{ "metadata": { "version": "1.2.0", "updated_at": "2026-10-19", "search_refreshed_at": "2026-10-19", "topic_id": "context-compression", "name": "Context compression", "definition": "Compress an instruction or context once and reuse the result across calls.", "record_count": 2, "topic_url": "https://programasweights.com/reading-list/guides/compilation/#context-compression", "source_url": "https://programasweights.com/reading-list/data/papers.json" }, "comparisons": [ { "title": "What gets reused?", "text": "Gisting caches a few instruction activations. Generative Adapter stores context in parameter updates. PAW saves a function as a program containing text and a neural module.", "source_ids": [ "2304.08467", "2411.05877", "2607.02512" ], "source_urls": [ "https://arxiv.org/abs/2304.08467", "https://arxiv.org/abs/2411.05877", "https://arxiv.org/html/2607.02512v1#S2" ], "source_labels": [ "Gist paper", "Generative Adapter paper", "PAW program definition" ] } ], "demos": [ ], "papers": [ { "id": "2304.08467", "title": "Learning to Compress Prompts with Gist Tokens", "authors": "Jesse Mu, Xiang Lisa Li, Noah Goodman", "first_public": "2023-04-17", "first_public_source": "https://arxiv.org/abs/2304.08467", "arxiv_first_submission": "2023-04-17", "date_label": "arXiv v1", "arxiv": "2304.08467", "paper_url": "https://arxiv.org/abs/2304.08467", "venue": "NeurIPS 2023", "lens": [ "context-compression" ], "related_terms": [ "gist tokens", "prompt compression", "activation caching", "amortized inference" ], "summary": "Compresses an instruction into a few reusable token activations.", "contribution": "Trains with an attention mask that forces later tokens to use gist activations in place of the original prompt.", "limitations": "Compression can reduce output quality. The cached activations are tied to the trained model.", "program_form": "Cached activations at a few gist-token positions.", "conditioning": "The instruction or prompt.", "execution": "The language model generates outputs while attending to the cached gist.", "relationship": "PAW cites gisting as another way to reuse instruction computation. Gisting stores activations; PAW stores a program with text and adaptation parameters.", "relationship_source": "https://arxiv.org/html/2607.02512v1#S10", "provenance": "arXiv record and paper.", "author_names": [ "Jesse Mu", "Xiang Lisa Li", "Noah Goodman" ], "record_url": "https://programasweights.com/reading-list/papers/2304.08467/", "legacy_record_url": "https://programasweights.com/reading-list/papers/#paper-2304-08467", "citation_date": "2023-04-17", "json_url": "https://programasweights.com/reading-list/papers/2304.08467/record.json", "markdown_url": "https://programasweights.com/reading-list/papers/2304.08467/index.md", "bibtex_url": "https://programasweights.com/reading-list/papers/2304.08467/citation.bib", "csl_url": "https://programasweights.com/reading-list/papers/2304.08467/citation.csl.json", "publication": { "title": "Learning to Compress Prompts with Gist Tokens", "author_names": [ "Jesse Mu", "Xiang Li", "Noah Goodman" ], "year": 2023, "type": "paper-conference", "container_title": "Advances in Neural Information Processing Systems", "url": "https://proceedings.neurips.cc/paper_files/paper/2023/hash/3d77c6dcc7f143aa2154e7f4d5e22d68-Abstract-Conference.html", "source_url": "https://proceedings.neurips.cc/paper_files/paper/2023/hash/3d77c6dcc7f143aa2154e7f4d5e22d68-Abstract-Conference.html", "verified_at": "2026-10-19", "volume": "36", "doi": "10.52202/075280-0848", "pages": "19327-19352" }, "publication_bibtex_url": "https://programasweights.com/reading-list/papers/2304.08467/publication.bib", "publication_csl_url": "https://programasweights.com/reading-list/papers/2304.08467/publication.csl.json", "method_comparisons": [ { "title": "What gets reused?", "text": "Gisting caches a few instruction activations. Generative Adapter stores context in parameter updates. PAW saves a function as a program containing text and a neural module.", "source_ids": [ "2304.08467", "2411.05877", "2607.02512" ], "source_urls": [ "https://arxiv.org/abs/2304.08467", "https://arxiv.org/abs/2411.05877", "https://arxiv.org/html/2607.02512v1#S2" ], "source_labels": [ "Gist paper", "Generative Adapter paper", "PAW program definition" ], "topic_id": "context-compression", "topic_label": "Context compression", "topic_url": "https://programasweights.com/reading-list/guides/compilation/#context-compression" } ] }, { "id": "2411.05877", "title": "Generative Adapter: Contextualizing Language Models in Parameters with A Single Forward Pass", "authors": "Tong Chen, Hao Fang, Patrick Xia, Xiaodong Liu, Benjamin Van Durme, Luke Zettlemoyer, Jianfeng Gao, Hao Cheng", "first_public": "2024-11-08", "first_public_source": "https://arxiv.org/abs/2411.05877", "arxiv_first_submission": "2024-11-08", "date_label": "arXiv v1", "arxiv": "2411.05877", "paper_url": "https://arxiv.org/abs/2411.05877", "venue": "ICLR 2025", "lens": [ "generated-adapters", "context-compression" ], "related_terms": [ "Generative Adapter", "contextualization", "fast weights", "streaming context" ], "summary": "Converts new context into low-rank model updates in one forward pass.", "contribution": "Trains an adapter generator with self-supervision. Tests learning from documents, demonstrations, and conversation history.", "limitations": "The adapter generator is tied to its frozen base model.", "program_form": "Additive low-rank adapters.", "conditioning": "Documents, demonstrations, or conversation history, including successive chunks of context.", "execution": "The adapted model answers new inputs using context stored in its parameter updates.", "relationship": "PAW cites this context-to-adapter method. 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Generative Adapter stores context in parameter updates. PAW saves a function as a program containing text and a neural module.", "source_ids": [ "2304.08467", "2411.05877", "2607.02512" ], "source_urls": [ "https://arxiv.org/abs/2304.08467", "https://arxiv.org/abs/2411.05877", "https://arxiv.org/html/2607.02512v1#S2" ], "source_labels": [ "Gist paper", "Generative Adapter paper", "PAW program definition" ], "topic_id": "context-compression", "topic_label": "Context compression", "topic_url": "https://programasweights.com/reading-list/guides/compilation/#context-compression" } ] } ] }