Papers with embedding-based models
Multi-Hop Knowledge Graph Reasoning with Reward Shaping (D18-1)
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| Challenge: | Multi-hop reasoning is an effective approach for query answering over incomplete knowledge graphs (KGs). |
| Approach: | They propose to adopt a pretrained one-hop embedding model to estimate reward of unobserved facts and to force agents to explore diverse set of paths using randomly generated edge masks. |
| Outcome: | The proposed model reduces false negative supervision and counters spurious search trajectories by forcing the agent to explore a diverse set of paths using randomly generated edge masks. |
SAMRank: Unsupervised Keyphrase Extraction using Self-Attention Map in BERT and GPT-2 (2023.emnlp-main)
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| Challenge: | Existing methods for keyphrase extraction use contextualized embeddings to capture semantic relevance between words, sentences, and documents. |
| Approach: | They propose an unsupervised keyphrase extraction approach that uses only a self-attention map in a pre-trained language model to determine the importance of phrases. |
| Outcome: | The proposed approach outperforms embedding-based models on three keyphrase extraction datasets. |
ViLL-E: Video LLM Embeddings for Retrieval (2026.acl-long)
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| Challenge: | Video Large Language Models excel at video understanding tasks where outputs are textual . however, they underperform specialized embedding-based models in Retrieval tasks . |
| Approach: | They propose a video-LLM-based model with an embedding generation mechanism that allows the model to "think longer" for complex videos and stop early for easy ones. |
| Outcome: | The proposed model outperforms specialized embedding-based models in video understanding tasks while remaining competitive on VideoQA tasks. |