2 citations · 2 across the 6 of their papers we have counts for
5 papers · 1 filter
Neuro-Symbolic Synergy for World Modeling
Hongyu Zhao, Siyu Zhou, Haolin Yang +2
Large language models (LLMs) exhibit strong general-purpose reasoning capabilities, yet they frequently hallucinate when used as world models (WMs), where strict compliance with de…
TS-Reasoner: Aligning Time Series Foundation Models with LLM Reasoning
Fangxu Yu, Hongyu Zhao, Tianyi Zhou
Time series reasoning is crucial to decision-making in diverse domains, including finance, energy, and scientific discovery. While existing time series foundation models (TSFMs) ca…
BenTo: Benchmark Task Reduction with In-Context Transferability
Hongyu Zhao, Ming Li, Lichao Sun +1
Evaluating large language models (LLMs) is costly: it requires the generation and examination of LLM outputs on a large-scale benchmark of various tasks. This paper investigates ho…
Mosaic-IT: Cost-Free Compositional Data Synthesis for Instruction Tuning
Ming Li, Pei Chen, Chenguang Wang +5
Finetuning large language models with a variety of instruction-response pairs has enhanced their capability to understand and follow instructions. Current instruction tuning primar…
Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning
Ming Li, Yong Zhang, Shwai He +5
Instruction tuning is critical to improve LLMs but usually suffers from low-quality and redundant data. Data filtering for instruction tuning has proved important in improving both…