks and Reasoning in Large Language Models , ABCD: All Biases Come Disguised , CricBench: A Multilingual Benchmark for Evaluating LLMs in Cricket Analytics

Metric Brief

bleu

Coverage: 1 papers (9.1%)

1 papers (9.1%) mention bleu.

Examples: Mitigating Structural Noise in Low-Resource S2TT: An Optimized Cascaded Nepali-English Pipeline with Punctuation Restoration

Metric Brief

f1

Coverage: 1 papers (9.1%)

1 papers (9.1%) mention f1.

Examples: SPARTA: Scalable and Principled Benchmark of Tree-Structured Multi-hop QA over Text and Tables

Most Cited In This Hub

Fast path to methods with the strongest citation traction in this scope.

Papers: SPARTA: Scalable and Principled Benchmark of Tree-Structured Multi-hop QA over Text and Tables , Bridging Latent Reasoning and Target-Language Generation via Retrieval-Transition Heads , Understanding Artificial Theory of Mind: Perturbed Tasks and Reasoning in Large Language Models

Most Recent

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

Papers: SPARTA: Scalable and Principled Benchmark of Tree-Structured Multi-hop QA over Text and Tables , Bridging Latent Reasoning and Target-Language Generation via Retrieval-Transition Heads , Understanding Artificial Theory of Mind: Perturbed Tasks and Reasoning in Large Language Models

Best Protocol Detail

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

Papers: SPARTA: Scalable and Principled Benchmark of Tree-Structured Multi-hop QA over Text and Tables , Bridging Latent Reasoning and Target-Language Generation via Retrieval-Transition Heads , Understanding Artificial Theory of Mind: Perturbed Tasks and Reasoning in Large Language Models

Top Papers On This Benchmark

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