88 citations · 212 across the 36 of their papers we have counts for
38 papers
Sparse Modular Activation for Efficient Sequence Modeling
Liliang Ren, Yang Liu, Shuohang Wang +3
Recent hybrid models combining Linear State Space Models (SSMs) with self-attention mechanisms have demonstrated impressive results across a range of sequence modeling tasks. Howev…
LMGQS: A Large-scale Dataset for Query-focused Summarization
Ruochen Xu, Song Wang, Yang Liu +5
Query-focused summarization (QFS) aims to extract or generate a summary of an input document that directly answers or is relevant to a given query. The lack of large-scale datasets…
InheritSumm: A General, Versatile and Compact Summarizer by Distilling from GPT
Yichong Xu, Ruochen Xu, Dan Iter +4
While large models such as GPT-3 demonstrate exceptional performance in zeroshot and fewshot summarization tasks, their extensive serving and fine-tuning costs hinder their utiliza…
Small Models are Valuable Plug-ins for Large Language Models
Canwen Xu, Yichong Xu, Shuohang Wang +3
Large language models (LLMs) such as GPT-3 and GPT-4 are powerful but their weights are often publicly unavailable and their immense sizes make the models difficult to be tuned wit…
In-Context Demonstration Selection with Cross Entropy Difference
Dan Iter, Reid Pryzant, Ruochen Xu +4
Large language models (LLMs) can use in-context demonstrations to improve performance on zero-shot tasks. However, selecting the best in-context examples is challenging because mod…
"What do others think?": Task-Oriented Conversational Modeling with Subjective Knowledge
Chao Zhao, Spandana Gella, Seokhwan Kim +7
Task-oriented Dialogue (TOD) Systems aim to build dialogue systems that assist users in accomplishing specific goals, such as booking a hotel or a restaurant. Traditional TODs rely…
G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment
Yang Liu, Dan Iter, Yichong Xu +3
The quality of texts generated by natural language generation (NLG) systems is hard to measure automatically. Conventional reference-based metrics, such as BLEU and ROUGE, have bee…
KILM: Knowledge Injection into Encoder-Decoder Language Models
Yan Xu, Mahdi Namazifar, Devamanyu Hazarika +3
Large pre-trained language models (PLMs) have been shown to retain implicit knowledge within their parameters. To enhance this implicit knowledge, we propose Knowledge Injection in…
PLACES: Prompting Language Models for Social Conversation Synthesis
Maximillian Chen, Alexandros Papangelis, Chenyang Tao +5
Collecting high quality conversational data can be very expensive for most applications and infeasible for others due to privacy, ethical, or similar concerns. A promising directio…
Using In-Context Learning to Improve Dialogue Safety
Nicholas Meade, Spandana Gella, Devamanyu Hazarika +5
While large neural-based conversational models have become increasingly proficient dialogue agents, recent work has highlighted safety issues with these systems. For example, these…
DialGuide: Aligning Dialogue Model Behavior with Developer Guidelines
Prakhar Gupta, Yang Liu, Di Jin +6
Dialogue models are able to generate coherent and fluent responses, but they can still be challenging to control and may produce non-engaging, unsafe results. This unpredictability…
Unifying Vision, Text, and Layout for Universal Document Processing
Zineng Tang, Ziyi Yang, Guoxin Wang +6
We propose Universal Document Processing (UDOP), a foundation Document AI model which unifies text, image, and layout modalities together with varied task formats, including docume…
Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding
Maximillian Chen, Alexandros Papangelis, Chenyang Tao +5
Dialogue understanding tasks often necessitate abundant annotated data to achieve good performance and that presents challenges in low-resource settings. To alleviate this barrier,…
UniSumm and SummZoo: Unified Model and Diverse Benchmark for Few-Shot Summarization
Yulong Chen, Yang Liu, Ruochen Xu +4
The high annotation costs and diverse demands of various summarization tasks motivate the development of few-shot summarization. However, despite the emergence of many summarizatio…
Alexa, Let's Work Together: Introducing the First Alexa Prize TaskBot Challenge on Conversational Task Assistance
Anna Gottardi, Osman Ipek, Giuseppe Castellucci +27
Since its inception in 2016, the Alexa Prize program has enabled hundreds of university students to explore and compete to develop conversational agents through the SocialBot Grand…
DialogSum Challenge: Results of the Dialogue Summarization Shared Task
Yulong Chen, Naihao Deng, Yang Liu +1
We report the results of DialogSum Challenge, the shared task on summarizing real-life scenario dialogues at INLG 2022. Four teams participate in this shared task and three submit…
A Systematic Evaluation of Response Selection for Open Domain Dialogue
Behnam Hedayatnia, Di Jin, Yang Liu +1
Recent progress on neural approaches for language processing has triggered a resurgence of interest on building intelligent open-domain chatbots. However, even the state-of-the-art…
Improving Bot Response Contradiction Detection via Utterance Rewriting
Di Jin, Sijia Liu, Yang Liu +1
Though chatbots based on large neural models can often produce fluent responses in open domain conversations, one salient error type is contradiction or inconsistency with the prec…
What is wrong with you?: Leveraging User Sentiment for Automatic Dialog Evaluation
Sarik Ghazarian, Behnam Hedayatnia, Alexandros Papangelis +2
Accurate automatic evaluation metrics for open-domain dialogs are in high demand. Existing model-based metrics for system response evaluation are trained on human annotated data, w…
Towards Textual Out-of-Domain Detection without In-Domain Labels
Di Jin, Shuyang Gao, Seokhwan Kim +2
In many real-world settings, machine learning models need to identify user inputs that are out-of-domain (OOD) so as to avoid performing wrong actions. This work focuses on a chall…
Multi-Sentence Knowledge Selection in Open-Domain Dialogue
Mihail Eric, Nicole Chartier, Behnam Hedayatnia +4
Incorporating external knowledge sources effectively in conversations is a longstanding problem in open-domain dialogue research. The existing literature on open-domain knowledge s…
Unsupervised Multi-Granularity Summarization
Ming Zhong, Yang Liu, Suyu Ge +7
Text summarization is a user-preference based task, i.e., for one document, users often have different priorities for summary. As a key aspect of customization in summarization, gr…
Analyzing the Limits of Self-Supervision in Handling Bias in Language
Lisa Bauer, Karthik Gopalakrishnan, Spandana Gella +3
Prompting inputs with natural language task descriptions has emerged as a popular mechanism to elicit reasonably accurate outputs from large-scale generative language models with l…
User Response and Sentiment Prediction for Automatic Dialogue Evaluation
Sarik Ghazarian, Behnam Hedayatnia, Alexandros Papangelis +2
Automatic evaluation is beneficial for open-domain dialog system development. However, standard word-overlap metrics (BLEU, ROUGE) do not correlate well with human judgements of op…