21 lines
22 KiB
JSON
21 lines
22 KiB
JSON
{"instance_id": "locomo_conv-50_q086", "cell": "no-context", "provider": "deepseek", "model_id": "deepseek-v4-pro", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 10725, "prompt_tokens": 484, "completion_tokens": 360, "raw_text": "{\n \"verdict\": \"incorrect\",\n \"failure_mode\": \"F4\",\n \"rationale\": \"The model answered 'Surfing', which is a fabricated activity not present in the context and contradicts the correct answer 'Skiing'.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F4", "parsed_rationale": "The model answered 'Surfing', which is a fabricated activity not present in the context and contradicts the correct answer 'Skiing'."}
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{"instance_id": "locomo_conv-26_q059", "cell": "no-context", "provider": "deepseek", "model_id": "deepseek-v4-pro", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 13726, "prompt_tokens": 572, "completion_tokens": 487, "raw_text": "{\n \"verdict\": \"incorrect\",\n \"failure_mode\": \"F1\",\n \"rationale\": \"The model refused to answer by stating it cannot be determined without context, effectively abstaining despite the ground truth providing a definitive answer.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F1", "parsed_rationale": "The model refused to answer by stating it cannot be determined without context, effectively abstaining despite the ground truth providing a definitive answer."}
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{"instance_id": "locomo_conv-44_q000", "cell": "no-context", "provider": "deepseek", "model_id": "deepseek-v4-pro", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 8914, "prompt_tokens": 446, "completion_tokens": 281, "raw_text": "{\n \"verdict\": \"incorrect\",\n \"failure_mode\": \"F4\",\n \"rationale\": \"The model gives the year 1954, which has no basis in the provided context; the correct year derived from the statement 'I've had them for 3 years!' on 27 March 2023 is 2020.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F4", "parsed_rationale": "The model gives the year 1954, which has no basis in the provided context; the correct year derived from the statement 'I've had them for 3 years!' on 27 March 2023 is 2020."}
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{"instance_id": "locomo_conv-41_q080", "cell": "no-context", "provider": "deepseek", "model_id": "deepseek-v4-pro", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 20988, "prompt_tokens": 496, "completion_tokens": 745, "raw_text": "{\n \"verdict\": \"incorrect\",\n \"failure_mode\": \"F4\",\n \"rationale\": \"The model answers 'A wind chime', which does not appear in the ground-truth context and contradicts the correct answer of a painting of a castle on a hill; this is a fabricated detail.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F4", "parsed_rationale": "The model answers 'A wind chime', which does not appear in the ground-truth context and contradicts the correct answer of a painting of a castle on a hill; this is a fabricated detail."}
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{"instance_id": "locomo_conv-50_q086", "cell": "oracle-context", "provider": "deepseek", "model_id": "deepseek-v4-pro", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 5959, "prompt_tokens": 484, "completion_tokens": 151, "raw_text": "{\n \"verdict\": \"correct\",\n \"failure_mode\": null,\n \"rationale\": \"The model's answer 'Skiing' exactly matches the ground-truth answer derived from the context indicating Calvin plans to go skiing after the tour.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'Skiing' exactly matches the ground-truth answer derived from the context indicating Calvin plans to go skiing after the tour."}
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{"instance_id": "locomo_conv-26_q059", "cell": "oracle-context", "provider": "deepseek", "model_id": "deepseek-v4-pro", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 16766, "prompt_tokens": 568, "completion_tokens": 564, "raw_text": "{\n \"verdict\": \"incorrect\",\n \"failure_mode\": \"F2\",\n \"rationale\": \"The model's answer 'Yes' captures that Caroline is religious to some extent but omits the necessary qualifier that she is only 'somewhat, but not extremely religious', making it incomplete.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model's answer 'Yes' captures that Caroline is religious to some extent but omits the necessary qualifier that she is only 'somewhat, but not extremely religious', making it incomplete."}
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{"instance_id": "locomo_conv-44_q000", "cell": "oracle-context", "provider": "deepseek", "model_id": "deepseek-v4-pro", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 4835, "prompt_tokens": 446, "completion_tokens": 139, "raw_text": "{\n \"verdict\": \"correct\",\n \"failure_mode\": null,\n \"rationale\": \"The model's answer matches the ground truth year derived from the context (2023 minus 3 years = 2020).\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer matches the ground truth year derived from the context (2023 minus 3 years = 2020)."}
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{"instance_id": "locomo_conv-41_q080", "cell": "oracle-context", "provider": "deepseek", "model_id": "deepseek-v4-pro", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 15824, "prompt_tokens": 494, "completion_tokens": 719, "raw_text": "{\n \"verdict\": \"incorrect\",\n \"failure_mode\": \"F2\",\n \"rationale\": \"The model answered only 'A painting,' missing the necessary detail that it was a painting of a castle on a hill, which is required to fully specify what she made as a reminder of the England trip.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model answered only 'A painting,' missing the necessary detail that it was a painting of a castle on a hill, which is required to fully specify what she made as a reminder of the England trip."}
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{"instance_id": "locomo_conv-50_q086", "cell": "full-context", "provider": "deepseek", "model_id": "deepseek-v4-pro", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 7692, "prompt_tokens": 483, "completion_tokens": 219, "raw_text": "{\n \"verdict\": \"correct\",\n \"failure_mode\": null,\n \"rationale\": \"The model's answer 'skiing' matches the ground truth activity 'Skiing' from the context, with no additional incorrect claims.\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer 'skiing' matches the ground truth activity 'Skiing' from the context, with no additional incorrect claims."}
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{"instance_id": "locomo_conv-26_q059", "cell": "full-context", "provider": "deepseek", "model_id": "deepseek-v4-pro", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 6206, "prompt_tokens": 568, "completion_tokens": 184, "raw_text": "{\n \"verdict\": \"incorrect\",\n \"failure_mode\": \"F1\",\n \"rationale\": \"The model answered 'unknown', which is an explicit refusal to answer, fitting the F1 (ABSTAIN) failure mode.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F1", "parsed_rationale": "The model answered 'unknown', which is an explicit refusal to answer, fitting the F1 (ABSTAIN) failure mode."}
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{"instance_id": "locomo_conv-44_q000", "cell": "full-context", "provider": "deepseek", "model_id": "deepseek-v4-pro", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 4956, "prompt_tokens": 446, "completion_tokens": 145, "raw_text": "{\n \"verdict\": \"correct\",\n \"failure_mode\": null,\n \"rationale\": \"The model's answer '2020' matches the ground-truth year derived from the context (2023 minus 3 years).\"\n}", "parsed_verdict": "correct", "parsed_failure_mode": null, "parsed_rationale": "The model's answer '2020' matches the ground-truth year derived from the context (2023 minus 3 years)."}
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{"instance_id": "locomo_conv-41_q080", "cell": "full-context", "provider": "deepseek", "model_id": "deepseek-v4-pro", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 15491, "prompt_tokens": 494, "completion_tokens": 526, "raw_text": "{\n \"verdict\": \"incorrect\",\n \"failure_mode\": \"F2\",\n \"rationale\": \"The model correctly states she made a painting but omits the required detail that it was of a castle on a hill.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model correctly states she made a painting but omits the required detail that it was of a castle on a hill."}
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{"instance_id": "locomo_conv-50_q086", "cell": "retrieval", "provider": "deepseek", "model_id": "deepseek-v4-pro", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 28851, "prompt_tokens": 483, "completion_tokens": 1024, "raw_text": "We are given a question: \"What sports activity is Calvin planning to try after the tour with Frank Ocean?\" The ground-truth answer is \"Skiing\". The ground-truth supporting context is a dialogue from Session 9: Calvin says: \"Nope, not yet! I haven't been there before, but it's on my to-do list for after my tour with Frank Ocean ends. Can't wait to see them in person! Look at that snowy peak. [image: a photo of a person on skis on a snowy mountain]\" This implies the activity is skiing, as indicated by the image and the context of a snowy peak. So the correct answer is \"Skiing\". The model's answer is \"Not mentioned\". That is incorrect. The model says \"Not mentioned\" which means it claims the answer is not mentioned in the context. But the context does mention skiing implicitly. So the model failed. Which failure mode? The model does not explicitly refuse or say it doesn't know? Actually \"Not mentioned\" could be considered a refusal or claiming that it's not in the text. The decision tree: F1 (ABSTAIN) is \"Does the model explicitly refuse or say it does not know?\" The model says \"Not mentioned\". That is not exactly saying \"I don't know\", it's saying the information isn't mentioned. But in many cases, \"not mentioned\" is a way of abstaining because it indicates the model cannot answer. But the instructions: \"explicitly refuse or say it does not know\" - \"Not mentioned\" is somewhat saying the answer is not provided, so it's like saying I can't answer based on the text. However, the model might be evaluated on whether it answered correctly. The question asks \"What sports activity...\" and the model says \"Not mentioned\" which is a claim that the text doesn't mention it. That is an incorrect answer about the content of the context. So it's not abstaining in the sense of \"I don't know\" but rather stating that the text doesn't mention it, which is a factual error. So it's not F1. F5: OFF-TOPIC - does the model answer a different question? It answers the question by saying it's not mentioned. That's an answer to the question, albeit wrong. So not off-topic. F4: HALLUCINATED - relies on entities, names, dates, or claims that do NOT appear in the ground-truth context. The model says \"Not mentioned\". This is a claim that the information is not present. That claim is a fabrication because the context clearly implies skiing. The model is not fabricating entities, but making a false claim about the text. This might fall under F3 (INCORRECT) or F4. Let's check: \"Does the model correctly state SOME required facts but miss others, without stating any incorrect facts?\" It does not state any required facts; it states no correct facts, it just says \"Not mentioned\". So it's not partial (F2). It states a fact derived from the context but gets them wrong? It says \"Not mentioned\" which is a statement about the context. That is an incorrect fact. So F3: Otherwise (model states facts derived from the context but gets them wrong): F3. However, F4 says \"Does the model rely on entities, names, dates, or claims that do NOT appear in the ground-truth context (fabrication)?\" The claim \"Not mentioned\" is a claim that does not appear in the context; it's a fabricated statement about the context. But the decision tree sequence: 1. F1? No. 2. F5? No, it's answering the question directly. 3. F4? Does the model rely on entities, names, dates, or claims that do NOT appear in the ground-truth context? The model's answer is \"Not mentioned\". The claim that the activity is not mentioned is not present in the context; it's a fabricated assertion. So I think this falls under F4: Hallucinated, because it invents a claim that the information is missing. But many would argue that \"not mentioned\" is simply an incorrect answer, not necessarily a hallucination. However, the decision tree specifies F4 as \"relies on entities, names, dates, or claims that do NOT appear in the ground-truth context\". The claim \"not mentioned\" is a claim that is not supported by the context. In fact, the context does mention it indirectly. So the model is making a false claim. That seems like F4. I'll consider typical usage: If a model says \"not mentioned\" when it is mentioned, it's often considered a hallucination of omission - it's claiming something that isn't true about the text. So I'd go with F4. But let's double-check: could it be F3? \"Otherwise (model states facts derived from the context but gets", "parsed_verdict": null, "parsed_failure_mode": null, "parsed_rationale": null}
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{"instance_id": "locomo_conv-26_q059", "cell": "retrieval", "provider": "deepseek", "model_id": "deepseek-v4-pro", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 12082, "prompt_tokens": 568, "completion_tokens": 385, "raw_text": "{\n \"verdict\": \"incorrect\",\n \"failure_mode\": \"F3\",\n \"rationale\": \"The model answered 'No' but the ground truth states she is 'Somewhat, but not extremely religious,' making the answer factually incorrect.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F3", "parsed_rationale": "The model answered 'No' but the ground truth states she is 'Somewhat, but not extremely religious,' making the answer factually incorrect."}
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{"instance_id": "locomo_conv-44_q000", "cell": "retrieval", "provider": "deepseek", "model_id": "deepseek-v4-pro", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 21631, "prompt_tokens": 446, "completion_tokens": 767, "raw_text": "{\n \"verdict\": \"incorrect\",\n \"failure_mode\": \"F3\",\n \"rationale\": \"The model incorrectly stated that the year is not mentioned, while the context clearly indicates adoption in 2020 (three years before March 2023).\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F3", "parsed_rationale": "The model incorrectly stated that the year is not mentioned, while the context clearly indicates adoption in 2020 (three years before March 2023)."}
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{"instance_id": "locomo_conv-41_q080", "cell": "retrieval", "provider": "deepseek", "model_id": "deepseek-v4-pro", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 9374, "prompt_tokens": 494, "completion_tokens": 265, "raw_text": "{\n \"verdict\": \"incorrect\",\n \"failure_mode\": \"F2\",\n \"rationale\": \"The model identified that Maria made a painting, but omitted the required specific subject matter (a castle on a hill), making the answer incomplete.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model identified that Maria made a painting, but omitted the required specific subject matter (a castle on a hill), making the answer incomplete."}
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{"instance_id": "locomo_conv-50_q086", "cell": "agentic", "provider": "deepseek", "model_id": "deepseek-v4-pro", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 9916, "prompt_tokens": 493, "completion_tokens": 419, "raw_text": "{\n \"verdict\": \"incorrect\",\n \"failure_mode\": \"F1\",\n \"rationale\": \"The model did not answer the question; it simply reported that it reached the maximum number of tool turns, effectively abstaining from providing the requested sports activity.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F1", "parsed_rationale": "The model did not answer the question; it simply reported that it reached the maximum number of tool turns, effectively abstaining from providing the requested sports activity."}
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{"instance_id": "locomo_conv-26_q059", "cell": "agentic", "provider": "deepseek", "model_id": "deepseek-v4-pro", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 17581, "prompt_tokens": 601, "completion_tokens": 596, "raw_text": "{\n \"verdict\": \"incorrect\",\n \"failure_mode\": \"F4\",\n \"rationale\": \"The model references a necklace representing 'love, faith and strength' that does not appear in the provided ground-truth context, constituting a hallucination.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F4", "parsed_rationale": "The model references a necklace representing 'love, faith and strength' that does not appear in the provided ground-truth context, constituting a hallucination."}
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{"instance_id": "locomo_conv-44_q000", "cell": "agentic", "provider": "deepseek", "model_id": "deepseek-v4-pro", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 28694, "prompt_tokens": 456, "completion_tokens": 1024, "raw_text": "We are asked: \"Which year did Audrey adopt the first three of her dogs?\" Ground truth: 2020. Ground-truth supporting context: Session 1 (1:10 pm on 27 March, 2023): Audrey: \"I've had them for 3 years! Their names are Pepper, Precious and Panda. I can't live without my little ones!\" So the conversation is on 27 March 2023, she says she's had them for 3 years. So adoption year would be 2023 - 3 = 2020.\n\nModel's answer: \"Max tool turns reached (3 turns, 3 tools used).\" That's not an answer to the question. It appears the model failed to provide an answer because it hit a tool turn limit. So the model did not answer the question. It gave a system message or error. We need to evaluate: Is this correct? The model's answer is essentially no answer. It is not providing any information. So it does not contain the required facts. So verdict is incorrect. Then failure mode: Check decision tree. Does the model explicitly refuse or say it does not know? The model's output: \"Max tool turns reached (3 turns, 3 tools used).\" This is not explicitly refusing or saying \"I don't know\". It's a technical limit message. It's likely an error from the system, not a response to the question. So not F1. Does the model answer a different question? No, it doesn't answer any question. It just states a tool limit. So F5? F5 is \"off-topic\" meaning the model answers a different question than was asked. But here it's not answering a different question; it's giving a status message. That might be considered off-topic? Or maybe it's F1? The decision tree: step 1: \"Does the model explicitly refuse or say it does not know?\" The message \"Max tool turns reached\" could be interpreted as the model cannot complete the task, thus implicitly it does not know/cannot answer, but it's not an explicit refusal. However, typical evaluation tasks treat max tool turns as an inability to answer, which might be considered an abstention? But the phrasing is not \"I don't know\" or refusal. But it indicates it cannot provide an answer. I'd check: If the model says \"Max tool turns reached\" it might be a system-generated message when the assistant ran out of tool calls. This is essentially a failure to respond. In some rubrics, that's an F1 (ABSTAIN) if the model explicitly states it cannot answer or reaches a limit. But the instruction says \"explicitly refuse or say it does not know\". \"Max tool turns reached\" is not that. It might be considered a different output, not an answer. I think it's F5? Wait, F5: \"Does the model answer a DIFFERENT question than was asked (coherent but off-topic)?\" The model's output is \"Max tool turns reached (3 turns, 3 tools used).\" That's not an answer to any question; it's a status. It's not coherent as an answer. Could be considered off-topic? The definition of F5 is for when the model gives an answer, but to the wrong question. Here, no answer at all. Perhaps we need to see if it's F1 or something else. Alternatively, there might be a nuance: The model's answer is \"Max tool turns reached (3 turns, 3 tools used).\" That is a meta-response. It's not answering the user's question. I'd classify this as F1? The model is essentially unable to answer due to tool limit. It's akin to \"I couldn't retrieve the information.\" I'd lean towards F1 because it's a statement indicating it couldn't complete the task, which is a form of abstention/refusal. But does it explicitly say it doesn't know? Not exactly. However, in many evaluation frameworks, hitting a tool limit is considered a refusal to answer or an error. The decision tree says: 1. Does the model explicitly refuse or say it does not know? If yes, F1. \"Max tool turns reached\" doesn't explicitly refuse, but it's a technical limitation message. It might be better to treat it as F5 because it's off-topic (it's not answering the question, but talking about tool turns). But off-topic typically means the model gave an answer that is coherent but for a different question. This is more like a system error. I think this should be F1 (ABSTAIN). I'll check typical LLM evaluation: If the model outputs a message like \"I'm sorry, I can't answer that\" that's F1. If it outputs something like \"The sky is blue\" that's off-topic F5. Here it's \"Max tool turns reached\" –", "parsed_verdict": null, "parsed_failure_mode": null, "parsed_rationale": null}
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{"instance_id": "locomo_conv-41_q080", "cell": "agentic", "provider": "deepseek", "model_id": "deepseek-v4-pro", "routing": "direct", "http_status": 200, "error": null, "retries": 0, "latency_ms": 9279, "prompt_tokens": 494, "completion_tokens": 293, "raw_text": "{\n \"verdict\": \"incorrect\",\n \"failure_mode\": \"F2\",\n \"rationale\": \"The model answered 'a painting', which captures the general object but omits the required details that it was a painting of a castle on a hill.\"\n}", "parsed_verdict": "incorrect", "parsed_failure_mode": "F2", "parsed_rationale": "The model answered 'a painting', which captures the general object but omits the required details that it was a painting of a castle on a hill."}
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