Summary of Large Language Models Aren’t All That You Need, by Kiran Voderhobli Holla et al.
Large Language Models aren’t all that you need
by Kiran Voderhobli Holla, Chaithanya Kumar, Aryan Singh
First submitted to arxiv on: 1 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents an innovative approach to solving the SemEval 2023 Task 2: MultiCoNER II challenge, which involves recognizing complex named entities across multiple languages. The authors evaluate two architectures: a traditional Conditional Random Fields model and a Large Language Model (LLM) fine-tuned with a customized head. Key contributions include decaying auxiliary loss, triplet token blending, and task-optimal heads. These novel techniques are explored with GPT-3 and various hyperparameter settings to achieve state-of-the-art results on the development and test datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using artificial intelligence to improve how computers recognize complex names in different languages. The authors tried two ways of doing this: one that’s been used before, and a new way that uses large language models. They experimented with different techniques, like combining information from nearby words, to make the model better. Their results show that these new approaches can really help improve how well computers recognize complex names. |
Keywords
* Artificial intelligence * Gpt * Hyperparameter * Large language model * Token
