Researchers explore the link between compression and intelligence in large language models (LLMs), finding a strong correlation between compression efficiency and task performance.
- Compression and intelligence are closely linked, a concept discussed by researchers for a long time.
- Recent advancements in large language models (LLMs) have fueled discussions on compression and intelligence.
- According to compression theory, any predictive model can be seen as a lossless compressor.
- Language modeling can be viewed as a form of compression, with LLMs demonstrating strong data compression abilities.
- Limited empirical evidence exists on the connection between compression and intelligence.
- The study aims to fill the gap by investigating if efficient text encoding indicates higher intelligence.
- Intelligence is defined practically, focusing on performance in tasks like knowledge, common sense, coding, and mathematical reasoning.
- Raw data from different domains is gathered to evaluate LLMs' compression efficiency and task performance.
- A strong linear correlation is found between LLMs' compression efficiency and downstream task performance.
- The Pearson correlation coefficient is approximately -0.95 for each intelligence domain evaluated.
- Superior compression is indicative of higher intelligence in LLMs.
- Using compression efficiency as a metric can help prevent overfitting and test contamination.
- Diverse LLMs created using different training data, tokenizers, computation methods, and architectures are evaluated.
- Intelligence is measured based on performance across multiple tasks, including knowledge, common sense, coding, and mathematical reasoning.
- Average bits per character (BPC) is used as a metric to gauge compression efficiency.
- The context window size is standardized to 19900 tokens across all evaluations for consistency.
- Base models are focused on rather than fine-tuned models to avoid specialization bias.
- Different corpora are selected to highlight various aspects of models' abilities without overlapping pre-training data.
- The Min K per prob method detects potential data exposure during pre-training.
- Eight series of general-purpose language models, including diverse organizations and varying sizes, are investigated.
- Cutting-edge models specialized in coding and mathematical reasoning are incorporated into the study.
- Models are evaluated consistently using few-shot and zero-shot methods following benchmark norms.
- Pearson correlation coefficient and root mean square error (RMSE) measure the intelligence-compression correlation.
- A universal linear correlation between compression efficiency and intelligence is established across diverse models.
- Differences in linear correlation among benchmarks are attributed to factors like performance saturation and dataset mismatch.
- The study highlights the importance of matching compression corpora with specific areas of study for strong linear correlation.
- A mixed corpus combining Python and math corpora shows a stronger linear correlation for tasks requiring multiple domain abilities.
- Compression efficiency strongly correlates with LLMs' performance in downstream tasks across various domains.
- Superior data compression by LLMs indicates higher intelligence in practical task performance.
- Using diverse training data and architectures ensures generalizable conclusions about LLMs' intelligence.
- Standardizing context window sizes ensures consistent evaluation of LLMs' compression efficiency.
- Base models provide a more general measure of compression abilities compared to fine-tuned models.
- Matching compression corpora with specific study areas enhances the accuracy of linear correlations.
- The Min K per prob method effectively detects pre-training data exposure in LLMs.
- Few-shot and zero-shot evaluation methods ensure fair comparison across different LLMs.
- Pearson correlation coefficient and RMSE are reliable metrics for quantifying intelligence-compression relationships.
- Mixed corpora can better evaluate tasks requiring multiple domain abilities compared to single-domain corpora.
- "Compression and intelligence are closely linked, a concept discussed by researchers for a long time."
- "Recent advancements in large language models (LLMs) have fueled discussions on compression and intelligence."
- "According to compression theory, any predictive model can be seen as a lossless compressor."
- "Language modeling can be viewed as a form of compression, with LLMs demonstrating strong data compression abilities."
- "Limited empirical evidence exists on the connection between compression and intelligence."
- "The study aims to fill the gap by investigating if efficient text encoding indicates higher intelligence."
- "Intelligence is defined practically, focusing on performance in tasks like knowledge, common sense, coding, and mathematical reasoning."
- "Raw data from different domains is gathered to evaluate LLMs' compression efficiency and task performance."
- "A strong linear correlation is found between LLMs' compression efficiency and downstream task performance."
- "The Pearson correlation coefficient is approximately -0.95 for each intelligence domain evaluated."
- "Superior compression is indicative of higher intelligence in LLMs."
- "Using compression efficiency as a metric can help prevent overfitting and test contamination."
- "Diverse LLMs created using different training data, tokenizers, computation methods, and architectures are evaluated."
- "Intelligence is measured based on performance across multiple tasks, including knowledge, common sense, coding, and mathematical reasoning."
- "Average bits per character (BPC) is used as a metric to gauge compression efficiency."
- "The context window size is standardized to 19900 tokens across all evaluations for consistency."
- "Base models are focused on rather than fine-tuned models to avoid specialization bias."
- "Different corpora are selected to highlight various aspects of models' abilities without overlapping pre-training data."
- "The Min K per prob method detects potential data exposure during pre-training."
- "Eight series of general-purpose language models, including diverse organizations and varying sizes, are investigated."
- Gathering raw data from different domains to evaluate LLMs' abilities comprehensively.
- Standardizing context window sizes across evaluations for consistency in results.
- Focusing on base models rather than fine-tuned ones to avoid specialization bias.
- Selecting diverse corpora that align with specific study areas without overlapping pre-training data.
- Using the Min K per prob method to detect potential pre-training data exposure.
- Compression theory posits that any predictive model can be seen as a lossless compressor.
- A strong linear correlation exists between LLMs' compression efficiency and downstream task performance.
- The Pearson correlation coefficient for this relationship is approximately -0.95 for each intelligence domain evaluated.
- Average bits per character (BPC) is used as a metric to gauge compression efficiency in LLMs.
- Standardizing context window sizes to 19900 tokens ensures consistent evaluation of LLMs' abilities.
None provided.
Superior data compression by large language models indicates higher practical intelligence across various tasks.
- Use diverse training data and architectures to ensure generalizable conclusions about LLMs' intelligence.
- Standardize context window sizes across evaluations for consistent results in assessing LLMs' abilities.
- Focus on base models rather than fine-tuned ones to avoid specialization bias in evaluating LLMs' capabilities.
- Select diverse corpora that align with specific study areas without overlapping pre-training data for accurate results.
- Use the Min K per prob method to detect potential pre-training data exposure in evaluating LLMs.