{"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/get-completion","entry":"get_completion","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+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":26,"n_papers_ran":8,"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":31,"n_samples_ran":8,"n_samples_fingerprinted":1,"n_places":31,"n_places_pointer_only":18,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":8,"unverified":23},"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":"2607.17653","paper":"/paper/arxiv-2607-17653","title":"LFM: Leveraging Foundation Models for Source-Free Universal Domain Adaptation","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"iamjingli/LFM","path":"train_target.py","file_url":"https://github.com/iamjingli/LFM/blob/HEAD/train_target.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4528e48fcffa0b3d","mcp_get_code":{"code_sha256":"4528e48fcffa0b3d"}},{"arxiv_id":"2506.08607","paper":"/paper/sample-efficient-demonstration-selection-for","title":"Sample Efficient Demonstration Selection for In-Context Learning","date":"2025-06-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kiranpurohit/case","path":"Code/LLM_experiments/CASE_Gsm8K_selection.py","file_url":"https://github.com/kiranpurohit/case/blob/HEAD/Code/LLM_experiments/CASE_Gsm8K_selection.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f2f4fbbf9e2b7fe5","mcp_get_code":{"code_sha256":"f2f4fbbf9e2b7fe5"}},{"arxiv_id":"2501.00656","paper":"/paper/2-olmo-2-furious","title":"2 OLMo 2 Furious","date":"2024-12-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"allenai/olmes","path":"oe_eval/models/litellm.py","file_url":"https://github.com/allenai/olmes/blob/HEAD/oe_eval/models/litellm.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":"7162dfdf480a8995","mcp_get_code":{"code_sha256":"7162dfdf480a8995"}},{"arxiv_id":"2411.03877","paper":"/paper/explora-efficient-exemplar-subset-selection","title":"EXPLORA: Efficient Exemplar Subset Selection for Complex Reasoning","date":"2024-11-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kiranpurohit/explora","path":"StrategyQA/explora_api.py","file_url":"https://github.com/kiranpurohit/explora/blob/HEAD/StrategyQA/explora_api.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"00a219f9f3fe0f93","mcp_get_code":{"code_sha256":"00a219f9f3fe0f93"}},{"arxiv_id":"2411.03877","paper":"/paper/explora-efficient-exemplar-subset-selection","title":"EXPLORA: Efficient Exemplar Subset Selection for Complex Reasoning","date":"2024-11-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kiranpurohit/explora","path":"TabMwp/chatgpt_explora+sc_tabmwp_old_dev.py","file_url":"https://github.com/kiranpurohit/explora/blob/HEAD/TabMwp/chatgpt_explora%2Bsc_tabmwp_old_dev.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0f457018f13bc946","mcp_get_code":{"code_sha256":"0f457018f13bc946"}},{"arxiv_id":"2411.03877","paper":"/paper/explora-efficient-exemplar-subset-selection","title":"EXPLORA: Efficient Exemplar Subset Selection for Complex Reasoning","date":"2024-11-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kiranpurohit/explora","path":"TabMwp/explora_tabmwp_chatpgt-new.py","file_url":"https://github.com/kiranpurohit/explora/blob/HEAD/TabMwp/explora_tabmwp_chatpgt-new.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"745710d33641d7ee","mcp_get_code":{"code_sha256":"745710d33641d7ee"}},{"arxiv_id":"2410.20445","paper":"/paper/trajagent-an-agent-framework-for-unified","title":"TrajAgent: An Agent Framework for Unified Trajectory Modelling","date":"2024-10-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tsinghua-fib-lab/trajagent","path":"evaluate_model.py","file_url":"https://github.com/tsinghua-fib-lab/trajagent/blob/HEAD/evaluate_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2afa3ef771935a8e","mcp_get_code":{"code_sha256":"2afa3ef771935a8e"}},{"arxiv_id":"2410.14309","paper":"/paper/logu-long-form-generation-with-uncertainty","title":"LoGU: Long-form Generation with Uncertainty Expressions","date":"2024-10-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rhyang2021/logu","path":"src/eval/gen_atomics.py","file_url":"https://github.com/rhyang2021/logu/blob/HEAD/src/eval/gen_atomics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"912d444dbd510f87","mcp_get_code":{"code_sha256":"912d444dbd510f87"}},{"arxiv_id":"2410.12327","paper":"/paper/neuron-based-personality-trait-induction-in","title":"Neuron-based Personality Trait Induction in Large Language Models","date":"2024-10-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RUCAIBox/NPTI","path":"NPTI/code/gpt4_score.py","file_url":"https://github.com/RUCAIBox/NPTI/blob/HEAD/NPTI/code/gpt4_score.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a277bdb894d81f0a","mcp_get_code":{"code_sha256":"a277bdb894d81f0a"}},{"arxiv_id":"2406.07971","paper":"/paper/it-takes-two-on-the-seamlessness-between","title":"It Takes Two: On the Seamlessness between Reward and Policy Model in RLHF","date":"2024-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"taiminglu/seamless","path":"code/retrival/gpt/retrival.py","file_url":"https://github.com/taiminglu/seamless/blob/HEAD/code/retrival/gpt/retrival.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fb97e66f814910e9","mcp_get_code":{"code_sha256":"fb97e66f814910e9"}},{"arxiv_id":"2404.19097","paper":"/paper/exploring-the-capability-of-llms-in","title":"Exploring the Capability of LLMs in Performing Low-Level Visual Analytic Tasks on SVG Data Visualizations","date":null,"month_inferred_from_arxiv_id":"2024-04","title_source":"archive","repo":"lebretou/svg_taxonomy","path":"llm/completion.py","file_url":"https://github.com/lebretou/svg_taxonomy/blob/HEAD/llm/completion.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e3d745f6a2079de4","mcp_get_code":{"code_sha256":"e3d745f6a2079de4"}},{"arxiv_id":"2403.17983","paper":"/paper/is-watermarking-llm-generated-code-robust","title":"Is The Watermarking Of LLM-Generated Code Robust?","date":"2024-03-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"uiuc-arc/llm-code-watermark","path":"lpw/perturb_watermark.py","file_url":"https://github.com/uiuc-arc/llm-code-watermark/blob/HEAD/lpw/perturb_watermark.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8b74f1e0438ca473","mcp_get_code":{"code_sha256":"8b74f1e0438ca473"}},{"arxiv_id":"2402.16181","paper":"/paper/how-can-llm-guide-rl-a-value-based-approach","title":"How Can LLM Guide RL? A Value-Based Approach","date":"2024-02-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"agentification/language-integrated-vi","path":"alfworld/utils.py","file_url":"https://github.com/agentification/language-integrated-vi/blob/HEAD/alfworld/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"217b3cd8424bfb72","mcp_get_code":{"code_sha256":"217b3cd8424bfb72"}},{"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":"eval/gpt_judge/gpt_judge.py","file_url":"https://github.com/fanqiwan/KCA/blob/HEAD/eval/gpt_judge/gpt_judge.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":"cbcc22537dba8d33","mcp_get_code":{"code_sha256":"cbcc22537dba8d33"}},{"arxiv_id":"2401.03506","paper":"/paper/diarizationlm-speaker-diarization-post","title":"DiarizationLM: Speaker Diarization Post-Processing with Large Language Models","date":"2024-01-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google/speaker-id","path":"DiarizationLM/run_finetuned_gpt.py","file_url":"https://github.com/google/speaker-id/blob/HEAD/DiarizationLM/run_finetuned_gpt.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":"89a117349f149bd9","mcp_get_code":{"code_sha256":"89a117349f149bd9"}},{"arxiv_id":"2311.16714","paper":"/paper/embodied-multi-modal-agent-trained-by-an-llm","title":"Embodied Multi-Modal Agent trained by an LLM from a Parallel TextWorld","date":"2023-11-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stevenyangyj/emma-alfworld","path":"LAVIS/emma_utils.py","file_url":"https://github.com/stevenyangyj/emma-alfworld/blob/HEAD/LAVIS/emma_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ebee9e987c95636b","mcp_get_code":{"code_sha256":"ebee9e987c95636b"}},{"arxiv_id":"2311.14740","paper":"/paper/autokg-efficient-automated-knowledge-graph","title":"AutoKG: Efficient Automated Knowledge Graph Generation for Language Models","date":"2023-11-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wispcarey/autokg","path":"utils.py","file_url":"https://github.com/wispcarey/autokg/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8a0cdfe6982a9654","mcp_get_code":{"code_sha256":"8a0cdfe6982a9654"}},{"arxiv_id":"2311.09648","paper":"/paper/event-causality-is-key-to-computational-story","title":"Event Causality Is Key to Computational Story Understanding","date":"2023-11-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"insundaycathy/event-causality-extraction","path":"story_eval/OpenAI_API_score.py","file_url":"https://github.com/insundaycathy/event-causality-extraction/blob/HEAD/story_eval/OpenAI_API_score.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a940e320c829ce6b","mcp_get_code":{"code_sha256":"a940e320c829ce6b"}},{"arxiv_id":"2311.09648","paper":"/paper/event-causality-is-key-to-computational-story","title":"Event Causality Is Key to Computational Story Understanding","date":"2023-11-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"insundaycathy/event-causality-extraction","path":"story_eval/OpenAI_API_OpenMEVA_EN.py","file_url":"https://github.com/insundaycathy/event-causality-extraction/blob/HEAD/story_eval/OpenAI_API_OpenMEVA_EN.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"396131e07c01083b","mcp_get_code":{"code_sha256":"396131e07c01083b"}},{"arxiv_id":"2310.10467","paper":"/paper/stance-detection-with-collaborative-role","title":"Stance Detection with Collaborative Role-Infused LLM-Based Agents","date":"2023-10-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tsinghua-fib-lab/cola","path":"cola.py","file_url":"https://github.com/tsinghua-fib-lab/cola/blob/HEAD/cola.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6b026cbed566405d","mcp_get_code":{"code_sha256":"6b026cbed566405d"}},{"arxiv_id":"2310.09168","paper":"/paper/explore-instruct-enhancing-domain-specific","title":"Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration","date":"2023-10-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fanqiwan/Explore-Instruct","path":"eval/chatgpt_score.py","file_url":"https://github.com/fanqiwan/Explore-Instruct/blob/HEAD/eval/chatgpt_score.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":"b9b91448d55793d7","mcp_get_code":{"code_sha256":"b9b91448d55793d7"}},{"arxiv_id":"2310.09168","paper":"/paper/explore-instruct-enhancing-domain-specific","title":"Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration","date":"2023-10-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fanqiwan/Explore-Instruct","path":"eval/chatgpt_generate.py","file_url":"https://github.com/fanqiwan/Explore-Instruct/blob/HEAD/eval/chatgpt_generate.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":"10d36e6036039fa6","mcp_get_code":{"code_sha256":"10d36e6036039fa6"}},{"arxiv_id":"2308.12067","paper":"/paper/instructiongpt-4-a-200-instruction-paradigm","title":"InstructionGPT-4: A 200-Instruction Paradigm for Fine-Tuning MiniGPT-4","date":"2023-08-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"waltonfuture/InstructionGPT-4","path":"cc_sbu_align_test/generate_gpt_score.py","file_url":"https://github.com/waltonfuture/InstructionGPT-4/blob/HEAD/cc_sbu_align_test/generate_gpt_score.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"51806ca8d533b092","mcp_get_code":{"code_sha256":"51806ca8d533b092"}},{"arxiv_id":"2308.06921","paper":"/paper/codehelp-using-large-language-models-with","title":"CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes","date":null,"month_inferred_from_arxiv_id":"2023-08","title_source":"archive","repo":"liffiton/Gen-Ed","path":"dev/batch_llm.py","file_url":"https://github.com/liffiton/Gen-Ed/blob/HEAD/dev/batch_llm.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"70368cc569c94a4c","mcp_get_code":{"code_sha256":"70368cc569c94a4c"}},{"arxiv_id":"2308.01263","paper":"/paper/xstest-a-test-suite-for-identifying","title":"XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models","date":"2023-08-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"paul-rottger/exaggerated-safety","path":"evaluation/classify_completions_gpt.py","file_url":"https://github.com/paul-rottger/exaggerated-safety/blob/HEAD/evaluation/classify_completions_gpt.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"CC-BY-4.0","inline_ok":false,"code_sha256_prefix":"63088b4652873640","mcp_get_code":{"code_sha256":"63088b4652873640"}},{"arxiv_id":"2307.09254","paper":"/paper/pac-neural-prediction-set-learning-to","title":"Selective Generation for Controllable Language Models","date":"2023-07-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ml-postech/selective-generation","path":"generation/api_gen_answer.py","file_url":"https://github.com/ml-postech/selective-generation/blob/HEAD/generation/api_gen_answer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c61efb7cefa9daf5","mcp_get_code":{"code_sha256":"c61efb7cefa9daf5"}},{"arxiv_id":"2307.09254","paper":"/paper/pac-neural-prediction-set-learning-to","title":"Selective Generation for Controllable Language Models","date":"2023-07-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ml-postech/selective-generation","path":"generation/api_gen_samples.py","file_url":"https://github.com/ml-postech/selective-generation/blob/HEAD/generation/api_gen_samples.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7e05f2471cc1bc8c","mcp_get_code":{"code_sha256":"7e05f2471cc1bc8c"}},{"arxiv_id":"2306.06283","paper":"/paper/14-examples-of-how-llms-can-transform","title":"14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon","date":"2023-06-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qai222/llm_organic_synthesis","path":"hackathon/models_openai/inference.py","file_url":"https://github.com/qai222/llm_organic_synthesis/blob/HEAD/hackathon/models_openai/inference.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c75d1f1ed97dc9dd","mcp_get_code":{"code_sha256":"c75d1f1ed97dc9dd"}},{"arxiv_id":"2303.11366","paper":"/paper/reflexion-language-agents-with-verbal","title":"Reflexion: Language Agents with Verbal Reinforcement Learning","date":"2023-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"noahshinn/reflexion","path":"alfworld_runs/utils.py","file_url":"https://github.com/noahshinn/reflexion/blob/HEAD/alfworld_runs/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a7a0cc9d35a647f1","mcp_get_code":{"code_sha256":"a7a0cc9d35a647f1"}},{"arxiv_id":"2212.08061","paper":"/paper/on-second-thought-let-s-not-think-step-by","title":"On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning","date":"2022-12-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"salt-nlp/chain-of-thought-bias","path":"08_qa_bad.py","file_url":"https://github.com/salt-nlp/chain-of-thought-bias/blob/HEAD/08_qa_bad.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0b6870c8a431430b","mcp_get_code":{"code_sha256":"0b6870c8a431430b"}},{"arxiv_id":"2212.06801","paper":"/paper/a-fine-grained-comparison-of-pragmatic","title":"A fine-grained comparison of pragmatic language understanding in humans and language models","date":"2022-12-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jennhu/lm-pragmatics","path":"query_hf.py","file_url":"https://github.com/jennhu/lm-pragmatics/blob/HEAD/query_hf.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a4c1d0a4e77349c1","mcp_get_code":{"code_sha256":"a4c1d0a4e77349c1"}}]}