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20202026
most citedA Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots

8 citations · 12 across the 16 of their papers we have counts for

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13 papers · 1 filter

cs.CL2025

BARD: budget-aware reasoning distillation

Lujie Niu, Lei Shen, Yi Jiang +4

While long Chain-of-Thought (CoT) distillation effectively transfers reasoning capability to smaller language models, the reasoning process often remains redundant and computationa…

cs.CL2025

Enhancing Complex Instruction Following for Large Language Models with Mixture-of-Contexts Fine-tuning

Yuheng Lu, ZiMeng Bai, Caixia Yuan +2

Large language models (LLMs) exhibit remarkable capabilities in handling natural language tasks; however, they may struggle to consistently follow complex instructions including th…

cs.CL2025★ 1 cited

Collab-Overcooked: Benchmarking and Evaluating Large Language Models as Collaborative Agents

Haochen Sun, Shuwen Zhang, Lujie Niu +6

Large Language Models (LLMs) based agent systems have made great strides in real-world applications beyond traditional NLP tasks. This paper proposes a new LLM-based Multi-Agent Sy…

cs.CL2024

Controlled Low-Rank Adaptation with Subspace Regularization for Continued Training on Large Language Models

Yuheng Lu, Bingshuo Qian, Caixia Yuan +2

Large language models (LLMs) exhibit remarkable capabilities in natural language processing but face catastrophic forgetting when learning new tasks, where adaptation to a new doma…

cs.CL2023

Explicit Alignment and Many-to-many Entailment Based Reasoning for Conversational Machine Reading

Yangyang Luo, Shiyu Tian, Caixia Yuan +1

Conversational Machine Reading (CMR) requires answering a user's initial question through multi-turn dialogue interactions based on a given document. Although there exist many effe…

cs.CL2023

A Task-oriented Dialog Model with Task-progressive and Policy-aware Pre-training

Lucen Zhong, Hengtong Lu, Caixia Yuan +4

Pre-trained conversation models (PCMs) have achieved promising progress in recent years. However, existing PCMs for Task-oriented dialog (TOD) are insufficient for capturing the se…

cs.CL2023

AKEM: Aligning Knowledge Base to Queries with Ensemble Model for Entity Recognition and Linking

Di Lu, Zhongping Liang, Caixia Yuan +1

This paper presents a novel approach to address the Entity Recognition and Linking Challenge at NLPCC 2015. The task involves extracting named entity mentions from short search que…

cs.CL2023

An Asynchronous Updating Reinforcement Learning Framework for Task-oriented Dialog System

Sai Zhang, Yuwei Hu, Xiaojie Wang +1

Reinforcement learning has been applied to train the dialog systems in many works. Previous approaches divide the dialog system into multiple modules including DST (dialog state tr…

cs.CL2023

SPRING: Situated Conversation Agent Pretrained with Multimodal Questions from Incremental Layout Graph

Yuxing Long, Binyuan Hui, Fulong Ye +5

Existing multimodal conversation agents have shown impressive abilities to locate absolute positions or retrieve attributes in simple scenarios, but they fail to perform well when…

cs.CL2022★ 8 cited

A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots

Sai Zhang, Yuwei Hu, Yuchuan Wu +5

A slot value might be provided segment by segment over multiple-turn interactions in a dialog, especially for some important information such as phone numbers and names. It is a co…

cs.CL2021

Topic-Aware Contrastive Learning for Abstractive Dialogue Summarization

Junpeng Liu, Yanyan Zou, Hainan Zhang +4

Unlike well-structured text, such as news reports and encyclopedia articles, dialogue content often comes from two or more interlocutors, exchanging information with each other. In…

cs.CL2021

Converse, Focus and Guess -- Towards Multi-Document Driven Dialogue

Han Liu, Caixia Yuan, Xiaojie Wang +3

We propose a novel task, Multi-Document Driven Dialogue (MD3), in which an agent can guess the target document that the user is interested in by leading a dialogue. To benchmark pr…

cs.CL2020★ 2 cited

Label-Wise Document Pre-Training for Multi-Label Text Classification

Han Liu, Caixia Yuan, Xiaojie Wang

A major challenge of multi-label text classification (MLTC) is to stimulatingly exploit possible label differences and label correlations. In this paper, we tackle this challenge b…