Papers with MIT License
GraphQL Query Generation: A Large Training and Benchmarking Dataset (2024.emnlp-industry)
Copied to clipboard
Manish Kesarwani, Sambit Ghosh, Nitin Gupta, Shramona Chakraborty, Renuka Sindhgatta, Sameep Mehta, Carlos Eberhardt, Dan Debrunner
| Challenge: | GraphQL is a powerful query language for APIs, but crafting complex GraphqL queries can be challenging. |
| Approach: | a team of researchers has created a large-scale, cross-domain text-to-GraphQL query operation dataset . the dataset includes 10,940 training triples spanning 185 cross-source data stores and 957 test triples over 14 data stores. |
| Outcome: | The proposed dataset includes 10,940 training triples and 957 test triples over 14 data stores. |
Instructional Fingerprinting of Large Language Models (2024.naacl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) require considerable cost to train from scratch . fingerprinting is essential to protect intellectual property and to ensure downstream users and developers adhere to their license terms. |
| Approach: | They propose a method for fingerprinting Large language models that implants a private key into the model to generate specific text when the key is present. |
| Outcome: | The proposed method prevents publisher overclaim and maintains robustness against fingerprint guessing and parameter-efficient training. |
Building Sentiment Lexicons for Mainland Scandinavian Languages Using Machine Translation and Sentence Embeddings (2022.lrec-1)
Copied to clipboard
| Challenge: | a simple but effective method to build sentiment lexicons for the three Mainland Scandinavian languages is proposed . a number of experiments with Scandinavian language datasets yield state-of-the-art results using a rule-based sentiment analysis algorithm. |
| Approach: | They propose a simple but effective method to build sentiment lexicons for the three Mainland Scandinavian languages. |
| Outcome: | The proposed method is based on the English Sentiwordnet and a thesaurus in one of the target languages. |
NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data (2024.emnlp-main)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) is a core component of natural language processing, present in a variety of applications such as medical coding, financial news analysis, or legal documents parsing. |
| Approach: | They propose to use Large Language Models (LLMs) to create NuNER, a compact language representation model specialized in the Named Entity Recognition task. |
| Outcome: | The proposed model outperforms similar-sized foundation models in the few-shot regime and is based on a human-annotated dataset. |