David Wan

Excited to share GenerationPrograms! 🚀 How do we get LLMs to cite their sources? GenerationPrograms is attributable by design, producing a program that executes text w/ a trace of how the text was generated! Gains of up to +39 Attribution F1 and eliminates uncited sentences,…

🎉 Our paper, GenerationPrograms, which proposes a modular framework for attributable text generation, has been accepted to @COLM_conf! GenerationPrograms produces a program that executes to text, providing an auditable trace of how the text was generated and major gains on…

In RAG applications, self-citation methods are prone to make attribution mistakes because there is no inductive bias for LLMs to track which source supports each statement. We propose GenerationPrograms: first generate a clear plan, then use that plan to guide generation. That…

🚨 Excited to announce GenerationPrograms (GP) which generates inherently attributed text by asking LLMs to produce a program that executes to text. Following the program trace gives us a causal understanding of how the text was generated, with major benefits: ➡️ Attribution…

Excited to present VideoTree🌲 at #CVPR2025 Fri at 10:30AM! VideoTree improves long-video QA via smart sampling: -Query-adaptive: finds the parts of the video relevant to the query -Coarse-to-fine structure: structured hierarchically to sample granularly from relevant segments

Thanks for the discovering + sharing our work on contextualized late-interaction based multimodal content retrieval, Omar! (and ColBERT is awesome of course) 😀

🚨 RAG is a popular approach but what happens when the retrieved sources provide conflicting information?🤔 We're excited to introduce our paper: “DRAGged into CONFLICTS: Detecting and Addressing Conflicting Sources in Search-Augmented LLMs”🚀 A thread 🧵👇

Introducing CLaMR -- a late-interaction retriever for complex multimodal video content! 📽️📚 ➡️ Jointly encodes frames, speech, on-screen text, and metadata to answer diverse queries grounded across modalities ➡️ Trained with a new dataset we introduce, MultiVENT 2.0++, a…

How can a multimodal retriever accurately retrieve docs from massive online video content that spans multiple modalities? We introduce CLaMR, a contextualized late-interaction retriever that jointly encodes all modalities and dynamically selects those containing the relevant…

Excited to announce CLaMR, our new retriever for multimodal documents! Strong performance improvements (+25 nDGC@10) compared to both multimodal and unimodal retrieval baselines. 🤝 CLaMR jointly encodes multiple modalities and selects the most relevant ones for each query. 🏋️‍♂️…