22 citations · 70 across the 10 of their papers we have counts for
21 papers
The Self-Improvement Paradox: Can Language Models Bootstrap Reasoning Capabilities without External Scaffolding?
Yutao Sun, Mingshuai Chen, Tiancheng Zhao +3
Self-improving large language models (LLMs) -- i.e., to improve the performance of an LLM by fine-tuning it with synthetic data generated by itself -- is a promising way to advance…
SF-QA: Simple and Fair Evaluation Library for Open-domain Question Answering
Xiaopeng Lu, Kyusong Lee, Tiancheng Zhao
Although open-domain question answering (QA) draws great attention in recent years, it requires large amounts of resources for building the full system and is often difficult to re…
VisualSparta: An Embarrassingly Simple Approach to Large-scale Text-to-Image Search with Weighted Bag-of-words
Xiaopeng Lu, Tiancheng Zhao, Kyusong Lee
Text-to-image retrieval is an essential task in cross-modal information retrieval, i.e., retrieving relevant images from a large and unlabelled dataset given textual queries. In th…
SPARTA: Efficient Open-Domain Question Answering via Sparse Transformer Matching Retrieval
Tiancheng Zhao, Xiaopeng Lu, Kyusong Lee
We introduce SPARTA, a novel neural retrieval method that shows great promise in performance, generalization, and interpretability for open-domain question answering. Unlike many n…
Report from the NSF Future Directions Workshop, Toward User-Oriented Agents: Research Directions and Challenges
Maxine Eskenazi, Tiancheng Zhao
This USER Workshop was convened with the goal of defining future research directions for the burgeoning intelligent agent research community and to communicate them to the National…
Talk to Papers: Bringing Neural Question Answering to Academic Search
Tianchang Zhao, Kyusong Lee
We introduce Talk to Papers, which exploits the recent open-domain question answering (QA) techniques to improve the current experience of academic search. It's designed to enable…