{"schema_version":"onlylabs.public_signal.v1","title":"Amazon (Nova) Repo: amazon-science/llm-asymptotic-decoding","description":"Amazon (Nova) repo signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/c95d3df5-97cf-4ded-8d92-da21093016f7","json_url":"https://onlylabs.fyi/signals/c95d3df5-97cf-4ded-8d92-da21093016f7/signal.json","generated_at":"2026-06-11T03:57:04.235623+00:00","org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/amazon","dossier_json_url":"https://onlylabs.fyi/labs/amazon/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/c95d3df5-97cf-4ded-8d92-da21093016f7","signal_json":"https://onlylabs.fyi/signals/c95d3df5-97cf-4ded-8d92-da21093016f7/signal.json","source":"https://github.com/amazon-science/llm-asymptotic-decoding","lab_dossier":"https://onlylabs.fyi/labs/amazon","lab_dossier_json":"https://onlylabs.fyi/labs/amazon/dossier.json","analysis":"https://onlylabs.fyi/analysis/amazon","analysis_json":"https://onlylabs.fyi/analysis/amazon/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/amazon/evidence.json","category":"https://onlylabs.fyi/frontier","category_json":"https://onlylabs.fyi/frontier.json","category_feed":"https://onlylabs.fyi/frontier/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json","topic":null,"topic_signals_json":null,"topic_feed":null,"data_business":null},"answer_pack":{"answer":"Amazon (Nova) published amazon-science/llm-asymptotic-decoding (Jupyter Notebook). 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Then, consider to use our proposed REAL sampling and/or APD sampling. In FactualityPrompt, we show that APD + REAL sampling outperforms 13 state-of-the-art sampling methods. Our baselines include typical ([Meister et al., 2022](https://arxiv.org/abs/2202.00666)), eta ([Hewitt et al., 2022](https://arxiv.org/pdf/2210.15191)), EDT ([Zhang et al., 2024](https://arxiv.org/abs/2403.14541)), adaptive ([Zhu et al., 2024](https://arxiv.org/abs/2402.18223)), microstat ([Basu et al., 2021](https://arxiv.org/abs/2007.14966)), EAD w/o ELI ([Arora et al., 2023](https://arxiv.org/abs/2302.06784)) factual ([Lee et al., 2022](https://arxiv.org/abs/2206.04624)) top-p ([Holtzman et al., 2020](https://arxiv.org/pdf/1904.09751)), top-k ([Fan et..."},"evidence_pages":[{"url":"https://github.com/amazon-science/llm-asymptotic-decoding","final_url":"https://github.com/amazon-science/llm-asymptotic-decoding","title":"amazon-science/llm-asymptotic-decoding repository metadata","http_status":200,"content_type":"application/json","capture_method":"plain","fetched_at":"2026-06-11T03:57:04.235623+00:00","bytes":19283,"raw_path":"d6e074cb92cc893b68f1d6d9c21279ff312e1b385c93e8e362e3115566e46b46.json","content_hash":"7daf9c17f8f3f572eff341c6ab5d79f46ab57bbc4063414e7a22717de3efbc40","excerpt_chars":1200,"truncated":true,"excerpt":"amazon-science/llm-asymptotic-decoding Language: Jupyter Notebook License: NOASSERTION Stars: 11 Forks: 0 Open issues: 14 Created: 2024-10-03T17:55:51Z Pushed: 2026-02-20T18:27:41Z Default branch: main Fork: no Archived: no README: Extrapolating an Infinite LLM♾🤖 Introduction Assuming you have a series of LLMs with different sizes that are trained on the same data and you want to increase the factuality and diversity of the text sampled from your largest LLM. Then, consider to use our proposed REAL sampling and/or APD sampling. In FactualityPrompt, we show that APD + REAL sampling outperforms 13 state-of-the-art sampling methods. 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