{"schema_version":"onlylabs.public_signal.v1","title":"Google (DeepMind / Gemini) Repo: google-deepmind/inli","description":"Google (DeepMind / Gemini) repo signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/c82dd875-4247-45df-8c3c-b049373157c2","json_url":"https://onlylabs.fyi/signals/c82dd875-4247-45df-8c3c-b049373157c2/signal.json","generated_at":"2026-06-11T04:00:38.164218+00:00","org":{"slug":"google-deepmind","name":"Google (DeepMind / Gemini)","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/google-deepmind","dossier_json_url":"https://onlylabs.fyi/labs/google-deepmind/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/c82dd875-4247-45df-8c3c-b049373157c2","signal_json":"https://onlylabs.fyi/signals/c82dd875-4247-45df-8c3c-b049373157c2/signal.json","source":"https://github.com/google-deepmind/inli","lab_dossier":"https://onlylabs.fyi/labs/google-deepmind","lab_dossier_json":"https://onlylabs.fyi/labs/google-deepmind/dossier.json","analysis":"https://onlylabs.fyi/analysis/google-deepmind","analysis_json":"https://onlylabs.fyi/analysis/google-deepmind/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/google-deepmind/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":"Google (DeepMind / Gemini) published google-deepmind/inli. This repository signal exposes tooling, eval, infrastructure, or model-adjacent work before it may appear in a launch post. 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repositories can expose organization build priorities early, especially around internal tooling, eval infrastructure, data systems, deployment, and agent workflows.","required_sources":[{"label":"signal_json","url":"https://onlylabs.fyi/signals/c82dd875-4247-45df-8c3c-b049373157c2/signal.json","required":true},{"label":"source","url":"https://github.com/google-deepmind/inli","required":true},{"label":"dossier_json","url":"https://onlylabs.fyi/labs/google-deepmind/dossier.json","required":true},{"label":"analysis_evidence_json","url":"https://onlylabs.fyi/analysis/google-deepmind/evidence.json","required":true},{"label":"topic_signals_json","url":null,"required":false},{"label":"data_radar_json","url":null,"required":false}],"expected_output":["one-paragraph source-grounded interpretation","category-specific implication","confidence and missing evidence","recommended next source to inspect"],"prompt_seed":"Using only the linked onlylabs JSON, captured source context, and cited evidence, 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This repository signal exposes tooling, eval, infrastructure, or model-adjacent work before it may appear in a launch post. 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For more details on the design and content of the dataset, please see [our paper](https://arxiv.org/abs/2501.07719) Data Format Our raw dataset is split into three csv files. * Size of training dataset: 32,000 * Size of test dataset: 4,000 * Size of validation dataset: 4,000 In each of these files, one row represents a premise and four corresponding hypotheses. These files include..."},"evidence_pages":[{"url":"https://github.com/google-deepmind/inli","final_url":"https://github.com/google-deepmind/inli","title":"google-deepmind/inli repository metadata","http_status":200,"content_type":"application/json","capture_method":"plain","fetched_at":"2026-06-11T04:00:38.164218+00:00","bytes":10305,"raw_path":"ff610057a44cd6532a3bd368cd076c739b77f99ae84518b2d21cb9c98bf5eb6d.json","content_hash":"d0380d348fdc432e283cdb94cd7a134b9cb57b67708d680ff210acd14a762445","excerpt_chars":1200,"truncated":true,"excerpt":"google-deepmind/inli License: CC-BY-SA-4.0 Stars: 5 Forks: 1 Open issues: 0 Created: 2025-01-09T22:38:48Z Pushed: 2025-01-23T04:16:58Z Default branch: main Fork: no Archived: no README: Implied NLI Code & resources for the paper [Entailed Between the Lines: Incorporating Implication into NLI](https://arxiv.org/abs/2501.07719) Dataset Overview **Implied NLI**, or **INLI** is a corpus of 10k premises that mirror real-world communication and 40k hypotheses that are implied, explicit, neutral, and contradictory. * Number of examples: 40,000 * Number of labels: 4 (implied entailment, explicit entailment, neutral, contradictory) On top of the raw data, we also include all prompts used to generate INLI, all human annotations described in the paper, and the outputs of the LLM experiments described in the paper. For more details on the design and content of the dataset, please see [our paper](https://arxiv.org/abs/2501.07719) Data Format Our raw dataset is split into three csv files. * Size of training dataset: 32,000 * Size of test dataset: 4,000 * Size of validation dataset: 4,000 In each of these files, one row represents a premise and four corresponding hypotheses. 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