Where Do LLMs Learn to Recommend AI?
Asked what to study, which career to pursue, or what product to build, large language models (LLMs) often recommend AI-related options.
ProofPaper ↗
Key points
- We ask where this tendency comes from by comparing models before and after fine-tuning and by varying how questions are asked.
- First, pretrained models already include explicit AI/ML terms in the recommendations of 27% of their responses, and their released instruction-tuned versions do so in 59%.
- Second, fine-tuning models ourselves shows that this rise is not inevitable: general instruction tuning leaves the rate unchanged, but removing the 7% of instruction examples that concern AI cuts it by 14 percentage points, below the level of the pretrained models it starts from.
- Overall, the tendency starts in pretraining, and what models learn afterward, together with how a question is phrased, decides how strongly it appears.
Sources (1)
- [1]Where Do LLMs Learn to Recommend AI?arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 02:13 PM
Asked what to study, which career to pursue, or what product to build, large language models (LLMs) often recommend AI-related options.
We ask where this tendency comes from by comparing models before and after fine-tuning and by varying how questions are asked.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 21, 2026OpenAI Frontier Models Arrive on Google Cloud and Model Garden
- Oct 20, 2026SLoSa: Small, Low-Power, and Safe Foundation Models for Robotic Task Planning
- Oct 20, 2026Beyond the KV cache: Training and serving i14’s Laplace 3B linear attention model on AWS Trainium2 with native PyTorch
- Oct 20, 2026Temporal Evidence-Parameter Co-Alignment for LLM Reasoning over Temporal Knowledge Graphs
- Oct 19, 2026Introducing Clairic, a new System One class of extraction models
- Oct 19, 2026Push-based KV transfer to improve TTFT and TPOT for disaggregated inference in vLLM