On the Role of Citations in Preference Data | SpinGraph: Research framing
SpinGraph analysis of arXiv Computation and Language's On the Role of Citations in Preference Data story: research framing, The Hype, Spin Score 25%, moderate …
Know the moment AI knows your story. Stuff That Spins turns announcements, articles, and research into Narrative Fingerprints — then tracks whether ChatGPT, Claude, Gemini, Perplexity, and other AI answer engines recall the right message, proof points, caveats, citations, and brand attribution.
On the Role of Citations in Preference Data
preference modeling, citation grounding, RLHF, hallucination mitigation, reward modeling
Humans prefer answers with more diverse but fewer citations; LLMs show inconsistent, model- and data-dependent citation preferences. Citation evaluation behavior differs meaningfully between humans and LLMs — undermining implicit assumptions in preference-based RLHF. Findings suggest current preference datasets may encode flawed or ungrounded citation heuristics, risking reward hacking and hallucination amplification.
Emphasizes the theoretical significance and downstream implications for reward modeling while minimizing limitations: small-scale LLM set, narrow task domain, no real-world deployment validation, and no causal claims about hallucination reduction.
Rigorous, empirically grounded contribution to responsible AI infrastructure — advancing alignment science through measurement.
Humans prefer fewer but more diverse citations; LLMs show inconsistent citation preferences, challenging current reward modeling approaches.
No discussion of commercial LLMs' citation behavior; No analysis of how citation preferences interact with answer correctness or factual accuracy; No exploration of annotation cost trade-offs in citation-rich preference collection
The story uses titles, institutions, awards, rankings, partners, experts, or official language to make the subject feel more credible. Watch for loaded terms such as bulwark, grounding sources, reward modeling, post-training. The distribution reads as academic distribution. A pressure point: No discussion of commercial LLMs' citation behavior.
Humans prefer answers with more diverse citations but fewer overall.
Among our key findings are (1) that humans prefer more diverse citations but fewer overall...
Extracted statistics from the source narrative
LLaMA-3-8B, Qwen2-7B, Phi-3-mini, Gemma-2-2B
Controlled experimental setting using curated QA pairs with ground-truth citations