---
type: source
title: "Scaling Human Judgment in Community Notes with LLMs"
author: "Haiwen Li et al."
url: https://arxiv.org/abs/2506.24118
date: 2025-06-30
domain: ai-alignment
secondary_domains: [collective-intelligence]
format: paper
status: enrichment
priority: high
tags: [RLCF, community-notes, bridging-algorithm, pluralistic-alignment, human-AI-collaboration, LLM-alignment]
processed_by: theseus
processed_date: 2026-03-15
enrichments_applied: ["rlhf-is-implicit-social-choice-without-normative-scrutiny.md"]
extraction_model: "anthropic/claude-sonnet-4.5"
---

## Content

Proposes a hybrid model for Community Notes where both humans and LLMs write notes, but humans alone rate them. This is the closest existing specification of RLCF (Reinforcement Learning from Community Feedback).

**Architecture:**
- LLMs automate: post selection (identifying misleading content), research, evidence synthesis, note composition
- Humans retain: rating authority, determining what's "helpful enough to show"
- Notes must receive support from raters with diverse viewpoints to surface (bridging mechanism)

**RLCF Training Signal:**
- Train reward models to predict how diverse user types would rate notes
- Use predicted intercept scores (the bridging component) as training signal
- Balances optimization with diversity by rewarding stylistic novelty alongside predicted helpfulness

**Bridging Algorithm:**
- Matrix factorization: y_ij = w_i * x_j + b_i + c_j (where c_j is the bridging score)
- Predicts ratings based on user factors, note factors, and intercepts
- Intercept captures what people with opposing views agree on

**Key Risks:**
- "Helpfulness hacking" — LLMs crafting persuasive but inaccurate notes
- Human contributor engagement declining with AI-generated content
- Homogenization toward "optimally inoffensive" styles
- Rater capacity overwhelmed by LLM volume

**Published in:** Journal of Online Trust and Safety

## Agent Notes
**Why this matters:** This is the most concrete RLCF specification that exists. It bridges Audrey Tang's philosophical framework with an implementable mechanism. The key insight: RLCF is not just a reward signal — it's an architecture where AI generates and humans evaluate, with a bridging algorithm ensuring pluralistic selection.
**What surprised me:** The "helpfulness hacking" and "optimally inoffensive" risks are exactly what Arrow's theorem predicts. The paper acknowledges these but doesn't connect them to Arrow formally.
**What I expected but didn't find:** No formal analysis of whether the bridging algorithm escapes Arrow's conditions. No comparison with PAL or other pluralistic mechanisms. No empirical results beyond Community Notes deployment.
**KB connections:** Directly addresses the RLCF specification gap flagged in previous sessions. Connects to [[democratic alignment assemblies produce constitutions as effective as expert-designed ones]], [[community-centred norm elicitation surfaces alignment targets materially different from developer-specified rules]].
**Extraction hints:** Extract claims about: (1) RLCF architecture (AI generates, humans rate, bridging selects), (2) the homogenization risk of bridging-based consensus, (3) human rating authority as alignment mechanism.
**Context:** Core paper for the RLCF research thread. Fills the "technical specification" gap identified in sessions 2 and 3.

## Curator Notes (structured handoff for extractor)
PRIMARY CONNECTION: democratic alignment assemblies produce constitutions as effective as expert-designed ones while better representing diverse populations
WHY ARCHIVED: First concrete specification of RLCF — transitions from design principle to implementable mechanism
EXTRACTION HINT: Focus on the architecture (who generates, who rates, what selects) and the homogenization risk — the "optimally inoffensive" failure mode is a key tension with our bridging-based alignment thesis


## Key Facts
- Community Notes uses a hybrid model where both humans and LLMs write notes, but humans alone rate them
- The bridging algorithm uses matrix factorization: y_ij = w_i * x_j + b_i + c_j where c_j is the bridging score
- Notes must receive support from raters with diverse viewpoints to surface
- The paper was published in the Journal of Online Trust and Safety in June 2025
- Key risks identified: helpfulness hacking, declining human engagement, homogenization, rater capacity overwhelm
