Summary Effective Long-Context Scaling of Foundation Models arxiv.org
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One Line
Meta's long-context language models (LLMs) are highly proficient in a range of tasks including coding, math, conversations, and search queries, ensuring safety and offering valuable insights.
Slides
Slide Presentation (13 slides)
Key Points
- Meta presents a series of long-context language models (LLMs) that support context windows of up to 32,768 tokens.
- The models achieve consistent improvements on most regular tasks and significant improvements on long-context tasks compared to L LAMA 2.
- Context length is an important axis of scaling LLMs, and the models can continually improve their performance as the context length increases.
- The models are pretrained from L LAMA 2 checkpoints with additional 400 billion tokens formed as long training sequences.
- The models achieve stronger overall performance than gpt-3.5-turbo-16k on a series of long-context benchmarks.
- The models maintain similar safety performance compared to L LAMA 2 CHAT and are safer and less biased compared to other open-source LLMs.
- The paper explores the evaluation and scaling of large language models trained on code, including positional encodings and methods for extending sequence length.
- The experiments on extrapolation capabilities and the generation of self-instruct data demonstrate the potential and applicability of these models in real-world scenarios.
Summaries
21 word summary
Meta's long-context language models (LLMs) excel in long-context tasks, coding, math, conversations, and search queries, while maintaining safety and providing insights.
67 word summary
Meta has developed long-context language models (LLMs) that outperform previous models on long-context tasks and improve on regular tasks. Long texts in the pretrain dataset are not crucial, and long context continual pretraining is more efficient. The models achieve strong results in coding, math, knowledge-intensive tasks, multi-turn conversations, and multi-document search queries. They maintain safety performance and provide insights into positional encodings and sequence length extension methods.
137 word summary
Meta has developed long-context language models (LLMs) that can support context windows of up to 32,768 tokens. These models consistently outperform previous models on long-context tasks and show improvements on regular tasks. Abundant long texts in the pretrain dataset are not crucial for achieving strong performance, and long context continual pretraining is more efficient. The models achieve on-par or stronger results compared to previous models on standard short-context tasks, particularly in coding, math, and knowledge-intensive tasks. They also demonstrate competitive performance in multi-turn conversation data and multi-document search query answering data. Safety performance is maintained while being safer and less biased compared to other open-source LLMs. The models present insights into positional encodings, sequence length extension methods, extrapolation capabilities, and the generation of self-instruct data, contributing to the understanding and applicability of these models in real-world scenarios.
422 word summary
Meta has developed long-context language models (LLMs) that can support context windows of up to 32,768 tokens. These models have undergone extensive evaluation and show consistent improvements on regular tasks and significant improvements on long-context tasks compared to previous models. In fact, the 70B variant of the model can outperform gpt-3.5-turbo-16k on a suite of long-context tasks using a cost-effective instruction tuning procedure.
The models provide a detailed analysis of their method's individual components, examining the limitations of position encodings and exploring different design choices in the pretraining process. They find that having abundant long texts in the pretrain dataset is not crucial for achieving strong performance and that long context continual pretraining is more efficient and equally effective compared to pretraining from scratch with long sequences.
The models demonstrate a power-law scaling behavior, consistently benefiting from more contexts. Performance continually improves as the context length increases up to 32,768 tokens.
To build long-context LLMs with superior performance, the models continually pretrain from L LAMA 2 checkpoints with additional 400 billion tokens formed as long training sequences. They achieve on-par or stronger results compared to previous models on standard short-context tasks, particularly in coding, math, and knowledge-intensive tasks.
The models explore a simple and cost-effective procedure for instruction tuning without human-annotated data. They leverage a pre-built short-prompt dataset and augment it with synthetic self-instruct long data generated by L LAMA 2 CHAT. This approach leads to stronger overall performance on long-context benchmarks covering question answering, summarization, and multi-document aggregation tasks.
The models conduct human evaluations comparing their generation quality with other proprietary models. They achieve competitive performance in terms of helpfulness, honesty, and harmlessness in multi-turn conversation data and multi-document search query answering data.
The models perform ablation experiments to justify their design choices, finding that their proposed positional encoding refinement performs the best. Adjusting the length distribution of the pretrain data does not provide major benefits, and improvements mostly come from the quality of the data itself.
The models evaluate their safety performance on three standard academic benchmarks and maintain similar safety performance compared to previous models while being safer and less biased compared to other open-source LLMs.
In conclusion, the models present long-context LLMs that achieve strong performance on both short and long-context tasks. The paper also explores the evaluation and scaling of large language models trained on code, providing insights into positional encodings, sequence length extension methods, extrapolation capabilities, and the generation of self-instruct data. These findings contribute to the understanding and applicability of these models in real-world scenarios.
556 word summary
Meta has developed a series of long-context language models (LLMs) that can support context windows of up to 32,768 tokens. These models are created through continual pretraining from L LAMA 2 using longer training sequences and an upsampled dataset of long texts. The models have been extensively evaluated on language modeling, synthetic context probing tasks, and various research benchmarks. The results show consistent improvements on regular tasks and significant improvements on long-context tasks compared to L LAMA 2. In fact, the 70B variant of the model can outperform gpt-3.5-turbo-16k on a suite of long-context tasks when using a cost-effective instruction tuning procedure.
The models also provide a detailed analysis of their method's individual components. They examine the limitations of L LAMA's position encodings in modeling long dependencies and explore the impact of different design choices in the pretraining process. Their experiments suggest that having abundant long texts in the pretrain dataset is not crucial for achieving strong performance. They also find that long context continual pretraining is more efficient and equally effective compared to pretraining from scratch with long sequences.
The models demonstrate a power-law scaling behavior, showing their ability to consistently benefit from more contexts. They highlight that context length is an important factor in scaling LLMs, with performance continually improving as the context length increases up to 32,768 tokens.
To build long-context LLMs with superior performance, the models continually pretrain from L LAMA 2 checkpoints with additional 400 billion tokens formed as long training sequences. The smaller variants are trained with longer sequences, while the larger variants are trained with shorter sequences. The models achieve on-par or stronger results compared to L LAMA 2 on standard short-context tasks, particularly in coding, math, and knowledge intensive tasks.
The models also explore a simple and cost-effective procedure for instruction tuning without human-annotated data. They leverage a pre-built short-prompt dataset and augment it with synthetic self-instruct long data generated by L LAMA 2 CHAT. This approach leads to stronger overall performance compared to gpt-3.5-turbo-16k on a series of long-context benchmarks covering question answering, summarization, and multi-document aggregation tasks.
The models conduct human evaluations to compare their generation quality with other proprietary models. The evaluations focus on multi-turn conversation data and multi-document search query answering data. The models achieve competitive performance against proprietary models in terms of helpfulness, honesty, and harmlessness.
The models perform ablation experiments to justify their design choices, including positional encoding variants, pretraining data mix, and training curriculum. They find that their proposed positional encoding refinement performs the best among the explored variants. They also observe that adjusting the length distribution of the pretrain data does not provide major benefits, and the improvements mostly come from the quality of the data itself.
The models evaluate their safety performance on three standard academic benchmarks: TruthfulQA, ToxiGen, and BOLD. They maintain similar safety performance compared to L LAMA 2 CHAT and are safer and less biased compared to other open-source LLMs.
In conclusion, the models present a series of long-context LLMs that achieve strong performance on both short and long-context tasks. The paper also explores the evaluation and scaling of large language models trained on code, providing insights into positional encodings, sequence length extension methods, extrapolation capabilities, and the generation of self-instruct data. These findings contribute to the understanding and applicability of these models in real-world scenarios.
949 word summary
Meta presents a series of long-context language models (LLMs) that support context windows of up to 32,768 tokens. These models are built through continual pretraining from L LAMA 2 with longer training sequences and on a dataset where long texts are upsampled. The models are evaluated extensively on language modeling, synthetic context probing tasks, and a wide range of research benchmarks. On research benchmarks, the models achieve consistent improvements on most regular tasks and significant improvements on long-context tasks compared to L LAMA 2. With a cost-effective instruction tuning procedure, the 70B variant of the model can surpass gpt-3.5-turbo-16k's overall performance on a suite of long-context tasks.
The models also provide an in-depth analysis of the individual components of their method. They delve into L LAMA's position encodings and discuss their limitations in modeling long dependencies. They also examine the impact of various design choices in the pretraining process, including the data mix and the training curriculum of sequence lengths. Their experiments suggest that having abundant long texts in the pretrain dataset is not the key to achieving strong performance, and they verify that long context continual pretraining is more efficient and similarly effective compared to pretraining from scratch with long sequences.
The models demonstrate a clear power-law scaling behavior with respect to context lengths, showing their ability to consistently benefit from more contexts. They also suggest that context length is another important axis of scaling LLMs, and the models can continually improve their performance as the context length increases up to 32,768 tokens.
To build long-context LLMs with superior performance, the models continually pretrain from L LAMA 2 checkpoints with additional 400 billion tokens formed as long training sequences. The smaller 7B/13B variants are trained with 32,768-token sequences, while the 34B/70B variants are trained with 16,384-token sequences. The models are evaluated on standard short-context tasks and achieve on-par or stronger results compared to L LAMA 2, particularly on coding, math, and knowledge intensive tasks.
The models also explore a simple and cost-effective procedure for instruction tuning without human-annotated data. They leverage a pre-built large and diverse short-prompt dataset and augment it with synthetic self-instruct long data generated by L LAMA 2 CHAT. The models achieve stronger overall performance than gpt-3.5-turbo-16k on a series of long-context benchmarks covering question answering, summarization, and multi-document aggregation tasks.
The models are continually pretrained from the 7B L LAMA 2 checkpoint with increased sequence length while keeping the same number of tokens per batch. They train all models for a total of 400B tokens over 100,000 steps. The larger 34B/70B models require a smaller learning rate to achieve monotonically decreasing validation losses.
The models also conduct human evaluations to compare the generation quality of the instruction finetuned model with other proprietary models. The evaluation focuses on multi-turn conversation data and multi-document search query answering data. The models achieve competitive performance against proprietary models in terms of helpfulness, honesty, and harmlessness.
The models perform ablation experiments to justify their design choices, including positional encoding variants, pretraining data mix, and training curriculum. They find that their proposed positional encoding refinement performs the best among the explored variants. They also observe that adjusting the length distribution of the pretrain data does not provide major benefits, and the improvements mostly come from the quality of the data itself.
The models evaluate their safety performance on three standard academic benchmarks: TruthfulQA, ToxiGen, and BOLD. They maintain similar safety performance compared to L LAMA 2 CHAT and are safer and less biased compared to other open-source LLMs.
In conclusion, the models present a series of long-context LLMs that achieve strong performance on both short and long-context tasks
The paper "Effective Long-Context Scaling of Foundation Models" explores the evaluation and scaling of large language models trained on code. The authors reference several related works that provide insights into training and evaluating large language models. They also mention the importance of long-context scaling in improving the performance and capabilities of these models.
The paper presents a theoretical analysis of positional encodings (RoPE) and introduces two methods for extending the sequence length of a trained transformer model: Position Interpolation (PI) and Adjusted Base Frequency (ABF). The authors compare the cosine similarity between consecutive images of the positional embeddings for both methods and provide mathematical proofs for their bounds. They conclude that both methods can effectively adapt to extended sequence lengths, but ABF exhibits higher granularity and may be more suitable for distinguishing between positional embedding images.
To evaluate the extrapolation capabilities of their models, the authors conduct experiments using validation loss and a synthetic FIRST-SENTENCE-RETRIEVAL task. The results show that their 70B model with either RoPE ABF or xPos ABF maintains low loss in the extrapolation area, indicating effective extrapolation abilities. In the FIRST-SENTENCE-RETRIEVAL task, some performance degradation is observed during extrapolation, but overall, the models perform well.
The paper also discusses the generation of self-instruct data using Llama 2 Chat. The authors describe a process for automatically generating long-context instruct data from short-context models. They split long documents into smaller chunks and use prompts to generate question-answer pairs. The questions are based on the text chunks and are used as reading comprehension tests over the entire document. The authors provide prompts for generating normal answer and short answer data, along with corresponding templates for constructing long question-answer data.
Overall, the paper provides valuable insights into the evaluation and scaling of large language models trained on code. The theoretical analysis of positional encodings and the comparison of different methods for extending sequence length contribute to the understanding of long-context scaling. The experiments on extrapolation capabilities and the generation of self-instruct data demonstrate the potential and applicability of these models in real-world scenarios.