DS2-Instruct: Domain-Specific Data Synthesis for Large Language Models Instruction Tuning (2026 · 2 citations)
Instruction-tuning an LLM for a specialized field usually requires expensive expert annotation. The alternatives are domain corpora, which are often proprietary or missing, or human-written seed examples. DS2-Instruct removes these requirements. It is a zero-shot pipeline that builds domain instruction data from only…
CoAct: Co-Active LLM Preference Learning with Human-AI Synergy (2026 · 1 citation)
Preference-based alignment of LLMs depends on human-labelled response pairs, which are costly to collect. Self-rewarding pipelines produce such pairs cheaply, but they can amplify the model's own mistakes. Active preference learning keeps labels trustworthy, yet it leaves most of the unlabeled pool unused. CoAct…

Co-authors here

4 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 3 times (of the 149,439 papers whose bibliographies we read); 2 citing papers with a page here →.

Updated 31 October 2026