9 citations · 30 across the 15 of their papers we have counts for
9 papers · 1 filter
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…
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…
Generalization v.s. Memorization: Tracing Language Models' Capabilities Back to Pretraining Data
Xinyi Wang, Antonis Antoniades, Yanai Elazar +4
The impressive capabilities of large language models (LLMs) have sparked debate over whether these models genuinely generalize to unseen tasks or predominantly rely on memorizing v…
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…
Zero-Shot Detection of Machine-Generated Codes
Xianjun Yang, Kexun Zhang, Haifeng Chen +3
This work proposes a training-free approach for the detection of LLMs-generated codes, mitigating the risks associated with their indiscriminate usage. To the best of our knowledge…
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…
Large Language Models Are Partially Primed in Pronoun Interpretation
Suet-Ying Lam, Qingcheng Zeng, Kexun Zhang +2
While a large body of literature suggests that large language models (LLMs) acquire rich linguistic representations, little is known about whether they adapt to linguistic biases i…
ALGO: Synthesizing Algorithmic Programs with LLM-Generated Oracle Verifiers
Kexun Zhang, Danqing Wang, Jingtao Xia +2
Large language models (LLMs) excel at implementing code from functionality descriptions but struggle with algorithmic problems that require not only implementation but also identif…
A Study of Syntactic Multi-Modality in Non-Autoregressive Machine Translation
Kexun Zhang, Rui Wang, Xu Tan +4
It is difficult for non-autoregressive translation (NAT) models to capture the multi-modal distribution of target translations due to their conditional independence assumption, whi…