{"schema_version":"onlylabs.public_signal.v1","title":"Cohere Repo: cohere-ai/human-feedback-paper","description":"Cohere repo signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/ffd60f02-d98a-442f-85cd-4d6e0cf09089","json_url":"https://onlylabs.fyi/signals/ffd60f02-d98a-442f-85cd-4d6e0cf09089/signal.json","generated_at":"2026-06-11T03:58:57.196013+00:00","org":{"slug":"cohere","name":"Cohere","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/cohere","dossier_json_url":"https://onlylabs.fyi/labs/cohere/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/ffd60f02-d98a-442f-85cd-4d6e0cf09089","signal_json":"https://onlylabs.fyi/signals/ffd60f02-d98a-442f-85cd-4d6e0cf09089/signal.json","source":"https://github.com/cohere-ai/human-feedback-paper","lab_dossier":"https://onlylabs.fyi/labs/cohere","lab_dossier_json":"https://onlylabs.fyi/labs/cohere/dossier.json","analysis":"https://onlylabs.fyi/analysis/cohere","analysis_json":"https://onlylabs.fyi/analysis/cohere/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/cohere/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":{"radar":"https://onlylabs.fyi/data-radar","radar_json":"https://onlylabs.fyi/data-radar.json","opportunities":"https://onlylabs.fyi/opportunities","opportunities_json":"https://onlylabs.fyi/opportunities.json","lanes":[{"key":"data","label":"Data demand","url":"https://onlylabs.fyi/data-radar/data","json_url":"https://onlylabs.fyi/data-radar/data/signals.json"}]}},"answer_pack":{"answer":"Cohere published cohere-ai/human-feedback-paper (Jupyter Notebook). 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model-adjacent, or product work before it appears in polished launch channels.."},{"subject":"cohere-ai/human-feedback-paper","predicate":"has source host","object":"github.com","text":"cohere-ai/human-feedback-paper has source host github.com."},{"subject":"cohere-ai/human-feedback-paper","predicate":"has lab","object":"Cohere","text":"cohere-ai/human-feedback-paper has lab Cohere."},{"subject":"cohere-ai/human-feedback-paper","predicate":"has signal desk","object":"repos","text":"cohere-ai/human-feedback-paper has signal desk repos."},{"subject":"cohere-ai/human-feedback-paper","predicate":"has source host","object":"github.com","text":"cohere-ai/human-feedback-paper has source host github.com."},{"subject":"cohere-ai/human-feedback-paper","predicate":"has repository","object":"cohere-ai/human-feedback-paper","text":"cohere-ai/human-feedback-paper has repository cohere-ai/human-feedback-paper."},{"subject":"cohere-ai/human-feedback-paper","predicate":"has language","object":"Jupyter 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This repository signal exposes tooling, eval, infrastructure, or model-adjacent work before it may appear in a launch post. High-signal details: repo cohere-ai/human-feedback-paper · language Jupyter Notebook. onlylabs links this event to 1 captured evidence page and 6 related repo signals. 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However, it is not clear which properties of a generated output this single `preference' score captures. We hypothesise that preference scores are subjective and open to undesirable biases. We critically analyse the use of human feedback for both training and evaluation, to verify whether it fully captures a range of crucial error criteria. We find that while preference scores have fairly good coverage, they under-represent important aspects like factuality. We further hypothesise that both preference scores and error annotation may be affected by confounders, and leverage instruction-tuned models to generate outputs that..."},"evidence_pages":[{"url":"https://github.com/cohere-ai/human-feedback-paper","final_url":"https://github.com/cohere-ai/human-feedback-paper","title":"cohere-ai/human-feedback-paper repository metadata","http_status":200,"content_type":"application/json","capture_method":"plain","fetched_at":"2026-06-11T03:58:57.196013+00:00","bytes":11961,"raw_path":"4b19464a4c30a67e7c33b2757c06ca97443a404163089da30340eacca91c7ae8.json","content_hash":"9850747437cdd67accec77884e7145b22ab9d60b1540ea2771d50839c21a762f","excerpt_chars":1200,"truncated":true,"excerpt":"cohere-ai/human-feedback-paper Description: Code and data from the paper 'Human Feedback is not Gold Standard' Language: Jupyter Notebook Stars: 21 Forks: 1 Open issues: 0 Created: 2023-09-22T10:48:22Z Pushed: 2026-05-05T13:32:46Z Default branch: main Fork: no Archived: no README: [Human Feedback is not Gold Standard](https://arxiv.org/abs/2309.16349) Tom Hosking, Phil Blunsom, Max Bartolo Description Human feedback has become the de facto standard for evaluating the performance of Large Language Models, and is increasingly being used as a training objective. However, it is not clear which properties of a generated output this single `preference' score captures. We hypothesise that preference scores are subjective and open to undesirable biases. We critically analyse the use of human feedback for both training and evaluation, to verify whether it fully captures a range of crucial error criteria. We find that while preference scores have fairly good coverage, they under-represent important aspects like factuality. We further hypothesise that both preference scores and error annotation may be affected by confounders, and leverage instruction-tuned models to generate outputs that..."}],"related_signals":[{"id":"b04cc72b-18a2-4d54-a2ff-ac76954c11eb","url":"https://onlylabs.fyi/signals/b04cc72b-18a2-4d54-a2ff-ac76954c11eb","source_url":"https://github.com/cohere-ai/cohere-azure-workshops","title":"cohere-ai/cohere-azure-workshops","context":"Jupyter Notebook","kind":{"key":"repo_new","label":"Repo"},"org":{"slug":"cohere","name":"Cohere","category":"frontier-lab"},"occurred_at":"2026-05-01T21:40:24+00:00","first_seen_at":"2026-06-05T05:43:00.174979+00:00","date_source":"source"},{"id":"dd51f42b-b547-4722-88fa-9e9b7ba7a5ae","url":"https://onlylabs.fyi/signals/dd51f42b-b547-4722-88fa-9e9b7ba7a5ae","source_url":"https://github.com/cohere-ai/cvm-measure","title":"cohere-ai/cvm-measure","context":"Makefile","kind":{"key":"repo_new","label":"Repo"},"org":{"slug":"cohere","name":"Cohere","category":"frontier-lab"},"occurred_at":"2026-04-10T19:07:01+00:00","first_seen_at":"2026-06-05T05:43:00.174979+00:00","date_source":"source"},{"id":"8f7c20cc-6531-4978-bb14-4e3b1d6c7b25","url":"https://onlylabs.fyi/signals/8f7c20cc-6531-4978-bb14-4e3b1d6c7b25","source_url":"https://github.com/cohere-ai/melody","title":"cohere-ai/melody","context":"Rust","kind":{"key":"repo_new","label":"Repo"},"org":{"slug":"cohere","name":"Cohere","category":"frontier-lab"},"occurred_at":"2025-10-10T17:32:30+00:00","first_seen_at":"2026-06-05T05:43:00.174979+00:00","date_source":"source"},{"id":"93fef8fe-ae41-4db2-aa12-e85c4b88fda0","url":"https://onlylabs.fyi/signals/93fef8fe-ae41-4db2-aa12-e85c4b88fda0","source_url":"https://github.com/cohere-ai/htn-2025-techtalk","title":"cohere-ai/htn-2025-techtalk","context":"Jupyter Notebook","kind":{"key":"repo_new","label":"Repo"},"org":{"slug":"cohere","name":"Cohere","category":"frontier-lab"},"occurred_at":"2025-09-04T01:21:23+00:00","first_seen_at":"2026-06-05T05:43:00.174979+00:00","date_source":"source"},{"id":"a8a38c03-91f9-452f-915d-2191c3a03296","url":"https://onlylabs.fyi/signals/a8a38c03-91f9-452f-915d-2191c3a03296","source_url":"https://github.com/cohere-ai/north-mcp-python-sdk","title":"cohere-ai/north-mcp-python-sdk","context":"Python","kind":{"key":"repo_new","label":"Repo"},"org":{"slug":"cohere","name":"Cohere","category":"frontier-lab"},"occurred_at":"2025-05-08T13:04:41+00:00","first_seen_at":"2026-06-05T05:43:00.174979+00:00","date_source":"source"},{"id":"f4ee1bbf-3fd2-4c59-9827-17979cbffa3c","url":"https://onlylabs.fyi/signals/f4ee1bbf-3fd2-4c59-9827-17979cbffa3c","source_url":"https://github.com/cohere-ai/cohere-workshop-march-17-2025","title":"cohere-ai/cohere-workshop-march-17-2025","context":"Jupyter Notebook","kind":{"key":"repo_new","label":"Repo"},"org":{"slug":"cohere","name":"Cohere","category":"frontier-lab"},"occurred_at":"2025-02-25T14:18:28+00:00","first_seen_at":"2026-06-05T05:43:00.174979+00:00","date_source":"source"}]}