Papers by Zeqiu Wu
InSCIt: Information-Seeking Conversations with Mixed-Initiative Interactions (2023.tacl-1)
Copied to clipboard
Zeqiu Wu, Ryu Parish, Hao Cheng, Sewon Min, Prithviraj Ammanabrolu, Mari Ostendorf, Hannaneh Hajishirzi
| Challenge: | In information-seeking conversations, a user may ask questions that are under-specified or unanswerable. |
| Approach: | They present a dataset for information-seeking conversations with mixed-initiative interactions . they use Wikipedia to search for answers and provide relevant information . |
| Outcome: | The proposed system significantly underperforms humans in two of the most recent studies. |
CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning (2022.emnlp-main)
Copied to clipboard
Zeqiu Wu, Yi Luan, Hannah Rashkin, David Reitter, Hannaneh Hajishirzi, Mari Ostendorf, Gaurav Singh Tomar
| Challenge: | Existing models for conversational question answering require specific retrievers to understand user questions. |
| Approach: | They develop a query rewriting model CONQRR that rewrites a conversational question into a standalone question. |
| Outcome: | The proposed model achieves state-of-the-art on an open-domain conversational question answering dataset and is effective for two different off-the shelf retrievers. |
Training Language Models to Generate Text with Citations via Fine-grained Rewards (2024.acl-long)
Copied to clipboard
| Challenge: | Recent Large Language Models (LLMs) are prone to hallucination and their outputs often contain incorrect or unverifiable claims. |
| Approach: | They propose a training framework using fine-grained rewards to teach LLMs to generate highly supportive and relevant citations while ensuring the correctness of their responses. |
| Outcome: | The proposed training framework outperforms existing methods on QA datasets and surpasses GPT-3.5-turbo on LLaMA-2-7B. |
DIALKI: Knowledge Identification in Conversational Systems through Dialogue-Document Contextualization (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing knowledge grounding models focus on locating knowledge in document contexts that are relevant to the conversation. |
| Approach: | They propose a knowledge identification model that leverages document structure to provide dialogue-contextualized passage encodings and better locate knowledge relevant to the conversation. |
| Outcome: | The proposed model can be applied to document-grounded conversational datasets and shows generalization to unseen documents and long dialogue contexts. |
Automatic Document Sketching: Generating Drafts from Analogous Texts (2021.findings-acl)
Copied to clipboard
| Challenge: | Large pre-trained language models have made it possible to make high-quality predictions on how to add or change a sentence in a document. |
| Approach: | They propose a task to generate entire draft documents for the writer to review and revise. |
| Outcome: | The proposed model can make high-quality predictions on how to add or change a sentence in a document, but it lacks the branching factor to offer useful editing suggestions at a global or document level. |