Skip to content
OpenTrain AIFor AI Companies

HFEPX · Eval paper review

GitSearch: Enhancing Community Notes Generation with Gap-Informed Targeted Search

Sahajpreet Singh, Kokil Jaidka, Min-Yen Kan, Preslav Nakov

Published

Feb 9, 2026

Citations

0

Trust level

High

Usefulness score

65/100 (Medium)

Extraction confidence

80% (High)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Oct 18, 2026

Open arXiv Find implementation Shortlist (0) RSS feed

Tags

Automatic Metrics General Pairwise Preference

Should you rely on this paper?

This paper has useful evaluation signal, but protocol completeness is partial; pair it with related papers before deciding implementation strategy.

Use this as a practical starting point for protocol research, then validate against the original paper.

Best use

Secondary protocol comparison source

Use if you need

A benchmark-and-metrics comparison anchor.

What to verify

Validate the evaluation procedure and quality controls in the full paper before operational use.

Main weakness

No major weakness surfaced.

Human feedback signal
Detected
From extracted signals
Evaluation signal
Detected
Eval setup described
Usefulness for eval research
65/100
Moderate-confidence candidate

Useful as a secondary reference; validate protocol details against neighboring papers.

If you are doing eval pipeline work, start here

Human Eval Papers LLM-as-Judge Papers Pairwise Preference Papers Tool-Use Evaluation Search

Abstract

Community Notes enable scalable, community-based fact-checking, but automated generation faces a coverage--quality trade-off. Note-dependent methods (e.g., summarization-based Supernotes) cannot operate without prior crowd input, while web-search LLMs achieve broad coverage but often retrieve evidence misaligned with what raters need. We introduce GitSearch (Gap-Informed Targeted Search), which treats missing, ambiguous, or disputed information as first-class retrieval targets. It infers information gaps from a post and optional incomplete notes, searches specifically to resolve them, and synthesizes an evidence-grounded Community Note. To evaluate this, we introduce PolBench, a new dataset of 78,698 U.S. political tweets with Community Notes. Keeping single-call search inference budget fixed, GitSearch improves human-rated helpfulness by 0.43 over unconditioned web search and wins 59% of pairwise comparisons. It generates notes for 99% of test cases, including the 46% where summarization methods fail. Human evaluators prefer GitSearch to human-written helpful notes in 69% of comparisons, primarily due to better context coverage. These findings show that how search is organized matters more than information access alone.

What we could verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

strong

Pairwise Preference

Directly usable for protocol triage.

"Community Notes enable scalable, community-based fact-checking, but automated generation faces a coverage--quality trade-off."

Evaluation Modes

strong

Automatic Metrics

Includes extracted eval setup.

"Community Notes enable scalable, community-based fact-checking, but automated generation faces a coverage--quality trade-off."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Community Notes enable scalable, community-based fact-checking, but automated generation faces a coverage--quality trade-off."

Benchmarks / Datasets

strong

Polbench

Useful for quick benchmark comparison.

"To evaluate this, we introduce PolBench, a new dataset of 78,698 U.S."

Reported Metrics

strong

Helpfulness

Useful for evaluation criteria comparison.

"Keeping single-call search inference budget fixed, GitSearch improves human-rated helpfulness by 0.43 over unconditioned web search and wins 59% of pairwise comparisons."

Benchmarks and datasets

Polbench

Reported metrics

helpfulness
Human feedback details
Uses human feedback
Yes
Feedback types
Pairwise Preference
Rater population
Not reported
Unit of annotation
Pairwise
Expertise required
General
Evaluation details
Evaluation modes
Automatic Metrics
Agentic eval
None
Quality controls
Not reported
Evidence quality
High
Use this page as
Secondary protocol comparison source

Research brief

Metadata summary

Community Notes enable scalable, community-based fact-checking, but automated generation faces a coverage--quality trade-off.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key takeaways

  • Community Notes enable scalable, community-based fact-checking, but automated generation faces a coverage--quality trade-off.
  • Note-dependent methods (e.g., summarization-based Supernotes) cannot operate without prior crowd input, while web-search LLMs achieve broad coverage but often retrieve evidence misaligned with what raters need.
  • We introduce GitSearch (Gap-Informed Targeted Search), which treats missing, ambiguous, or disputed information as first-class retrieval targets.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Validate inferred eval signals (Human evaluation) against the full paper.
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended queries

Pairwise preference evaluation Crowd/annotator signal evaluation

Contribution summary

  • We introduce GitSearch (Gap-Informed Targeted Search), which treats missing, ambiguous, or disputed information as first-class retrieval targets.
  • To evaluate this, we introduce PolBench, a new dataset of 78,698 U.S.
  • Keeping single-call search inference budget fixed, GitSearch improves human-rated helpfulness by 0.43 over unconditioned web search and wins 59% of pairwise comparisons.

Why it matters for eval

  • Keeping single-call search inference budget fixed, GitSearch improves human-rated helpfulness by 0.43 over unconditioned web search and wins 59% of pairwise comparisons.
  • Human evaluators prefer GitSearch to human-written helpful notes in 69% of comparisons, primarily due to better context coverage.

Researcher checklist

  • Human feedback protocol is explicit

    Detected: Pairwise Preference

  • Evaluation mode is explicit

    Detected: Automatic Metrics

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    Detected: Polbench

  • Metric reporting is present

    Detected: helpfulness