8 lines
9.2 KiB
JSON
8 lines
9.2 KiB
JSON
{"instance_id": "locomo_conv-43_q155", "cell": "agentic", "provider": "zhipu", "model_id": "glm-5.1", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 14784, "prompt_tokens": 453, "completion_tokens": 556, "raw_text": "```json\n{\n \"verdict\": \"incorrect\",\n \"failure_mode\": \"F2\",\n \"rationale\": \"The model correctly identifies that John felt great but misses the core ground-truth fact that jogging without pain was a 'huge success'.\"\n}\n```", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model correctly identifies that John felt great but misses the core ground-truth fact that jogging without pain was a 'huge success'.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "incorrect"}
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{"instance_id": "locomo_conv-50_q015", "cell": "agentic", "provider": "zhipu", "model_id": "glm-5.1", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 26449, "prompt_tokens": 553, "completion_tokens": 1024, "raw_text": "The user wants to evaluate if the model's answer \"to relax and recharge\" is correct against the ground-truth answer \"because it relaxes and calms him\".\n\nStep 1: Determine if the model's answer is correct.\nGround truth: \"because it relaxes and calms him\"\nModel's answer: \"to relax and recharge\"\n\n\"relax\" matches \"relaxes\".\n\"recharge\" vs \"calms him\". Does \"recharge\" mean the same as \"calms him\"? Not exactly. \"Calms\" implies finding peace and tranquility, while \"recharge\" implies regaining energy. However, in the context of visiting parks to relax, \"recharge\" is a very common synonym/associated concept. Let's look at the context: \"so calming\", \"to relax - it's so peaceful being surrounded by nature\". \"Recharge\" is an extra detail that is a reasonable inference from relaxing and being peaceful, but is it factually incorrect? The instructions say \"Extra detail is acceptable ONLY if it is factually correct.\" Is \"recharge\" factually correct based on the context? It's a plausible inference, but the ground truth specifically says \"calms him\". Wait, \"recharge\" is not explicitly in the text, but is it an incorrect claim? It's a very minor difference. Let's consider if \"recharge\" is an acceptable alternative formulation for \"calms\". Often, people say \"relax and recharge\" as a set phrase. But \"calms\" is more about reducing stress, \"recharge\" is about gaining energy.\nLet's check the failure modes if incorrect:\nF4: relies on claims that do NOT appear in the ground-truth context (fabrication). \"recharge\" does not appear in the context.\nF2: correctly states SOME required facts but misses others, without stating incorrect facts. \"relax\" is stated, \"calms\" is missed. Is \"recharge\" an incorrect fact? It's an addition.\n\nWait, is \"recharge\" considered an incorrect claim? If I say \"I visit parks to relax and recharge\", it's not contradicted by the text, but it's an addition. The ground truth says \"relaxes and calms him\". The model says \"to relax and recharge\". It captures \"relax\" but replaces \"calms\" with \"recharge\".\nIs this a PARTIAL (F2) because it missed \"calms\" and added \"recharge\"? Or is it correct because \"recharge\" is an acceptable synonym/extra detail?\nActually, \"calm\" and \"recharge\" have slightly different meanings. Calm is about peace, recharge is about energy. The context says \"so calming\" and \"so peaceful\". Recharging is not explicitly mentioned.\nIf we consider \"recharge\" as an extra detail that is not factually supported (fabrication), it might be F4. But usually, \"relax and recharge\" is seen as a minor paraphrase. Let's be strict. The ground truth is \"because it relaxes and calms him\". The model missed \"calms\" and added \"recharge\".\nIf it's incorrect, it's F2 (PARTIAL) because it correctly states \"relax\" but misses \"calms\", and \"recharge\" is an extra detail. Wait, if \"recharge\" is an incorrect claim, it's not F2. F2 is \"without stating any incorrect facts\". Is \"recharge\" an incorrect fact? It's not in the text.\nLet's reconsider if it's \"correct\". \"Minor phrasing differences, synonyms, or alternative but equivalent formulations are acceptable.\" Is \"recharge\" an alternative but equivalent formulation for \"calms\"? Not really, calming is about reducing arousal, recharging is about restoring resources. However, in casual conversation, they are often grouped.\nLet's look at the instruction: \"Extra detail is acceptable ONLY if it is factually correct.\" Is it factually correct that he visits parks to recharge? The text doesn't say that. It says he visits to relax and because it's calming/peaceful. So \"recharge\" is an unsupported addition.\nIf it's an unsupported addition, it's a fabrication (F4) or just incorrect (F3). But it also misses \"calms\".\nActually, many evaluators would mark \"to relax and recharge\" as correct because it captures the gist. But strictly speaking, \"calms\" is replaced by \"recharge\".\nLet's evaluate if it's F2 (PARTIAL). It states \"relax\" (correct), misses \"calms\", and adds \"recharge\". If \"recharge\" is considered an incorrect fact, it fails the F2 condition (\"without stating any incorrect facts\").\nIs \"recharge\" an incorrect fact? It's an inference. A reasonable inference, but not explicitly stated.\nWhat if \"recharge\" is just a synonym for \"re-energize", "parsed_verdict": null, "parsed_failure_mode": null, "parsed_rationale": null, "opus_verdict_ref": "correct", "gpt_verdict_ref": "incorrect"}
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{"instance_id": "locomo_conv-43_q155", "cell": "full-context", "provider": "zhipu", "model_id": "glm-5.1", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 22581, "prompt_tokens": 453, "completion_tokens": 868, "raw_text": "```json\n{\n \"verdict\": \"incorrect\",\n \"failure_mode\": \"F2\",\n \"rationale\": \"The model correctly identifies that John felt 'great' but misses the core ground-truth fact that jogging without pain was a 'huge success'.\"\n}\n```", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model correctly identifies that John felt 'great' but misses the core ground-truth fact that jogging without pain was a 'huge success'.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "incorrect"}
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{"instance_id": "locomo_conv-50_q015", "cell": "full-context", "provider": "zhipu", "model_id": "glm-5.1", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 22543, "prompt_tokens": 551, "completion_tokens": 737, "raw_text": "```json\n{\n \"verdict\": \"incorrect\",\n \"failure_mode\": \"F2\",\n \"rationale\": \"The model's answer captures the 'relax' part but misses the 'calms him' part of the ground-truth answer.\"\n}\n```", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model's answer captures the 'relax' part but misses the 'calms him' part of the ground-truth answer.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "incorrect"}
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{"instance_id": "locomo_conv-43_q155", "cell": "oracle-context", "provider": "zhipu", "model_id": "glm-5.1", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 15651, "prompt_tokens": 453, "completion_tokens": 569, "raw_text": "```json\n{\n \"verdict\": \"incorrect\",\n \"failure_mode\": \"F2\",\n \"rationale\": \"The model captures the positive sentiment but misses the specific required fact from the ground truth that it was 'a huge success'.\"\n}\n```", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model captures the positive sentiment but misses the specific required fact from the ground truth that it was 'a huge success'.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "incorrect"}
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{"instance_id": "locomo_conv-47_q092", "cell": "oracle-context", "provider": "zhipu", "model_id": "glm-5.1", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 6011, "prompt_tokens": 457, "completion_tokens": 234, "raw_text": "```json\n{\n \"verdict\": \"incorrect\",\n \"failure_mode\": \"F2\",\n \"rationale\": \"The model correctly identifies that John created an application but misses the required details that it is a computer application on smartphones.\"\n}\n```", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model correctly identifies that John created an application but misses the required details that it is a computer application on smartphones.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "incorrect"}
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{"instance_id": "locomo_conv-50_q015", "cell": "oracle-context", "provider": "zhipu", "model_id": "glm-5.1", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 20114, "prompt_tokens": 551, "completion_tokens": 902, "raw_text": "```json\n{\n \"verdict\": \"incorrect\",\n \"failure_mode\": \"F2\",\n \"rationale\": \"The model captures the fact that it relaxes him, but misses the additional required fact from the ground truth that it also calms him.\"\n}\n```", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model captures the fact that it relaxes him, but misses the additional required fact from the ground truth that it also calms him.", "opus_verdict_ref": "correct", "gpt_verdict_ref": "incorrect"}
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