Recent research has proposed a method to expand context windows in transformers using recurrent memory, addressing limitations of computing scalability. The team introduced the BABILong framework for NLP model evaluation in handling lengthy dispersed data, achieving a new record for the largest sequence size handled by a single model and analyzing GPT-4 and RAG on question-answering tasks with millions of tokens.
Advances in Machine Learning
Recent advances in Machine Learning have resulted in larger input sizes for models. However, the quadratic scaling of computing needed for transformer self-attention poses limitations. A viable method for expanding context windows in transformers has been presented using recurrent memory, allowing for better handling of lengthy contexts divided into smaller chunks.
Introducing BABILong Framework
The BABILong framework has been introduced as a generative benchmark for testing NLP models on processing arbitrarily lengthy documents containing scattered facts. It assesses how well generative models manage lengthy contexts and separates pertinent details from crucial information.
Improving bAbI Benchmark
The team has focused on improving the bAbI benchmark, originally created to assess fundamental reasoning features. They have shared that the generated benchmarks, such as bAbI and BABILong, are not susceptible to data leaking, unlike many other NLP benchmarks.
Primary Contributions
- BABILong, a generative benchmark for evaluating NLP models’ effectiveness, has been introduced.
- Analysis of GPT-4 and RAG on complex question-answering tasks has been conducted.
- A new record for the largest sequence size handled by a single model has been achieved through the evaluation of a recurrent memory transformer on input texts up to 11 million tokens.
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