activity
20142025
most citedA Diversity-Promoting Objective Function for Neural Conversation Models

255 citations · 487 across the 9 of their papers we have counts for

collaborators

9 papers

cs.CL2025★ 1 cited

LETToT: Label-Free Evaluation of Large Language Models On Tourism Using Expert Tree-of-Thought

Ruiyan Qi, Congding Wen, Weibo Zhou +3

Evaluating large language models (LLMs) in specific domain like tourism remains challenging due to the prohibitive cost of annotated benchmarks and persistent issues like hallucina…

cs.CL2025

SitEmb-v1.5: Improved Context-Aware Dense Retrieval for Semantic Association and Long Story Comprehension

Junjie Wu, Jiangnan Li, Yuqing Li +6

Retrieval-augmented generation (RAG) over long documents typically involves splitting the text into smaller chunks, which serve as the basic units for retrieval. However, due to de…

cs.CR2022★ 19 cited

CATER: Intellectual Property Protection on Text Generation APIs via Conditional Watermarks

Xuanli He, Qiongkai Xu, Yi Zeng +4

Previous works have validated that text generation APIs can be stolen through imitation attacks, causing IP violations. In order to protect the IP of text generation APIs, a recent…

eess.AS2021★ 12 cited

Complex Spectral Mapping With Attention Based Convolution Recurrent Neural Network for Speech Enhancement

Liming Zhou, Yongyu Gao, Ziluo Wang +2

Speech enhancement has benefited from the success of deep learning in terms of intelligibility and perceptual quality. Conventional time-frequency (TF) domain methods focus on pred…

cs.CL2020★ 5 cited

Teaching Machines to Converse

Jiwei Li

The ability of a machine to communicate with humans has long been associated with the general success of AI. This dates back to Alan Turing's epoch-making work in the early 1950s,…

cs.CL2018★ 5 cited

Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints

Ashutosh Baheti, Alan Ritter, Jiwei Li +1

Neural conversation models tend to generate safe, generic responses for most inputs. This is due to the limitations of likelihood-based decoding objectives in generation tasks with…