Papers by Dennis Ulmer
Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcity (2022.findings-emnlp)
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| Challenge: | Using low-resource languages, we assess the quality of uncertainty estimates from a wide array of approaches, but with more data. |
| Approach: | They train models on sub-sampled datasets in three different languages to assess the confidence of a neural classifier. |
| Outcome: | The proposed models train on sub-sampled datasets in three different languages and show that the quality of uncertainty estimates suffers with more data. |
Bootstrapping LLM-based Task-Oriented Dialogue Agents via Self-Talk (2024.findings-acl)
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| Challenge: | Large language models (LLMs) are powerful dialogue agents, but specializing them towards fulfilling a specific function can be prohibitive in terms of feasibility, time, and resources. |
| Approach: | They propose a method for training large language models by enabling "self-talk" they propose supervised fine-tuning of LLMs to improve quality of dialogues . |
| Outcome: | The proposed method generates training data via "self-talk" of LLMs that can be refined and utilized for supervised fine-tuning. |
TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification (2024.findings-acl)
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| Challenge: | Large Language Model (LLM) services and models often come with legal rules on who can use them and how they must use them. |
| Approach: | They propose a method that uses adversarial suffixes to get an answer from a target LLM. |
| Outcome: | The proposed method detects the LLMs with over 95% true positive rate at under 0.2% false positive rate even after a single interaction. |
Experimental Standards for Deep Learning in Natural Language Processing Research (2022.findings-emnlp)
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Dennis Ulmer, Elisa Bassignana, Max Müller-Eberstein, Daniel Varab, Mike Zhang, Rob van der Goot, Christian Hardmeier, Barbara Plank
| Challenge: | a lack of common experimental standards remains an open challenge to the field at large . |
| Approach: | They propose to distill discussions on experimental standards into a single, widely-applicable methodology. |
| Outcome: | Using best practices, we can strengthen experimental evidence, improve reproducibility and enable scientific progress. |
Non-Exchangeable Conformal Language Generation with Nearest Neighbors (2024.findings-eacl)
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| Challenge: | Existing methods to evaluate reliability of generated text are lacking in natural language generation. |
| Approach: | They propose a non-exchangeable conformal prediction method that provides bounds on coverage . they validated their method with k-NN retrieval and show that it produces encouraging results . |
| Outcome: | The proposed method produces encouraging results in machine translation and language modeling tasks. |