Natural Language Understanding

Natural Language Understanding is an important field of Natural Language Processing which contains various tasks such as text classification, natural language inference and story comprehension. Applications enabled by natural language understanding range from question answering to automated reasoning.

Source: Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1–12 of 12 papers

Title	Date	Tasks	Status	Hype
Mixture-of-Agents Enhances Large Language Model Capabilities	Jun 7, 2024	Language ModelingLanguage Modelling	CodeCode Available	7
DataComp-LM: In search of the next generation of training sets for language models	Jun 17, 2024	Language ModellingMMLU	CodeCode Available	7
Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond	Apr 26, 2023	Language ModellingNatural Language Understanding	CodeCode Available	6
Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG	Jan 15, 2025	Natural Language UnderstandingRAG	CodeCode Available	5
MedCare: Advancing Medical LLMs through Decoupling Clinical Alignment and Knowledge Aggregation	Jun 25, 2024	DiversityNatural Language Understanding	CodeCode Available	5
MING-MOE: Enhancing Medical Multi-Task Learning in Large Language Models with Sparse Mixture of Low-Rank Adapter Experts	Apr 13, 2024	DiversityLanguage Modeling	CodeCode Available	5
TaskWeaver: A Code-First Agent Framework	Nov 29, 2023	Natural Language Understanding	CodeCode Available	5
How to Design Translation Prompts for ChatGPT: An Empirical Study	Apr 5, 2023	Machine TranslationNatural Language Understanding	CodeCode Available	5
Decoder Tuning: Efficient Language Understanding as Decoding	Dec 16, 2022	DecoderNatural Language Understanding	CodeCode Available	4
What Makes Good In-Context Examples for GPT-3?	Jan 17, 2021	Few-Shot LearningNatural Language Understanding	CodeCode Available	4
Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective	Oct 16, 2022	Coreference ResolutionMultiple-choice	CodeCode Available	4
Attention Is All You Need	Jun 12, 2017	Abstractive Text SummarizationAll	CodeCode Available	3

Show:10 25 50

All datasets DialoGLUE fewshot DialoGLUE full PDP60 STREUSLE

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ConvBERT-DG	Average	74.6	—	Unverified
2	ConvBERT-DG + Pre + Multi	Average	73.8	—	Unverified
3	mslm	Average	73.49	—	Unverified
4	ConvBERT + Pre + Multi	Average	68.22	—	Unverified
5	BanLanGen	Average	39.16	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ConvBERT + Pre + Multi	Average	86.89	—	Unverified
2	mslm	Average	85.83	—	Unverified
3	ConvBERT-DG + Pre + Multi	Average	85.34	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Subword-level Transformer LM	Accuracy	58.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RoBERTa + Linear	Full F1 (Preps)	78.2	—	Unverified