Papers by Bohong Wu
Extrapolating Multilingual Understanding Models as Multilingual Generators (2023.findings-emnlp)
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| Challenge: | Existing multilingual understanding models are not capable of generating high-quality text compared with decoder-based causal language models. |
| Approach: | They propose a method to adapt a multilingual encoder to a language generator with a small number of additional parameters. |
| Outcome: | The proposed approach outperforms initialization-based methods with 9.4 BLEU on machine translation, 8.1 Rouge-L on question generation, and 5.5 METEOR on story generation. |
Sentence Representation Learning with Generative Objective rather than Contrastive Objective (2022.emnlp-main)
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| Challenge: | Existing sentences-level training objectives focus on acquiring sentence-level representations, but they lack effective self-supervised objectives. |
| Approach: | They propose a generative self-supervised learning objective based on phrase reconstruction to improve sentence representation. |
| Outcome: | Empirical results show that the proposed objective outperforms current methods on STS benchmarks and retrieval and reranking tasks. |
World to Code: Multi-modal Data Generation via Self-Instructed Compositional Captioning and Filtering (2024.emnlp-main)
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| Challenge: | Recent advances in Vision-Language Models and the scarcity of high-quality multi-modal alignment data have inspired numerous researches on synthetic VLM data generation. |
| Approach: | They propose a multi-modal data construction pipeline that organizes the final output into a Python code format. |
| Outcome: | The proposed pipeline improves visual question answering and visual grounding benchmarks across different VLMs. |
Sentence-aware Contrastive Learning for Open-Domain Passage Retrieval (2022.acl-long)
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| Challenge: | Existing studies focus on improving negative sampling strategy or extra pretraining for dense passage representations, but these studies are not capturing passage with internal representation conflicts. |
| Approach: | They propose a model with a smaller granularity to capture internal representation conflicts . they introduce a negative sampling strategy to encourage a diverse generation of sentence representations within the same passage. |
| Outcome: | The proposed model can be trained on three benchmark datasets to alleviate internal representation conflicts. |