In this survey, researchers discuss the challenges and limitations of evaluating large language models (LLMs) due to their vast scale and diverse applications. They highlight the importance of understanding these models’ performance in various contexts, including short- and long-range dependencies, to improve their effectiveness. The authors explore several methods for evaluating LLMs, such as using different datasets, context lengths, and training techniques, and discuss their trade-offs regarding precision loss and context information discarding. They also introduce new techniques, including Attention Sinks and KV cache eviction algorithms, to mitigate these issues. The survey concludes by emphasizing the need for a comprehensive understanding of LLMs’ strengths and limitations to optimize their performance in real-world applications.
Context Length Benchmark
The article presents a context length benchmark, which shows the distribution of context lengths across different datasets. This benchmark is essential for evaluating LLMs, as it helps researchers understand how these models perform based on the length of the input context. The graph indicates that context lengths range from 1 to 1,900K, with varying proportions in each dataset.
Focused Transformer
The authors discuss a contrastive training technique called Focused Transformer, which improves LLMs’ performance by scaling their context. This method involves adding an attention sink to the model, allowing it to focus on specific parts of the input context and improve its precision. The authors demonstrate that this technique reduces the precision loss due to context information discarding, making LLMs more effective in various applications.
Attention is All You Need
The article references a popular paper titled "Attention is All You Need," which introduced the Transformer architecture and demonstrated its effectiveness in machine translation tasks. The authors highlight the importance of attention mechanisms in LLMs, as they enable these models to focus on relevant parts of the input context and improve their performance.
Bloom
The authors introduce a new language model called Bloom, which is designed to address the limitations of existing LLMs. Bloom uses an efficient streaming architecture and incorporates an attention sink to mitigate the precision loss due to context information discarding. The authors show that Bloom outperforms other state-of-the-art models in various language processing tasks.
Palm 2 Technical Report
The article references a technical report on the Palm 2 model, which is designed to improve the efficiency and scalability of LLMs. The report discusses the challenges of training large language models and presents several techniques for addressing these challenges.
Longformer
The authors discuss the Longformer model, which is designed to handle long-range dependencies in language processing tasks. This model uses a transformer architecture with a novel attention mechanism that allows it to process input sequences of varying lengths. The authors show that Longformer outperforms other state-of-the-art models in various language processing tasks.
Evaluating Large Language Models: A Survey
In this survey, the authors discuss the challenges and limitations of evaluating large language models (LLMs) due to their vast scale and diverse applications. They highlight the importance of understanding these models’ performance in various contexts, including short- and long-range dependencies, to improve their effectiveness. The authors explore several methods for evaluating LLMs, such as using different datasets, context lengths, and training techniques, and discuss their trade-offs regarding precision loss and context information discarding. They also introduce new techniques, including Attention Sinks and KV cache eviction algorithms, to mitigate these issues. The survey concludes by emphasizing the need for a comprehensive understanding of LLMs’ strengths and limitations to optimize their performance in real-world applications.
