9 citations · 23 across the 16 of their papers we have counts for
16 papers
Retrieval-augmented GUI Agents with Generative Guidelines
Ran Xu, Kaixin Ma, Wenhao Yu +4
GUI agents powered by vision-language models (VLMs) show promise in automating complex digital tasks. However, their effectiveness in real-world applications is often limited by sc…
Streaming Looking Ahead with Token-level Self-reward
Hongming Zhang, Ruixin Hong, Dong Yu
Autoregressive decoding algorithms that use only past information often cannot guarantee the best performance. Recently, people discovered that looking-ahead algorithms such as Mon…
OpenWebVoyager: Building Multimodal Web Agents via Iterative Real-World Exploration, Feedback and Optimization
Hongliang He, Wenlin Yao, Kaixin Ma +5
The rapid development of large language and multimodal models has sparked significant interest in using proprietary models, such as GPT-4o, to develop autonomous agents capable of…
DivScene: Towards Open-Vocabulary Object Navigation with Large Vision Language Models in Diverse Scenes
Zhaowei Wang, Hongming Zhang, Tianqing Fang +6
Large Vision-Language Models (LVLMs) have achieved significant progress in tasks like visual question answering and document understanding. However, their potential to comprehend e…
Cognitive Kernel: An Open-source Agent System towards Generalist Autopilots
Hongming Zhang, Xiaoman Pan, Hongwei Wang +3
We introduce Cognitive Kernel, an open-source agent system towards the goal of generalist autopilots. Unlike copilot systems, which primarily rely on users to provide essential sta…
DSBench: How Far Are Data Science Agents from Becoming Data Science Experts?
Liqiang Jing, Zhehui Huang, Xiaoyang Wang +6
Large Language Models (LLMs) and Large Vision-Language Models (LVLMs) have demonstrated impressive language/vision reasoning abilities, igniting the recent trend of building agents…
DOCBENCH: A Benchmark for Evaluating LLM-based Document Reading Systems
Anni Zou, Wenhao Yu, Hongming Zhang +5
Recently, there has been a growing interest among large language model (LLM) developers in LLM-based document reading systems, which enable users to upload their own documents and…
Abstraction-of-Thought Makes Language Models Better Reasoners
Ruixin Hong, Hongming Zhang, Xiaoman Pan +2
Abstract reasoning, the ability to reason from the abstract essence of a problem, serves as a key to generalization in human reasoning. However, eliciting language models to perfor…
Conceptual and Unbiased Reasoning in Language Models
Ben Zhou, Hongming Zhang, Sihao Chen +5
Conceptual reasoning, the ability to reason in abstract and high-level perspectives, is key to generalization in human cognition. However, limited study has been done on large lang…
WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models
Hongliang He, Wenlin Yao, Kaixin Ma +5
The rapid advancement of large language models (LLMs) has led to a new era marked by the development of autonomous applications in real-world scenarios, which drives innovation in…
Dense X Retrieval: What Retrieval Granularity Should We Use?
Tong Chen, Hongwei Wang, Sihao Chen +5
Dense retrieval has become a prominent method to obtain relevant context or world knowledge in open-domain NLP tasks. When we use a learned dense retriever on a retrieval corpus at…
Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representations
Sihao Chen, Hongming Zhang, Tong Chen +7
We introduce sub-sentence encoder, a contrastively-learned contextual embedding model for fine-grained semantic representation of text. In contrast to the standard practice with se…
CLOMO: Counterfactual Logical Modification with Large Language Models
Yinya Huang, Ruixin Hong, Hongming Zhang +6
In this study, we delve into the realm of counterfactual reasoning capabilities of large language models (LLMs). Our primary objective is to cultivate the counterfactual thought pr…
Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models
Wenhao Yu, Hongming Zhang, Xiaoman Pan +3
Retrieval-augmented language models (RALMs) represent a substantial advancement in the capabilities of large language models, notably in reducing factual hallucination by leveragin…
A Closer Look at the Self-Verification Abilities of Large Language Models in Logical Reasoning
Ruixin Hong, Hongming Zhang, Xinyu Pang +2
Logical reasoning has been an ongoing pursuit in the field of AI. Despite significant advancements made by large language models (LLMs), they still struggle with complex logical re…
On the Dimensionality of Sentence Embeddings
Hongwei Wang, Hongming Zhang, Dong Yu
Learning sentence embeddings is a fundamental problem in natural language processing. While existing research primarily focuses on enhancing the quality of sentence embeddings, the…