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Found 12 papers, 6 papers with code
Stay Tuned: An Empirical Study of the Impact of Hyperparameters on LLM Tuning in Real-World Applications
Here, we provide recommended HP configurations for practical use-cases that represent a better starting point for practitioners, when considering two SOTA LLMs and two commonly used tuning methods.
Do These LLM Benchmarks Agree? Fixing Benchmark Evaluation with BenchBench
Despite the crucial role of BAT for benchmark builders and consumers, there are no standardized procedures for such agreement testing.
Genie: Achieving Human Parity in Content-Grounded Datasets Generation
Furthermore, we compare models trained on our data with models trained on human-written data -- ELI5 and ASQA for LFQA and CNN-DailyMail for Summarization.
Unitxt: Flexible, Shareable and Reusable Data Preparation and Evaluation for Generative AI
In the dynamic landscape of generative NLP, traditional text processing pipelines limit research flexibility and reproducibility, as they are tailored to specific dataset, task, and model combinations.
Improving Cross-Lingual Transfer through Subtree-Aware Word Reordering
Despite the impressive growth of the abilities of multilingual language models, such as XLM-R and mT5, it has been shown that they still face difficulties when tackling typologically-distant languages, particularly in the low-resource setting.
Efficient Benchmarking of Language Models
The increasing versatility of language models (LMs) has given rise to a new class of benchmarks that comprehensively assess a broad range of capabilities.
The Benefits of Bad Advice: Autocontrastive Decoding across Model Layers
Applying language models to natural language processing tasks typically relies on the representations in the final model layer, as intermediate hidden layer representations are presumed to be less informative.
On the Relation between Syntactic Divergence and Zero-Shot Performance
We explore the link between the extent to which syntactic relations are preserved in translation and the ease of correctly constructing a parse tree in a zero-shot setting.
HUJI-KU at MRP 2020: Two Transition-based Neural Parsers
This paper describes the HUJI-KU system submission to the shared task on CrossFramework Meaning Representation Parsing (MRP) at the 2020 Conference for Computational Language Learning (CoNLL), employing TUPA and the HIT-SCIR parser, which were, respectively, the baseline system and winning system in the 2019 MRP shared task.
HUJI-KU at MRP~2020: Two Transition-based Neural Parsers
This paper describes the HUJI-KU system submission to the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2020 Conference for Computational Language Learning (CoNLL), employing TUPA and the HIT-SCIR parser, which were, respectively, the baseline system and winning system in the 2019 MRP shared task.
Ranked #2 on
Semantic Parsing
on DRG (english, MRP 2020)
Fine-Grained Analysis of Cross-Linguistic Syntactic Divergences
The patterns in which the syntax of different languages converges and diverges are often used to inform work on cross-lingual transfer.
TUPA at MRP 2019: A Multi-Task Baseline System
This paper describes the TUPA system submission to the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2019 Conference for Computational Language Learning (CoNLL).
Ranked #2 on
UCCA Parsing
on CoNLL 2019
