31 citations · 37 across the 5 of their papers we have counts for
5 papers
TencentLLMEval: A Hierarchical Evaluation of Real-World Capabilities for Human-Aligned LLMs
Shuyi Xie, Wenlin Yao, Yong Dai +11
Large language models (LLMs) have shown impressive capabilities across various natural language tasks. However, evaluating their alignment with human preferences remains a challeng…
Bridging the Gap between Synthetic and Authentic Images for Multimodal Machine Translation
Wenyu Guo, Qingkai Fang, Dong Yu +1
Multimodal machine translation (MMT) simultaneously takes the source sentence and a relevant image as input for translation. Since there is no paired image available for the input…
The Trickle-down Impact of Reward (In-)consistency on RLHF
Lingfeng Shen, Sihao Chen, Linfeng Song +5
Standard practice within Reinforcement Learning from Human Feedback (RLHF) involves optimizing against a Reward Model (RM), which itself is trained to reflect human preferences for…
A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generation
Neeraj Varshney, Wenlin Yao, Hongming Zhang +2
Recently developed large language models have achieved remarkable success in generating fluent and coherent text. However, these models often tend to 'hallucinate' which critically…
MinT: Boosting Generalization in Mathematical Reasoning via Multi-View Fine-Tuning
Zhenwen Liang, Dian Yu, Xiaoman Pan +4
Reasoning in mathematical domains remains a significant challenge for relatively small language models (LMs). Many current methods focus on specializing LMs in mathematical reasoni…