Papers with entity-centric tasks
Instructed Language Models with Retrievers Are Powerful Entity Linkers (2023.emnlp-main)
Copied to clipboard
| Challenge: | Generative approaches powered by large language models have demonstrated emergent abilities in tasks that require complex reasoning abilities. |
| Approach: | They propose a sequence-to-sequence training objective with instruction-tuning that enables casual language models to perform entity linking over knowledge bases. |
| Outcome: | The proposed framework outperforms existing approaches with +6.8 F1 points gain on average and huge advantage in training data efficiency and compute consumption. |
DOCENT: Learning Self-Supervised Entity Representations from Large Document Collections (2021.eacl-main)
Copied to clipboard
Yury Zemlyanskiy, Sudeep Gandhe, Ruining He, Bhargav Kanagal, Anirudh Ravula, Juraj Gottweis, Fei Sha, Ilya Eckstein
| Challenge: | Using pre-trained models, we learn to jointly predict words and entities from multiple text sources without any human supervision. |
| Approach: | They propose to learn rich self-supervised entity representations from large amounts of associated text. |
| Outcome: | The proposed models outperform baseline models on downstream tasks in the TV-Movies domain, and scale to very large corpora. |
KEPLET: Knowledge-Enhanced Pretrained Language Model with Topic Entity Awareness (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Pre-trained language models (PLMs) have shown their superiority by pre-training on unstructured text corpus and then fine-tuning on downstream tasks. |
| Approach: | They propose a Knowledge-Enhanced Pre-trained LanguagE model with Topic entity awareness that incorporates the interactions between tokens and mentioned entities in pre-training. |
| Outcome: | The proposed model incorporates the interactions between tokens and mentioned entities in pre-training and is more effective on entity-centric tasks. |
Failure Modes in Multi-Hop QA: The Weakest Link Effect and the Recognition Bottleneck (2026.acl-long)
Copied to clipboard
| Challenge: | Existing studies have identified a position bias in Large Language Models that causes them to overlook information at certain positions. |
| Approach: | They propose a semantic probe to disentangle position bias in Large Language Models . they propose MFAI to steer attention towards selected positions . |
| Outcome: | The proposed model can locate and integrate information at certain positions even in noisy, long-context settings. |