Papers by Tobias Schnabel
Symbolic Prompt Program Search: A Structure-Aware Approach to Efficient Compile-Time Prompt Optimization (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Recent work on prompt programs has focused on simple prompt programs or assumed that the structure of a prompt program is fixed. |
| Approach: | They propose a framework to perform symbolic prompt program search for compile-time optimizations of prompt programs. |
| Outcome: | The proposed framework improves performance of complex prompts on instruction tuning, pipeline tuning, prompt compression and more. |
Keep It Simple: Unsupervised Simplification of Multi-Paragraph Text (2021.acl-long)
Copied to clipboard
| Challenge: | a novel approach to text simplification learns to balance a reward across three properties: fluency, salience and simplicity. |
| Approach: | They propose a novel algorithm to optimize the reward which proposes several candidate simplifications and a realistic text comprehension task as an evaluation method for text simplification. |
| Outcome: | The proposed model outperforms strong supervised baselines on the English news domain and can help people complete a comprehension task an average of 18% faster while retaining accuracy. |
SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization (2022.tacl-1)
Copied to clipboard
| Challenge: | Recent studies have shown that even state-of-the-art pre-trained language models can generate inconsistent summaries in more than 70% of all cases. |
| Approach: | They propose a method that enables NLI models to be used for inconsistency detection by segmenting documents into sentence units and aggregating scores between pairs of sentences. |
| Outcome: | The proposed method achieves state-of-the-art accuracy of 74.4% on six large inconsistency detection datasets. |