Newest papers

Where Do LLMs Learn to Recommend AI?

Benaya Trabelsi, Jonathan Shaki, Sarit Kraus · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29785 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

AI関連のオプションはどこから来るのかと問われ、どのようなキャリアを追求するのか、あるいはどのような製品を構築するのか、大規模な言語モデル(LLMs)はしばしばAI関連のオプションを推奨します。私たちは、この傾向は、ファイナリングの前に、そして後にモデルを比較し、質問の方法を変えることによって生じます。第一に、プレトレーニングモデルはすでに、彼らの回答の27%の推奨に明確なAI/ML用語を含んでおり、彼らのリリースされた指示調整されたバージョンは59%でそうします。第二に、ファイナリングモデル自体は、この上昇は避けられないことを示しています:一般的な指示調節は、レートを変更せず、しかし

Machine translation (Meta M2M100); the English original follows.

Abstract

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. 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. Data rewarding helpfulness raise it by 9 points, while data ranked by the preferences of diverse simulated users lower it by 9. Finally, with models held fixed, asking for the "best" option or for what to do "right now" raises the rate by 8 and 18 points, whereas telling the model it is an AI advisor adds 4. Overall, the tendency starts in pretraining, and what models learn afterward, together with how a question is phrased, decides how strongly it appears.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

Topics: Helpfulness, Ask price, Product (mathematics), Computer science, Psychology, Language model

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange