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

Does anyone else can’t stand ComfyUI and prefers classic Automatic/Forge UI or it’s just me?

EDIT: I can't believe how many great and useful replies I've got, and not a…

11 hours ago

Serving Multiple Users at Once: How Continuous Batching Keeps LLM Inference Efficient

This article is divided into four parts; they are: • The Problem with Static Batching…

11 hours ago

Everyone Has Their Targets Set on the MacBook Neo

Dell, Microsoft, and others are unveiling new laptops to compete directly with the Neo, but…

12 hours ago

Photon-driven synapse advances low-power neuromorphic systems

Modern artificial intelligence systems rely on moving large amounts of data between memory and processors,…

12 hours ago

Anima – Sharing Some Prompts and Results

Been experimenting with Anima lately and ended up spending way too much time refining prompts.…

1 day ago

Keychron K2 HE Concrete Edition Review: Rock-Solid Typing

Keychron's K2 HE Concrete Edition sounds like a cute gimmick, but as I discovered, there's…

1 day ago