32 citations · 73 across the 28 of their papers we have counts for
9 papers · 1 filter
Controlling Performance and Budget of a Centralized Multi-agent LLM System with Reinforcement Learning
Bowen Jin, TJ Collins, Donghan Yu +10
Large language models (LLMs) exhibit complementary strengths across domains and come with varying inference costs, motivating the design of multi-agent LLM systems where specialize…
ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities
Jiarui Lu, Thomas Holleis, Yizhe Zhang +9
Recent large language models (LLMs) advancements sparked a growing research interest in tool assisted LLMs solving real-world challenges, which calls for comprehensive evaluation o…
MMIDR: Teaching Large Language Model to Interpret Multimodal Misinformation via Knowledge Distillation
Longzheng Wang, Xiaohan Xu, Lei Zhang +5
Automatic detection of multimodal misinformation has gained a widespread attention recently. However, the potential of powerful Large Language Models (LLMs) for multimodal misinfor…
Can Large Language Models Understand Context?
Yilun Zhu, Joel Ruben Antony Moniz, Shruti Bhargava +6
Understanding context is key to understanding human language, an ability which Large Language Models (LLMs) have been increasingly seen to demonstrate to an impressive extent. Howe…
MARRS: Multimodal Reference Resolution System
Halim Cagri Ates, Shruti Bhargava, Site Li +15
Successfully handling context is essential for any dialog understanding task. This context maybe be conversational (relying on previous user queries or system responses), visual (r…
STEER: Semantic Turn Extension-Expansion Recognition for Voice Assistants
Leon Liyang Zhang, Jiarui Lu, Joel Ruben Antony Moniz +5
In the context of a voice assistant system, steering refers to the phenomenon in which a user issues a follow-up command attempting to direct or clarify a previous turn. We propose…