most citedAlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model

38 citations · 67 across the 2 of their papers we have counts for

collaborators

5 papers

cs.AI2025

Can a Single Model Master Both Multi-turn Conversations and Tool Use? CoALM: A Unified Conversational Agentic Language Model

Emre Can Acikgoz, Jeremiah Greer, Akul Datta +6

Large Language Models (LLMs) with API-calling capabilities enabled building effective Language Agents (LA), while also revolutionizing the conventional task-oriented dialogue (TOD)…

cs.CL20251 cited

Better Slow than Sorry: Introducing Positive Friction for Reliable Dialogue Systems

Mert İnan, Anthony Sicilia, Suvodip Dey +6

While theories of discourse and cognitive science have long recognized the value of unhurried pacing, recent dialogue research tends to minimize friction in conversational systems.…

cs.CL2024

Dialog Flow Induction for Constrainable LLM-Based Chatbots

Stuti Agrawal, Nishi Uppuluri, Pranav Pillai +5

LLM-driven dialog systems are used in a diverse set of applications, ranging from healthcare to customer service. However, given their generalization capability, it is difficult to…

cs.CL202238 cited

AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model

Saleh Soltan, Shankar Ananthakrishnan, Jack FitzGerald +13

In this work, we demonstrate that multilingual large-scale sequence-to-sequence (seq2seq) models, pre-trained on a mixture of denoising and Causal Language Modeling (CLM) tasks, ar…

cs.AI201629 cited

Knowledge as a Teacher: Knowledge-Guided Structural Attention Networks

Yun-Nung Chen, Dilek Hakkani-Tur, Gokhan Tur +3

Natural language understanding (NLU) is a core component of a spoken dialogue system. Recently recurrent neural networks (RNN) obtained strong results on NLU due to their superior…