35 citations · 62 across the 18 of their papers we have counts for
26 papers · 1 filter
BitStack: Any-Size Compression of Large Language Models in Variable Memory Environments
Xinghao Wang, Pengyu Wang, Bo Wang +3
Large language models (LLMs) have revolutionized numerous applications, yet their deployment remains challenged by memory constraints on local devices. While scaling laws have enha…
Benchmarking Hallucination in Large Language Models based on Unanswerable Math Word Problem
Yuhong Sun, Zhangyue Yin, Qipeng Guo +3
Large language models (LLMs) are highly effective in various natural language processing (NLP) tasks. However, they are susceptible to producing unreliable conjectures in ambiguous…
Training-Free Long-Context Scaling of Large Language Models
Chenxin An, Fei Huang, Jun Zhang +4
The ability of Large Language Models (LLMs) to process and generate coherent text is markedly weakened when the number of input tokens exceeds their pretraining length. Given the e…
Are LLMs Rational Investors? A Study on Detecting and Reducing the Financial Bias in LLMs
Yuhang Zhou, Yuchen Ni, Yunhui Gan +7
Large Language Models (LLMs) are increasingly adopted in financial analysis for interpreting complex market data and trends. However, their use is challenged by intrinsic biases (e…
DenoSent: A Denoising Objective for Self-Supervised Sentence Representation Learning
Xinghao Wang, Junliang He, Pengyu Wang +3
Contrastive-learning-based methods have dominated sentence representation learning. These methods regularize the representation space by pulling similar sentence representations cl…
InferAligner: Inference-Time Alignment for Harmlessness through Cross-Model Guidance
Pengyu Wang, Dong Zhang, Linyang Li +5
With the rapid development of large language models (LLMs), they are not only used as general-purpose AI assistants but are also customized through further fine-tuning to meet the…