activity
20232026
most citedBOLAA: Benchmarking and Orchestrating LLM-augmented Autonomous Agents

9 citations · 27 across the 24 of their papers we have counts for

collaborators
Showing 2024Show all

8 papers · 1 filter

cs.SE2024

ToolScan: A Benchmark for Characterizing Errors in Tool-Use LLMs

Shirley Kokane, Ming Zhu, Tulika Awalgaonkar +15

Evaluating Large Language Models (LLMs) is one of the most critical aspects of building a performant compound AI system. Since the output from LLMs propagate to downstream steps, i…

cs.AI2024

PRACT: Optimizing Principled Reasoning and Acting of LLM Agent

Zhiwei Liu, Weiran Yao, Jianguo Zhang +13

We introduce the Principled Reasoning and Acting (PRAct) framework, a novel method for learning and enforcing action principles from trajectory data. Central to our approach is the…

cs.CL2024★ 5 cited

xLAM: A Family of Large Action Models to Empower AI Agent Systems

Jianguo Zhang, Tian Lan, Ming Zhu +19

Autonomous agents powered by large language models (LLMs) have attracted significant research interest. However, the open-source community faces many challenges in developing speci…

cs.SE2024★ 1 cited

Diversity Empowers Intelligence: Integrating Expertise of Software Engineering Agents

Kexun Zhang, Weiran Yao, Zuxin Liu +13

Large language model (LLM) agents have shown great potential in solving real-world software engineering (SWE) problems. The most advanced open-source SWE agent can resolve over 27%…

cs.IR2024

Personalized Multi-task Training for Recommender System

Liangwei Yang, Zhiwei Liu, Jianguo Zhang +5

In the vast landscape of internet information, recommender systems (RecSys) have become essential for guiding users through a sea of choices aligned with their preferences. These s…

cs.CL2024★ 2 cited

APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets

Zuxin Liu, Thai Hoang, Jianguo Zhang +14

The advancement of function-calling agent models requires diverse, reliable, and high-quality datasets. This paper presents APIGen, an automated data generation pipeline designed t…

cs.CL2024★ 2 cited

MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases

Rithesh Murthy, Liangwei Yang, Juntao Tan +15

The deployment of Large Language Models (LLMs) and Large Multimodal Models (LMMs) on mobile devices has gained significant attention due to the benefits of enhanced privacy, stabil…

cs.AI2024

AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning

Jianguo Zhang, Tian Lan, Rithesh Murthy +15

Autonomous agents powered by large language models (LLMs) have garnered significant research attention. However, fully harnessing the potential of LLMs for agent-based tasks presen…