112 citations · 313 across the 18 of their papers we have counts for
18 papers
SkillWeaver: Web Agents can Self-Improve by Discovering and Honing Skills
Boyuan Zheng, Michael Y. Fatemi, Xiaolong Jin +8
To survive and thrive in complex environments, humans have evolved sophisticated self-improvement mechanisms through environment exploration, hierarchical abstraction of experience…
Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems
Bang Liu, Xinfeng Li, Jiayi Zhang +45
The advent of large language models (LLMs) has catalyzed a transformative shift in artificial intelligence, paving the way for advanced intelligent agents capable of sophisticated…
Is Your LLM Secretly a World Model of the Internet? Model-Based Planning for Web Agents
Yu Gu, Kai Zhang, Yuting Ning +9
Language agents based on large language models (LLMs) have demonstrated great promise in automating web-based tasks. Recent work has shown that incorporating advanced planning algo…
VisualAgentBench: Towards Large Multimodal Models as Visual Foundation Agents
Xiao Liu, Tianjie Zhang, Yu Gu +27
Large Multimodal Models (LMMs) have ushered in a new era in artificial intelligence, merging capabilities in both language and vision to form highly capable Visual Foundation Agent…
HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models
Bernal Jiménez Gutiérrez, Yiheng Shu, Yu Gu +2
In order to thrive in hostile and ever-changing natural environments, mammalian brains evolved to store large amounts of knowledge about the world and continually integrate new inf…
Middleware for LLMs: Tools Are Instrumental for Language Agents in Complex Environments
Yu Gu, Yiheng Shu, Hao Yu +6
The applications of large language models (LLMs) have expanded well beyond the confines of text processing, signaling a new era where LLMs are envisioned as generalist agents capab…
Reviving the Context: Camera Trap Species Classification as Link Prediction on Multimodal Knowledge Graphs
Vardaan Pahuja, Weidi Luo, Yu Gu +7
Camera traps are important tools in animal ecology for biodiversity monitoring and conservation. However, their practical application is limited by issues such as poor generalizati…
FlexKBQA: A Flexible LLM-Powered Framework for Few-Shot Knowledge Base Question Answering
Zhenyu Li, Sunqi Fan, Yu Gu +5
Knowledge base question answering (KBQA) is a critical yet challenging task due to the vast number of entities within knowledge bases and the diversity of natural language question…
AgentBench: Evaluating LLMs as Agents
Xiao Liu, Hao Yu, Hanchen Zhang +19
The potential of Large Language Model (LLM) as agents has been widely acknowledged recently. Thus, there is an urgent need to quantitatively \textit{evaluate LLMs as agents} on cha…
KoLA: Carefully Benchmarking World Knowledge of Large Language Models
Jifan Yu, Xiaozhi Wang, Shangqing Tu +32
The unprecedented performance of large language models (LLMs) necessitates improvements in evaluations. Rather than merely exploring the breadth of LLM abilities, we believe meticu…
Mind2Web: Towards a Generalist Agent for the Web
Xiang Deng, Yu Gu, Boyuan Zheng +5
We introduce Mind2Web, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website.…
Few-shot In-context Learning for Knowledge Base Question Answering
Tianle Li, Xueguang Ma, Alex Zhuang +3
Question answering over knowledge bases is considered a difficult problem due to the challenge of generalizing to a wide variety of possible natural language questions. Additionall…
Don't Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments
Yu Gu, Xiang Deng, Yu Su
A key missing capacity of current language models (LMs) is grounding to real-world environments. Most existing work for grounded language understanding uses LMs to directly generat…
Knowledge Base Question Answering: A Semantic Parsing Perspective
Yu Gu, Vardaan Pahuja, Gong Cheng +1
Recent advances in deep learning have greatly propelled the research on semantic parsing. Improvement has since been made in many downstream tasks, including natural language inter…
Clues Before Answers: Generation-Enhanced Multiple-Choice QA
Zixian Huang, Ao Wu, Jiaying Zhou +3
A trending paradigm for multiple-choice question answering (MCQA) is using a text-to-text framework. By unifying data in different tasks into a single text-to-text format, it train…
ArcaneQA: Dynamic Program Induction and Contextualized Encoding for Knowledge Base Question Answering
Yu Gu, Yu Su
Question answering on knowledge bases (KBQA) poses a unique challenge for semantic parsing research due to two intertwined challenges: large search space and ambiguities in schema…
A Systematic Investigation of KB-Text Embedding Alignment at Scale
Vardaan Pahuja, Yu Gu, Wenhu Chen +4
Knowledge bases (KBs) and text often contain complementary knowledge: KBs store structured knowledge that can support long range reasoning, while text stores more comprehensive and…
Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases
Yu Gu, Sue Kase, Michelle Vanni +4
Existing studies on question answering on knowledge bases (KBQA) mainly operate with the standard i.i.d assumption, i.e., training distribution over questions is the same as the te…