241 citations · 395 across the 9 of their papers we have counts for
10 papers · 1 filter
Language Models that Seek for Knowledge: Modular Search & Generation for Dialogue and Prompt Completion
Kurt Shuster, Mojtaba Komeili, Leonard Adolphs +3
Language models (LMs) have recently been shown to generate more factual responses by employing modularity (Zhou et al., 2021) in combination with retrieval (Adolphs et al., 2021).…
Human Evaluation of Conversations is an Open Problem: comparing the sensitivity of various methods for evaluating dialogue agents
Eric Michael Smith, Orion Hsu, Rebecca Qian +3
At the heart of improving conversational AI is the open problem of how to evaluate conversations. Issues with automatic metrics are well known (Liu et al., 2016, arXiv:1603.08023),…
Teaching Models new APIs: Domain-Agnostic Simulators for Task Oriented Dialogue
Moya Chen, Paul A. Crook, Stephen Roller
We demonstrate that large language models are able to simulate Task Oriented Dialogues in novel domains, provided only with an API implementation and a list of goals. We show these…
Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions
Stephen Roller, Y-Lan Boureau, Jason Weston +13
We present our view of what is necessary to build an engaging open-domain conversational agent: covering the qualities of such an agent, the pieces of the puzzle that have been bui…
Don't Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training
Margaret Li, Stephen Roller, Ilia Kulikov +4
Generative dialogue models currently suffer from a number of problems which standard maximum likelihood training does not address. They tend to produce generations that (i) rely to…
The Dialogue Dodecathlon: Open-Domain Knowledge and Image Grounded Conversational Agents
Kurt Shuster, Da Ju, Stephen Roller +3
We introduce dodecaDialogue: a set of 12 tasks that measures if a conversational agent can communicate engagingly with personality and empathy, ask questions, answer questions by u…