{ "results": { "tenetur-sit-9095_lsat-rc_cot": { "acc,none": 0.1821561338289963, "acc_stderr,none": 0.023577062969635087, "alias": "tenetur-sit-9095_lsat-rc_cot" }, "tenetur-sit-9095_lsat-lr_cot": { "acc,none": 0.1450980392156863, "acc_stderr,none": 0.01561097532356473, "alias": "tenetur-sit-9095_lsat-lr_cot" }, "tenetur-sit-9095_lsat-ar_cot": { "acc,none": 0.2217391304347826, "acc_stderr,none": 0.02745149660405892, "alias": "tenetur-sit-9095_lsat-ar_cot" }, "tenetur-sit-9095_logiqa_cot": { "acc,none": 0.21405750798722045, "acc_stderr,none": 0.01640667626359583, "alias": "tenetur-sit-9095_logiqa_cot" }, "tenetur-sit-9095_logiqa2_cot": { "acc,none": 0.20610687022900764, "acc_stderr,none": 0.01020561459385605, "alias": "tenetur-sit-9095_logiqa2_cot" } }, "group_subtasks": { "tenetur-sit-9095_logiqa2_cot": [], "tenetur-sit-9095_logiqa_cot": [], "tenetur-sit-9095_lsat-ar_cot": [], "tenetur-sit-9095_lsat-lr_cot": [], "tenetur-sit-9095_lsat-rc_cot": [] }, "configs": { "tenetur-sit-9095_logiqa2_cot": { "task": "tenetur-sit-9095_logiqa2_cot", "group": "logikon-bench", "dataset_path": "cot-leaderboard/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "data/google/gemma-7b/tenetur-sit-9095-logiqa2.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n", "doc_to_target": "{{answer}}", "doc_to_choice": "{{options}}", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "tenetur-sit-9095_logiqa_cot": { "task": "tenetur-sit-9095_logiqa_cot", "group": "logikon-bench", "dataset_path": "cot-leaderboard/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "data/google/gemma-7b/tenetur-sit-9095-logiqa.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n", "doc_to_target": "{{answer}}", "doc_to_choice": "{{options}}", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "tenetur-sit-9095_lsat-ar_cot": { "task": "tenetur-sit-9095_lsat-ar_cot", "group": "logikon-bench", "dataset_path": "cot-leaderboard/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "data/google/gemma-7b/tenetur-sit-9095-lsat-ar.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n", "doc_to_target": "{{answer}}", "doc_to_choice": "{{options}}", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "tenetur-sit-9095_lsat-lr_cot": { "task": "tenetur-sit-9095_lsat-lr_cot", "group": "logikon-bench", "dataset_path": "cot-leaderboard/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "data/google/gemma-7b/tenetur-sit-9095-lsat-lr.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n", "doc_to_target": "{{answer}}", "doc_to_choice": "{{options}}", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "tenetur-sit-9095_lsat-rc_cot": { "task": "tenetur-sit-9095_lsat-rc_cot", "group": "logikon-bench", "dataset_path": "cot-leaderboard/cot-eval-traces", "dataset_kwargs": { "data_files": { "test": "data/google/gemma-7b/tenetur-sit-9095-lsat-rc.parquet" } }, "test_split": "test", "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n", "doc_to_target": "{{answer}}", "doc_to_choice": "{{options}}", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } } }, "versions": { "tenetur-sit-9095_logiqa2_cot": 0.0, "tenetur-sit-9095_logiqa_cot": 0.0, "tenetur-sit-9095_lsat-ar_cot": 0.0, "tenetur-sit-9095_lsat-lr_cot": 0.0, "tenetur-sit-9095_lsat-rc_cot": 0.0 }, "n-shot": { "tenetur-sit-9095_logiqa2_cot": 0, "tenetur-sit-9095_logiqa_cot": 0, "tenetur-sit-9095_lsat-ar_cot": 0, "tenetur-sit-9095_lsat-lr_cot": 0, "tenetur-sit-9095_lsat-rc_cot": 0 }, "config": { "model": "vllm", "model_args": "pretrained=google/gemma-7b,revision=main,dtype=bfloat16,tensor_parallel_size=2,gpu_memory_utilization=0.5,trust_remote_code=true,max_length=2048", "batch_size": "auto", "batch_sizes": [], "device": null, "use_cache": null, "limit": null, "bootstrap_iters": 100000, "gen_kwargs": null }, "git_hash": "f3c749c", "date": 1715673519.6710796, "pretty_env_info": "PyTorch version: 2.1.2+cu121\nIs debug build: False\nCUDA used to build PyTorch: 12.1\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.3 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.27.6\nLibc version: glibc-2.35\n\nPython version: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0] (64-bit runtime)\nPython platform: Linux-5.15.0-60-generic-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.2.140\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA RTX A6000\nGPU 1: NVIDIA RTX A6000\n\nNvidia driver version: 525.105.17\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.5\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.5\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.5\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.5\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.5\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.5\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.5\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 43 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 128\nOn-line CPU(s) list: 0-127\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7502 32-Core Processor\nCPU family: 23\nModel: 49\nThread(s) per core: 2\nCore(s) per socket: 32\nSocket(s): 2\nStepping: 0\nFrequency boost: enabled\nCPU max MHz: 2500.0000\nCPU min MHz: 1500.0000\nBogoMIPS: 5000.35\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip rdpid overflow_recov succor smca sme sev sev_es\nVirtualization: AMD-V\nL1d cache: 2 MiB (64 instances)\nL1i cache: 2 MiB (64 instances)\nL2 cache: 32 MiB (64 instances)\nL3 cache: 256 MiB (16 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-31,64-95\nNUMA node1 CPU(s): 32-63,96-127\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Retbleed: Mitigation; untrained return thunk; SMT enabled with STIBP protection\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\nVersions of relevant libraries:\n[pip3] mypy-extensions==1.0.0\n[pip3] numpy==1.22.2\n[pip3] pytorch-quantization==2.1.2\n[pip3] torch==2.1.2\n[pip3] torch-tensorrt==0.0.0\n[pip3] torchdata==0.7.0a0\n[pip3] torchtext==0.16.0a0\n[pip3] torchvision==0.16.0a0\n[pip3] triton==2.1.0+e621604\n[conda] Could not collect", "transformers_version": "4.40.0", "upper_git_hash": null }