activity
20202026
most citedOverview of the Ninth Dialog System Technology Challenge: DSTC9

37 citations · 45 across the 4 of their papers we have counts for

collaborators

5 papers

cs.MA2026

EAIR: Interaction Risks in Enterprise LLM Multi-Agent Systems

Yuchen Ma, Swadheen Shukla, Paulo Salem +5

LLM agents increasingly divide enterprise work across roles with different goals, authority, access, and information. Because each agent often sees only a local snapshot of the ent…

cs.LG2026

SkillGen: Verified Inference-Time Agent Skill Synthesis

Yuchen Ma, Yue Huang, Han Bao +5

Skills are a promising way to improve LLM agent capabilities without retraining, while keeping the added procedure reusable and controllable. However, high-quality skills are still…

cs.CL2025

The Bias is in the Details: An Assessment of Cognitive Bias in LLMs

R. Alexander Knipper, Charles S. Knipper, Kaiqi Zhang +3

As Large Language Models (LLMs) are increasingly embedded in real-world decision-making processes, it becomes crucial to examine the extent to which they exhibit cognitive biases.…

cs.CL2020★ 37 cited

Overview of the Ninth Dialog System Technology Challenge: DSTC9

Chulaka Gunasekara, Seokhwan Kim, Luis Fernando D'Haro +36

This paper introduces the Ninth Dialog System Technology Challenge (DSTC-9). This edition of the DSTC focuses on applying end-to-end dialog technologies for four distinct tasks in…

cs.CL2020★ 8 cited

Conversation Learner -- A Machine Teaching Tool for Building Dialog Managers for Task-Oriented Dialog Systems

Swadheen Shukla, Lars Liden, Shahin Shayandeh +6

Traditionally, industry solutions for building a task-oriented dialog system have relied on helping dialog authors define rule-based dialog managers, represented as dialog flows. W…