2 citations · 4 across the 10 of their papers we have counts for
12 papers
Self-Improving World Modelling with Latent Actions
Yifu Qiu, Zheng Zhao, Waylon Li +4
Internal modelling of the world -- predicting transitions between previous states and next states under actions -- is essential to reasoning and planning for LLMs and V…
Lost in Space? Vision-Language Models Struggle with Relative Camera Pose Estimation
Ken Deng, Yifu Qiu, Yoni Kasten +2
We study whether vision-language models (VLMs) can solve relative camera pose estimation (RCPE) from image pairs, a direct test of multi-view spatial reasoning. We cast RCPE as a d…
Iterative Multilingual Spectral Attribute Erasure
Shun Shao, Yftah Ziser, Zheng Zhao +3
Multilingual representations embed words with similar meanings to share a common semantic space across languages, creating opportunities to transfer debiasing effects between langu…
Can VLMs Predict Future States? Bootstrapping World Models from Inverse Dynamics
Yifu Qiu, Yftah Ziser, Anna Korhonen +2
Can unified vision-language models (VLMs) perform forward dynamics prediction (FDP), i.e., predicting the future state (in image form) given the previous observation and an action…
What Is That Talk About? A Video-to-Text Summarization Dataset for Scientific Presentations
Dongqi Liu, Chenxi Whitehouse, Xi Yu +6
Transforming recorded videos into concise and accurate textual summaries is a growing challenge in multimodal learning. This paper introduces VISTA, a dataset specifically designed…
Eliciting In-context Retrieval and Reasoning for Long-context Large Language Models
Yifu Qiu, Varun Embar, Yizhe Zhang +3
Recent advancements in long-context language models (LCLMs) promise to transform Retrieval-Augmented Generation (RAG) by simplifying pipelines. With their expanded context windows,…
Spectral Editing of Activations for Large Language Model Alignment
Yifu Qiu, Zheng Zhao, Yftah Ziser +3
Large language models (LLMs) often exhibit undesirable behaviours, such as generating untruthful or biased content. Editing their internal representations has been shown to be effe…
Are Large Language Models Temporally Grounded?
Yifu Qiu, Zheng Zhao, Yftah Ziser +3
Are Large language models (LLMs) temporally grounded? Since LLMs cannot perceive and interact with the environment, it is impossible to answer this question directly. Instead, we p…
Think While You Write: Hypothesis Verification Promotes Faithful Knowledge-to-Text Generation
Yifu Qiu, Varun Embar, Shay B. Cohen +1
Knowledge-to-text generators often struggle to faithfully generate descriptions for the input facts: they may produce hallucinations that contradict the input, or describe facts no…
Detecting and Mitigating Hallucinations in Multilingual Summarisation
Yifu Qiu, Yftah Ziser, Anna Korhonen +2
Hallucinations pose a significant challenge to the reliability of neural models for abstractive summarisation. While automatically generated summaries may be fluent, they often lac…
Abstractive Summarization Guided by Latent Hierarchical Document Structure
Yifu Qiu, Shay B. Cohen
Sequential abstractive neural summarizers often do not use the underlying structure in the input article or dependencies between the input sentences. This structure is essential to…
DuReader_retrieval: A Large-scale Chinese Benchmark for Passage Retrieval from Web Search Engine
Yifu Qiu, Hongyu Li, Yingqi Qu +5
In this paper, we present DuReader_retrieval, a large-scale Chinese dataset for passage retrieval. DuReader_retrieval contains more than 90K queries and over 8M unique passages fro…