Papers by Sergey Tulyakov
Train One Get One Free: Partially Supervised Neural Network for Bug Report Duplicate Detection and Clustering (N19-2)
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| Challenge: | Existing methods for duplicate classification require manual review and assigning bugs to the correct teams. |
| Approach: | They propose a loss function that can detect duplicate bug reports and aggregate them into latent topics without supervision. |
| Outcome: | The proposed model outperforms state-of-the-art methods for duplicate classification on both cases and can learn meaningful latent clusters without supervision. |
VIMI: Grounding Video Generation through Multi-modal Instruction (2024.emnlp-main)
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Yuwei Fang, Willi Menapace, Aliaksandr Siarohin, Tsai-Shien Chen, Kuan-Chieh Wang, Ivan Skorokhodov, Graham Neubig, Sergey Tulyakov
| Challenge: | Existing text-to-video diffusion models rely on text-only encoders for their pretraining, restricting their versatility and application in multimodal integration. |
| Approach: | They propose a multimodal conditional video generation framework for pretraining on augmented text prompts and then utilize a two-stage training strategy to enable diverse video generation tasks within a model. |
| Outcome: | The proposed model can synthesize consistent and temporally coherent videos with large motion while retaining the semantic control. |
Evaluating Very Long-Term Conversational Memory of LLM Agents (2024.acl-long)
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| Challenge: | Existing studies on long-term open-domain dialogues focus on evaluating responses within contexts spanning no more than five chat sessions. |
| Approach: | They propose a machine-human pipeline to generate very long-term dialogues by leveraging LLMs and retrieval augmented generation techniques. |
| Outcome: | The proposed pipeline generates very long-term dialogues using LLMs and RAGs . the generated conversations are verified and edited by human annotators for long-range consistency and grounding to the event graphs. |