Papers by Felix Faltings
Interactive Text Generation (2023.emnlp-main)
Copied to clipboard
Felix Faltings, Michel Galley, Kianté Brantley, Baolin Peng, Weixin Cai, Yizhe Zhang, Jianfeng Gao, Bill Dolan
| Challenge: | Advances in generative modeling have made it possible to automatically generate high-quality texts, code, and images, but they can be unsatisfactory in many respects. |
| Approach: | They propose a task that allows training generation models interactively without the costs of involving real users. |
| Outcome: | The proposed model trains with Imitation Learning without the cost of involving real users and is superior to non-interactive models. |
Enhancing Retrieval Systems with Inference-Time Logical Reasoning (2025.acl-short)
Copied to clipboard
| Challenge: | Existing retrieval methods rely on transforming user queries into vector representations and retrieving documents based on cosine similarity and static embeddings. |
| Approach: | They propose an inference-time logical reasoning framework that incorporates logical thinking into retrieval process. |
| Outcome: | The proposed method outperforms traditional retrieval methods on synthetic and real-world benchmarks on synthetic queries and datasets. |
Text Editing by Command (2021.naacl-main)
Copied to clipboard
| Challenge: | Recent work has focused on making such models more controllable and factually grounded. |
| Approach: | They propose a novel interactive text generation setting in which the user interacts with the system by issuing commands to edit existing text. |
| Outcome: | The proposed model outperforms baseline models and obtains positive results in automatic and human evaluations. |