{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/dispatch-openai-chat-requesets","entry":"dispatch_openai_chat_requesets","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-25T09:33:49+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":12,"n_papers_ran":4,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":6,"n_samples_ran":4,"n_samples_fingerprinted":0,"n_places":12,"n_places_pointer_only":4,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":4,"unverified":2},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2505.07437","paper":"/paper/lead-iterative-data-selection-for-efficient","title":"LEAD: Iterative Data Selection for Efficient LLM Instruction Tuning","date":"2025-05-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HKUSTDial/LEAD","path":"src/eval/dispatch_openai_requests.py","file_url":"https://github.com/HKUSTDial/LEAD/blob/HEAD/src/eval/dispatch_openai_requests.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"64d6138fb03f75c7","mcp_get_code":{"code_sha256":"64d6138fb03f75c7"}},{"arxiv_id":"2410.24175","paper":"/paper/constraint-back-translation-improves-complex","title":"Constraint Back-translation Improves Complex Instruction Following of Large Language Models","date":"2024-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thu-keg/crab","path":"eval/dispatch_openai_requests.py","file_url":"https://github.com/thu-keg/crab/blob/HEAD/eval/dispatch_openai_requests.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"dcad9ac62979ca49","mcp_get_code":{"code_sha256":"dcad9ac62979ca49"}},{"arxiv_id":"2410.16208","paper":"/paper/compute-constrained-data-selection","title":"Compute-Constrained Data Selection","date":"2024-10-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"oseyosey/CCDS","path":"ccds/evaluation/eval/dispatch_openai_requests.py","file_url":"https://github.com/oseyosey/CCDS/blob/HEAD/ccds/evaluation/eval/dispatch_openai_requests.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"dfe736d72f7fb77f","mcp_get_code":{"code_sha256":"dfe736d72f7fb77f"}},{"arxiv_id":"2407.08770","paper":"/paper/model-surgery-modulating-llm-s-behavior-via","title":"Model Surgery: Modulating LLM's Behavior Via Simple Parameter Editing","date":"2024-07-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lucywang720/model-surgery","path":"eval/dispatch_openai_requests.py","file_url":"https://github.com/lucywang720/model-surgery/blob/HEAD/eval/dispatch_openai_requests.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"dcad9ac62979ca49","mcp_get_code":{"code_sha256":"dcad9ac62979ca49"}},{"arxiv_id":"2406.14144","paper":"/paper/finding-safety-neurons-in-large-language","title":"Finding Safety Neurons in Large Language Models","date":"2024-06-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"THU-KEG/SafetyNeuron","path":"src/eval/dispatch_openai_requests.py","file_url":"https://github.com/THU-KEG/SafetyNeuron/blob/HEAD/src/eval/dispatch_openai_requests.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"233355240185dc1a","mcp_get_code":{"code_sha256":"233355240185dc1a"}},{"arxiv_id":"2405.05008","paper":"/paper/adelie-aligning-large-language-models-on","title":"ADELIE: Aligning Large Language Models on Information Extraction","date":"2024-05-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"THU-KEG/ADELIE","path":"train4llama/eval/dispatch_openai_requests.py","file_url":"https://github.com/THU-KEG/ADELIE/blob/HEAD/train4llama/eval/dispatch_openai_requests.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"dcad9ac62979ca49","mcp_get_code":{"code_sha256":"dcad9ac62979ca49"}},{"arxiv_id":"2402.16705","paper":"/paper/selectit-selective-instruction-tuning-for","title":"SelectIT: Selective Instruction Tuning for LLMs via Uncertainty-Aware Self-Reflection","date":"2024-02-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Blue-Raincoat/SelectIT","path":"eval/eval/dispatch_openai_requests.py","file_url":"https://github.com/Blue-Raincoat/SelectIT/blob/HEAD/eval/eval/dispatch_openai_requests.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"dcad9ac62979ca49","mcp_get_code":{"code_sha256":"dcad9ac62979ca49"}},{"arxiv_id":"2402.04333","paper":"/paper/less-selecting-influential-data-for-targeted","title":"LESS: Selecting Influential Data for Targeted Instruction Tuning","date":"2024-02-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"princeton-nlp/less","path":"evaluation/eval/dispatch_openai_requests.py","file_url":"https://github.com/princeton-nlp/less/blob/HEAD/evaluation/eval/dispatch_openai_requests.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dfe736d72f7fb77f","mcp_get_code":{"code_sha256":"dfe736d72f7fb77f"}},{"arxiv_id":"2401.16405","paper":"/paper/scaling-sparse-fine-tuning-to-large-language","title":"Scaling Sparse Fine-Tuning to Large Language Models","date":"2024-01-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ducdauge/sft-llm","path":"eval/dispatch_openai_requests.py","file_url":"https://github.com/ducdauge/sft-llm/blob/HEAD/eval/dispatch_openai_requests.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dcad9ac62979ca49","mcp_get_code":{"code_sha256":"dcad9ac62979ca49"}},{"arxiv_id":"2401.15006","paper":"/paper/airavata-introducing-hindi-instruction-tuned","title":"Airavata: Introducing Hindi Instruction-tuned LLM","date":"2024-01-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ai4bharat/indicinstruct","path":"eval/dispatch_openai_requests.py","file_url":"https://github.com/ai4bharat/indicinstruct/blob/HEAD/eval/dispatch_openai_requests.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"4967e8b8db0b2f2c","mcp_get_code":{"code_sha256":"4967e8b8db0b2f2c"}},{"arxiv_id":"2401.10768","paper":"/paper/mitigating-hallucinations-of-large-language","title":"Knowledge Verification to Nip Hallucination in the Bud","date":"2024-01-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fanqiwan/KCA","path":"examination/dispatch_openai_requests.py","file_url":"https://github.com/fanqiwan/KCA/blob/HEAD/examination/dispatch_openai_requests.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"dcad9ac62979ca49","mcp_get_code":{"code_sha256":"dcad9ac62979ca49"}},{"arxiv_id":"2310.06830","paper":"/paper/lemur-harmonizing-natural-language-and-code","title":"Lemur: Harmonizing Natural Language and Code for Language Agents","date":"2023-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"OpenLemur/Lemur","path":"xchat/eval/dispatch_openai_requests.py","file_url":"https://github.com/OpenLemur/Lemur/blob/HEAD/xchat/eval/dispatch_openai_requests.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"8140036b8f8671e0","mcp_get_code":{"code_sha256":"8140036b8f8671e0"}}]}