1 citations · 3 across the 8 of their papers we have counts for
4 papers · 1 filter
LIMO: Less is More for Reasoning
Yixin Ye, Zhen Huang, Yang Xiao +3
We challenge the prevailing assumption that complex reasoning in large language models (LLMs) necessitates massive training data. We demonstrate that sophisticated mathematical rea…
Halu-J: Critique-Based Hallucination Judge
Binjie Wang, Steffi Chern, Ethan Chern +1
Large language models (LLMs) frequently generate non-factual content, known as hallucinations. Existing retrieval-augmented-based hallucination detection approaches typically addre…
ANOLE: An Open, Autoregressive, Native Large Multimodal Models for Interleaved Image-Text Generation
Ethan Chern, Jiadi Su, Yan Ma +1
Previous open-source large multimodal models (LMMs) have faced several limitations: (1) they often lack native integration, requiring adapters to align visual representations with…
BeHonest: Benchmarking Honesty in Large Language Models
Steffi Chern, Zhulin Hu, Yuqing Yang +5
Previous works on Large Language Models (LLMs) have mainly focused on evaluating their helpfulness or harmlessness. However, honesty, another crucial alignment criterion, has recei…