MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning (2023 · 58 citations)
Most vision-language models (VLMs) built on large language models handle prompts with a single image well.
ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code (2023 · 33 citations)
ML-Bench tests whether LLMs and autonomous agents can use real machine-learning GitHub repositories as libraries.
Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data Filtering (2025 · 11 citations)
Instruction tuning can teach LLMs to hallucinate when the training examples contain knowledge the model never acquired in pre-training.
UltraIF: Advancing Instruction Following from the Wild (2025 · 7 citations)
Open-source models still follow complex, multi-constraint instructions worse than industry models, partly because leading labs do not release their instruction data.
GATEAU: Selecting Influential Samples for Long Context Alignment (2025 · 5 citations)
Teaching LLMs to follow instructions over very long inputs is hard because human annotation of long instruction-following data is costly.

Co-authors here

12 of 23 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

Papers of our library cite their works 93 times (of the 149,439 papers whose bibliographies we read); 28 citing papers with a page here →.

Updated 31 October 2026