Categories: FAANG

Prompting for a Conversation: How to Control a Dialog Model?

Dialog modelling faces a difficult trade-off. Models are trained on a large amount of text, yet their responses need to be limited to a desired scope and style of a dialog agent. Because the datasets used to achieve the former contain language that is not compatible with the latter, pre-trained dialog models are fine-tuned on smaller curated datasets. However, the fine-tuning process robs them of the ability to produce diverse responses, eventually reducing them to dull conversation partners. In this paper we investigate if prompting can mitigate the above trade-off. Specifically, we…
AI Generated Robotic Content

Recent Posts

Fast Attention Kernels and Operator Fusion

Overview This post is divided into four parts; they are: • Operator Fusion • FlashAttention-Style…

36 mins ago

Getting Reliable Structured Outputs from LLMs: Validation, Retries and Constrained Decoding

The fixture suite turns the sentences above into fourteen test cases, from a missing required…

36 mins ago

Theoretical Limits of Language Model Alignment

Language model (LM) alignment improves model outputs to reflect human preferences while preserving the capabilities…

36 mins ago

Deploy frontier AI models on AWS Trainium with SGLang-Neuron

Today, Amazon announced that SGLang-Neuron is publicly available and can be accessed through an AWS…

36 mins ago

‘Bring Back THE Boss’: Nvidia Employees Petition for Jensen Huang’s Return

An internal post urging the board to consider employee sentiment about Nvidia’s former CEO has…

2 hours ago

Inside this quantum computer, memory vibrates like a guitar string

Researchers have created a quantum computer that stores information in microscopic vibrations, potentially making quantum…

2 hours ago