94 citations · 242 across the 15 of their papers we have counts for
15 papers
Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback
Hamish Ivison, Yizhong Wang, Jiacheng Liu +6
Learning from preference feedback has emerged as an essential step for improving the generation quality and performance of modern language models (LMs). Despite its widespread use,…
Training Language Models to Generate Text with Citations via Fine-grained Rewards
Chengyu Huang, Zeqiu Wu, Yushi Hu +1
While recent Large Language Models (LLMs) have proven useful in answering user queries, they are prone to hallucination, and their responses often lack credibility due to missing r…
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
Akari Asai, Zeqiu Wu, Yizhong Wang +2
Despite their remarkable capabilities, large language models (LLMs) often produce responses containing factual inaccuracies due to their sole reliance on the parametric knowledge t…
Does Collaborative Human-LM Dialogue Generation Help Information Extraction from Human Dialogues?
Bo-Ru Lu, Nikita Haduong, Chia-Hsuan Lee +7
The capabilities of pretrained language models have opened opportunities to explore new application areas, but applications involving human-human interaction are limited by the fac…
Fine-Grained Human Feedback Gives Better Rewards for Language Model Training
Zeqiu Wu, Yushi Hu, Weijia Shi +6
Language models (LMs) often exhibit undesirable text generation behaviors, including generating false, toxic, or irrelevant outputs. Reinforcement learning from human feedback (RLH…
INSCIT: Information-Seeking Conversations with Mixed-Initiative Interactions
Zeqiu Wu, Ryu Parish, Hao Cheng +4
In an information-seeking conversation, a user may ask questions that are under-specified or unanswerable. An ideal agent would interact by initiating different response types acco…
CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning
Zeqiu Wu, Yi Luan, Hannah Rashkin +4
Compared to standard retrieval tasks, passage retrieval for conversational question answering (CQA) poses new challenges in understanding the current user question, as each questio…
DIALKI: Knowledge Identification in Conversational Systems through Dialogue-Document Contextualization
Zeqiu Wu, Bo-Ru Lu, Hannaneh Hajishirzi +1
Identifying relevant knowledge to be used in conversational systems that are grounded in long documents is critical to effective response generation. We introduce a knowledge ident…
Automatic Document Sketching: Generating Drafts from Analogous Texts
Zeqiu Wu, Michel Galley, Chris Brockett +2
The advent of large pre-trained language models has made it possible to make high-quality predictions on how to add or change a sentence in a document. However, the high branching…
Extracting Summary Knowledge Graphs from Long Documents
Zeqiu Wu, Rik Koncel-Kedziorski, Mari Ostendorf +1
Knowledge graphs capture entities and relations from long documents and can facilitate reasoning in many downstream applications. Extracting compact knowledge graphs containing onl…
A Controllable Model of Grounded Response Generation
Zeqiu Wu, Michel Galley, Chris Brockett +8
Current end-to-end neural conversation models inherently lack the flexibility to impose semantic control in the response generation process, often resulting in uninteresting respon…
HiExpan: Task-Guided Taxonomy Construction by Hierarchical Tree Expansion
Jiaming Shen, Zeqiu Wu, Dongming Lei +5
Taxonomies are of great value to many knowledge-rich applications. As the manual taxonomy curation costs enormous human effects, automatic taxonomy construction is in great demand.…
SetExpan: Corpus-Based Set Expansion via Context Feature Selection and Rank Ensemble
Jiaming Shen, Zeqiu Wu, Dongming Lei +3
Corpus-based set expansion (i.e., finding the "complete" set of entities belonging to the same semantic class, based on a given corpus and a tiny set of seeds) is a critical task i…
Indirect Supervision for Relation Extraction using Question-Answer Pairs
Zeqiu Wu, Xiang Ren, Frank F. Xu +2
Automatic relation extraction (RE) for types of interest is of great importance for interpreting massive text corpora in an efficient manner. Traditional RE models have heavily rel…
CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases
Xiang Ren, Zeqiu Wu, Wenqi He +5
Extracting entities and relations for types of interest from text is important for understanding massive text corpora. Traditionally, systems of entity relation extraction have rel…