4 papers · 1 filter
Human Bias in the Face of AI: Examining Human Judgment Against Text Labeled as AI Generated
Tiffany Zhu, Iain Weissburg, Kexun Zhang +1
As AI advances in text generation, human trust in AI generated content remains constrained by biases that go beyond concerns of accuracy. This study explores how bias shapes the pe…
Hire a Linguist!: Learning Endangered Languages with In-Context Linguistic Descriptions
Kexun Zhang, Yee Man Choi, Zhenqiao Song +3
How can large language models (LLMs) process and translate endangered languages? Many languages lack a large corpus to train a decent LLM; therefore existing LLMs rarely perform we…
Scaling LLM Inference with Optimized Sample Compute Allocation
Kexun Zhang, Shang Zhou, Danqing Wang +2
Sampling is a basic operation in many inference-time algorithms of large language models (LLMs). To scale up inference efficiently with a limited compute, it is crucial to find an…
Don't Fine-Tune, Decode: Syntax Error-Free Tool Use via Constrained Decoding
Kexun Zhang, Hongqiao Chen, Lei Li +1
Instruction-tuned large language models (LLMs) excel at many tasks but often fail to use external tools due to complicated and unfamiliar syntax constraints. While extensive fine-t…