26 citations · 154 across the 24 of their papers we have counts for
24 papers
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…
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…
AgentLite: A Lightweight Library for Building and Advancing Task-Oriented LLM Agent System
Zhiwei Liu, Weiran Yao, Jianguo Zhang +10
The booming success of LLMs initiates rapid development in LLM agents. Though the foundation of an LLM agent is the generative model, it is critical to devise the optimal reasoning…
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…
Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit
Yao Wan, Yang He, Zhangqian Bi +6
Code intelligence leverages machine learning techniques to extract knowledge from extensive code corpora, with the aim of developing intelligent tools to improve the quality and pr…
DRDT: Dynamic Reflection with Divergent Thinking for LLM-based Sequential Recommendation
Yu Wang, Zhiwei Liu, Jianguo Zhang +3
The rise of Large Language Models (LLMs) has sparked interest in their application to sequential recommendation tasks as they can provide supportive item information. However, due…
Enhancing Performance on Seen and Unseen Dialogue Scenarios using Retrieval-Augmented End-to-End Task-Oriented System
Jianguo Zhang, Stephen Roller, Kun Qian +6
End-to-end task-oriented dialogue (TOD) systems have achieved promising performance by leveraging sophisticated natural language understanding and natural language generation capab…
BOLAA: Benchmarking and Orchestrating LLM-augmented Autonomous Agents
Zhiwei Liu, Weiran Yao, Jianguo Zhang +12
The massive successes of large language models (LLMs) encourage the emerging exploration of LLM-augmented Autonomous Agents (LAAs). An LAA is able to generate actions with its core…
Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization
Weiran Yao, Shelby Heinecke, Juan Carlos Niebles +12
Recent months have seen the emergence of a powerful new trend in which large language models (LLMs) are augmented to become autonomous language agents capable of performing objecti…
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI
Jianguo Zhang, Kun Qian, Zhiwei Liu +7
Despite advancements in conversational AI, language models encounter challenges to handle diverse conversational tasks, and existing dialogue dataset collections often lack diversi…
Zero-shot Item-based Recommendation via Multi-task Product Knowledge Graph Pre-Training
Ziwei Fan, Zhiwei Liu, Shelby Heinecke +4
Existing recommender systems face difficulties with zero-shot items, i.e. items that have no historical interactions with users during the training stage. Though recent works extra…
Fantastic Rewards and How to Tame Them: A Case Study on Reward Learning for Task-oriented Dialogue Systems
Yihao Feng, Shentao Yang, Shujian Zhang +4
When learning task-oriented dialogue (ToD) agents, reinforcement learning (RL) techniques can naturally be utilized to train dialogue strategies to achieve user-specific goals. Pri…
HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization
Ye Liu, Jian-Guo Zhang, Yao Wan +3
To capture the semantic graph structure from raw text, most existing summarization approaches are built on GNNs with a pre-trained model. However, these methods suffer from cumbers…
Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning
Jianguo Zhang, Trung Bui, Seunghyun Yoon +6
In this work, we focus on a more challenging few-shot intent detection scenario where many intents are fine-grained and semantically similar. We present a simple yet effective few-…
Are Pretrained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent Detection
Jianguo Zhang, Kazuma Hashimoto, Yao Wan +4
Pre-trained Transformer-based models were reported to be robust in intent classification. In this work, we first point out the importance of in-domain out-of-scope detection in few…
Enriching Non-Autoregressive Transformer with Syntactic and SemanticStructures for Neural Machine Translation
Ye Liu, Yao Wan, Jian-Guo Zhang +2
The non-autoregressive models have boosted the efficiency of neural machine translation through parallelized decoding at the cost of effectiveness when comparing with the autoregre…
NaturalCC: A Toolkit to Naturalize the Source Code Corpus
Yao Wan, Yang He, Jian-Guo Zhang +5
We present NaturalCC, an efficient and extensible toolkit to bridge the gap between natural language and programming language, and facilitate the research on big code analysis. Usi…
Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference
Jian-Guo Zhang, Kazuma Hashimoto, Wenhao Liu +5
Intent detection is one of the core components of goal-oriented dialog systems, and detecting out-of-scope (OOS) intents is also a practically important skill. Few-shot learning is…
MultiWOZ 2.2 : A Dialogue Dataset with Additional Annotation Corrections and State Tracking Baselines
Xiaoxue Zang, Abhinav Rastogi, Srinivas Sunkara +3
MultiWOZ is a well-known task-oriented dialogue dataset containing over 10,000 annotated dialogues spanning 8 domains. It is extensively used as a benchmark for dialogue state trac…
Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Tracking
Jian-Guo Zhang, Kazuma Hashimoto, Chien-Sheng Wu +4
Dialog state tracking (DST) is a core component in task-oriented dialog systems. Existing approaches for DST mainly fall into one of two categories, namely, ontology-based and onto…
Multi-Modal Generative Adversarial Network for Short Product Title Generation in Mobile E-Commerce
Jian-Guo Zhang, Pengcheng Zou, Zhao Li +4
Nowadays, more and more customers browse and purchase products in favor of using mobile E-Commerce Apps such as Taobao and Amazon. Since merchants are usually inclined to describe…
Product Title Refinement via Multi-Modal Generative Adversarial Learning
Jianguo Zhang, Pengcheng Zou, Zhao Li +5
Nowadays, an increasing number of customers are in favor of using E-commerce Apps to browse and purchase products. Since merchants are usually inclined to employ redundant and over…
Layerwise Perturbation-Based Adversarial Training for Hard Drive Health Degree Prediction
Jianguo Zhang, Ji Wang, Lifang He +2
With the development of cloud computing and big data, the reliability of data storage systems becomes increasingly important. Previous researchers have shown that machine learning…
Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloud
Ji Wang, Jianguo Zhang, Weidong Bao +3
The increasing demand for on-device deep learning services calls for a highly efficient manner to deploy deep neural networks (DNNs) on mobile devices with limited capacity. The cl…