TIGER: Learning from Researcher Taste to Generate Tailored Research Ideas
Shane Gu, Yufeng Lin, Dixi Yao, Xuchen Gong, Chenhao Tan
Abstract
In pairwise comparisons where each judge chooses between an idea generated by its corresponding TIGER-8B policy and one generated by GPT-4.1, the three LLM researchers select the TIGER-8B idea in 56.6%-68.4% of 76 comparisons per researcher. PhD1 and PhD2 select the TIGER-8B idea in 19 of 30 pairs (63.3%) and 17 of 30 pairs (56.7%), respectively. All six directed cross-policy comparisons likewise favor the judge's matched TIGER-8B policy over an off-target policy, supporting the use of individual preference feedback for tailored research ideation.