Systems that help find experts or answer technical questions are experiencing a resurgence in activity.
Over four weeks, 62 papers focused on expert finding and question-answering systems (Expert finding and Q&A systems), compared to 25 four weeks earlier. This surge is particularly evident in the evaluation of models and the distribution of tasks between humans and machines.
Three recent papers illustrate this trend:
- JEV vs. LLMs as Rubric Judges: Cheaper, Faster, and Wrong in the Same Places
- How Much Were You Told? Measuring External Information in Peer Reviews
- Experts Rise Where LLMs Disagree: Using Cross-Model Disagreement to Target Expert Effort in LLM Codebook Revision for Large-Scale Annotation
The term training seeds (19 papers over four weeks, compared to 7 previously) appears in work testing the robustness of models against biased or incomplete initial data. It refers to the starting examples used to train a system before it generates its own data.