activity
20182025
most citedWizard of Wikipedia: Knowledge-Powered Conversational agents

623 citations · 1.2k across the 22 of their papers we have counts for

collaborators

24 papers

cs.CL2025★ 49 cited

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Gheorghe Comanici, Eric Bieber, Mike Schaekermann +3431

In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our…

cs.CV2024

Towards Artwork Explanation in Large-scale Vision Language Models

Kazuki Hayashi, Yusuke Sakai, Hidetaka Kamigaito +2

Large-scale Vision-Language Models (LVLMs) output text from images and instructions, demonstrating capabilities in text generation and comprehension. However, it has not been clari…

cs.AI2023★ 11 cited

The Troubling Emergence of Hallucination in Large Language Models -- An Extensive Definition, Quantification, and Prescriptive Remediations

Vipula Rawte, Swagata Chakraborty, Agnibh Pathak +5

The recent advancements in Large Language Models (LLMs) have garnered widespread acclaim for their remarkable emerging capabilities. However, the issue of hallucination has paralle…

cs.CL2023

Enhancing Performance on Seen and Unseen Dialogue Scenarios using Retrieval-Augmented End-to-End Task-Oriented System

Jianguo Zhang, Stephen Roller, Kun Qian +6

End-to-end task-oriented dialogue (TOD) systems have achieved promising performance by leveraging sophisticated natural language understanding and natural language generation capab…

cs.CL2023★ 1 cited

Leveraging Implicit Feedback from Deployment Data in Dialogue

Richard Yuanzhe Pang, Stephen Roller, Kyunghyun Cho +2

We study improving social conversational agents by learning from natural dialogue between users and a deployed model, without extra annotations. To implicitly measure the quality o…

cs.LG2023★ 2 cited

A Theory on Adam Instability in Large-Scale Machine Learning

Igor Molybog, Peter Albert, Moya Chen +14

We present a theory for the previously unexplained divergent behavior noticed in the training of large language models. We argue that the phenomenon is an artifact of the dominant…