5 papers
Prompt Fairness: Sub-group Disparities in LLMs
Meiyu Zhong, Noel Teku, Ravi Tandon
Large Language Models (LLMs), though shown to be effective in many applications, can vary significantly in their response quality. In this paper, we investigate this problem of pro…
PROPS: Progressively Private Self-alignment of Large Language Models
Noel Teku, Fengwei Tian, Payel Bhattacharjee +3
Alignment is a key step in developing Large Language Models (LLMs) using human feedback to ensure adherence to human values and societal norms. Dependence on human feedback raises…
Speeding up Speculative Decoding via Sequential Approximate Verification
Meiyu Zhong, Noel Teku, Ravi Tandon
Speculative Decoding (SD) is a recently proposed technique for faster inference using Large Language Models (LLMs). SD operates by using a smaller draft LLM for autoregressively ge…
Latency-Distortion Tradeoffs in Communicating Classification Results over Noisy Channels
Noel Teku, Sudarshan Adiga, Ravi Tandon
In this work, the problem of communicating decisions of a classifier over a noisy channel is considered. With machine learning based models being used in variety of time-sensitive…
Adversarial Filters for Secure Modulation Classification
Alex Berian, Kory Staab, Noel Teku +3
Modulation Classification (MC) refers to the problem of classifying the modulation class of a wireless signal. In the wireless communications pipeline, MC is the first operation pe…