Success of Large Language Models in RST Discourse Parsing

Grunnleggende konsepter

Large language models significantly enhance RST discourse parsing, demonstrating state-of-the-art results and generalizability.

Sammendrag

Large language models have revolutionized RST discourse parsing, showing superior performance and generalizability across benchmark datasets. The study explores the potential benefits of using LLMs for RST parsing, showcasing significant advancements in the field.

Statistikk

Llama 2 with 70 billion parameters obtained state-of-the-art results in bottom-up strategy.
Outperformed current SOTA models by around 2-3 points on RST-DT.
Achieved smaller degradation when evaluated on RST-DT despite being trained with GUM corpus.
Parser demonstrated generalizability across different datasets.

Sitater

"LLMs have demonstrated remarkable success in various NLP tasks due to their large numbers of parameters and ease of availability."
"Our parsers showed generalizability when evaluated on RST-DT, indicating promising future directions for RST discourse parsing."

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Can we obtain significant success in RST discourse parsing by using Large Language Models?

by Aru Maekawa,... klokken arxiv.org 03-11-2024

https://arxiv.org/pdf/2403.05065.pdf

Dypere Spørsmål

How do large language models impact other areas of natural language processing beyond discourse parsing?

Large Language Models (LLMs) have significantly impacted various areas of Natural Language Processing (NLP) beyond discourse parsing. One key area is machine translation, where LLMs have shown improved performance in translating text between different languages. Additionally, sentiment analysis has benefited from LLMs by providing more accurate and nuanced understanding of the emotions expressed in text. In question answering tasks, LLMs have enhanced the ability to retrieve relevant information and provide coherent responses to user queries. Moreover, automatic summarization has seen improvements with LLMs generating concise and informative summaries of longer texts.

What are potential drawbacks or limitations of relying heavily on large language models for NLP tasks?

While Large Language Models offer significant benefits in NLP tasks, there are several drawbacks and limitations associated with relying heavily on them. One major concern is the computational resources required to train and fine-tune these models, which can be expensive and time-consuming. Additionally, LLMs may suffer from biases present in the training data, leading to biased outputs that perpetuate stereotypes or discrimination. Another limitation is the lack of interpretability in some cases where it's challenging to understand how the model arrived at a particular decision or output. Furthermore, deploying large models in real-time applications may pose challenges due to their high memory requirements and slower inference times compared to smaller models.

How can the success of encoder-only PLMs be compared to that of decoder-only LLMs in discourse parsing?

The success of encoder-only Pre-trained Language Models (PLMs) can be compared to that of decoder-only Large Language Models (LLMs) in discourse parsing based on several factors. Encoder-only PLMs focus on encoding input sequences into fixed-length representations without considering generation capabilities like decoding sentences sequentially as done by decoder-only LLMs.
In terms of efficiency, encoder-only PLMs tend to perform better when dealing with downstream tasks requiring only encoding functionality such as classification or clustering since they are optimized for this purpose.
On the other hand, decoder-only LLMs excel at generative tasks like text completion or machine translation where sequential generation plays a crucial role.
Overall performance comparison would depend on specific task requirements; while encoder-only PLMs might outperform decoder-based approaches for certain types of analyses due to their specialized nature towards encoding information efficiently without unnecessary overhead related to decoding processes typically found within larger-scale generative architectures like decoder-based systems used by many Large Language Models today.