Reddit LLM Analysis

What This Is

A multi-perspective reasoning system that generates distinct viewpoints on Reddit topics supporting, factual, and opposing and an F1 Commentary LLM fine-tuned on race commentary. The deeper question: can LLMs hold contradictory positions without collapsing to a consensus?

Current frontier LLMs are optimised for helpfulness and harmlessness, which often produces wishy-washy, fence-sitting responses. This project pushes in the opposite direction we want the model to commit to a position and defend it, then do the same for the opposing position, with equal conviction.

The Multi-Perspective Architecture

Single LLM calls with role-conditioning aren't enough the model tends to leak its "real" view across personas. We use a three-call architecture:

1. Supporting agent: instructed to find the strongest steelman for a given position
2. Factual agent: constrained to verified claims only, strips opinion entirely
3. Opposition agent: generates the best-faith rebuttal, not just strawmen

Each agent has separate system prompts with explicit epistemic constraints. The outputs are then cross-referenced: if the factual agent contradicts the supporting agent's claims, those claims get flagged.

The F1 Commentator LLM

Fine-tuned on scraped race commentary transcripts to produce authentic F1-style commentary from structured race data (lap times, positions, gaps, incidents). The goal is to bridge the gap between a data feed and the narrative colour that makes racing watchable.

What we learned: domain-specific fine-tuning dramatically improves register and vocabulary but doesn't improve factual accuracy. The model still hallucinates driver histories it wasn't told about. This is a grounding problem, not a style problem.

Reddit Data Pipeline

We collect subreddit posts and their top comment chains, preserving the reply structure. This gives us natural examples of humans arguing for different positions on the same topic gold-standard training data for perspective diversity.

What's Next

• Evaluate factual consistency with automated fact-checking (claim → evidence retrieval)
• Explore debate tree generation: model the full argument graph, not just two sides
• Fine-tune a smaller model on the perspective dataset for faster inference