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Long-Context Language Modeling with Parallel Context Encoding

Paper · 2024 · Howard Yen, Tianyu Gao and Danqi Chen

Summary

Decoder-only LLMs struggle with long inputs for three reasons. Positional encodings do not extrapolate well past the training length, full attention becomes expensive, and long instruction data is scarce. The authors propose CEPE (Context Expansion with Parallel Encoding), which attaches two trainable parts to a frozen decoder. The first is a small bidirectional encoder that processes extra context in independent chunks. The second is a set of cross-attention layers in every decoder block that read the encoder's final-layer outputs. Each chunk carries its own positions, so length limits disappear. Only the encoder's top-layer states are cached, so memory grows slowly. Applied to LLaMA-2-7B and trained on 8K-token sequences from a filtered RedPajama mix, CEPE keeps lowering perplexity up to 128K tokens. It runs much faster and uses far less memory than competing approaches. It also handles retrieved documents and many-shot in-context learning well. A distilled variant, CEPED, extends LLaMA-2-Chat using only unlabeled text. This makes long-context ability cheap to add to existing models.

Key points

  • CEPE extends LLaMA-2 from an 8K training length to 128K tokens. Compared with alternatives, it gives about 10× the throughput with roughly 1/6 of the memory, and each extra context token costs about 1/256 of the memory it would need in the decoder.
  • Training uses 20B tokens, around 1% of LLaMA-2's pre-training budget, on a single A100 80GB GPU. The data mixes RP train-filter (long ArXiv and Books documents of at least 8,192 tokens) with RP train-cat (concatenated documents) at a 2:1 ratio.
  • The encoder has 435M parameters. It follows the RoBERTa-large configuration but uses the LLaMA-2 vocabulary, and it is pre-trained as a masked LM on RedPajama for 100K steps.
  • CEPE outperforms prior methods such as REPLUG, YaRN and StreamingLLM on retrieval-augmented language modeling and open-domain QA, with Contriever as the retriever. Existing long-context models tend to degrade when given retrieved passages.
  • CEPED adds an auxiliary KL-divergence loss that distills the behavior of LLaMA-2-Chat. Ablations show this loss is essential for summarization and QALT, and the variant improves long-text understanding tasks. Code and models are available at github.com/princeton-nlp/CEPE.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Princeton UniversityPrinceton · United States90 works · 39,109 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksComputer Vision and Pattern Recognition · 2,869 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,320 works

Cite

Cite as Howard Yen, Tianyu Gao and Danqi Chen. 2024. Long-Context Language Modeling with Parallel Context Encoding. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2024.acl-long.142

@inproceedings{yen2024long,
  title = {{Long-Context Language Modeling with Parallel Context Encoding}},
  author = {Howard Yen, Tianyu Gao and Danqi Chen},
  booktitle = {Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2024},
  doi = {10.18653/v1/2024.acl-long.142},
  url = {https://doi.org/10.18653/v1/2024.acl-long.142}
}

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