analysis/error_analysis.md

Error Analysis

All numbers below are computed by src/summarize_human_review.py from the human review in analysis/human_review.csv and are stored in analysis/summary_metrics.json / analysis/summary_metrics.csv. Case IDs refer to data/evaluation.csv and results/evaluation_raw_outputs.jsonl.

1. Evaluation Overview

• Model: Qwen/Qwen3.5-4B (float16, no quantization), run 20261002T132459Z.
• Dataset: 100 synthetic clinical reasoning cases: 50 Arabic (Modern Standard Arabic) and 50 English. Every Arabic case has one semantically equivalent English case (scenario_id).
• Minimal pairs: 25 pairs (A/B), each present in both languages. Within a pair, one decisive piece of evidence differs and flips the correct answer.
• Categories (20 cases each): temporal_reasoning, contradiction_detection, missing_evidence, false_premise, causal_reasoning. The four binary categories are balanced at 5 Yes / 5 No answers per language.
• Generation: thinking disabled, greedy decoding (do_sample=False, num_beams=1), max_new_tokens=512, seed 42, batch size 1, Kaggle with 2 × Tesla T4.

2. Evaluation Method

• The prompt contained only case_text and question inside a fixed template. It asked for FINAL_ANSWER: and REASON: in the language of the case.
• expected_answer, expected_reason, evidence_flip and all identifiers were hidden from the model. The notebook asserts that none of them appear in any rendered prompt.
• Raw responses are stored unmodified in results/evaluation_raw_outputs.jsonl.
• Per-case outputs were reviewed using a structured rubric (analysis/review_guide.md), and aggregate metrics were computed from these labels. Each row records:
  • human_final_correct: whether FINAL_ANSWER matches the reference. For false-premise items, an explicit rejection of an unsupported premise counts as correct; exact wording is not required.
  • human_reason_supported: whether REASON uses the decisive evidence in the case and is consistent with the final answer. A response whose explanation is correct but contradicts its own final answer is marked no.
  • human_unsupported_assumption: whether the response relies on information that is not in the case.
  • human_error_type and human_notes: a short error label and an explanation.
• If a response contained more than one FINAL_ANSWER line, the first one was graded (3 cases; see §8).
• No LLM judge was used. The scripts only parse and aggregate. binary_auto_match in the review sheet is a mechanical string comparison that helped the reviewer; it is not a grade.

3. Quantitative Results

Overall final-answer accuracy

	n	Correct	Accuracy
Arabic	50	37	74%
English	50	44	88%
Overall	100	81	81%

Accuracy by category and language

Category	Arabic	English	Both languages
temporal_reasoning	6/10	8/10	14/20 (70%)
contradiction_detection	8/10	9/10	17/20 (85%)
missing_evidence	7/10	8/10	15/20 (75%)
false_premise	9/10	10/10	19/20 (95%)
causal_reasoning	7/10	9/10	16/20 (80%)

English accuracy is equal to or higher than Arabic accuracy in all five categories.

Reasoning quality and unsupported assumptions

	Reason supported	Final correct and reason supported	Unsupported assumption
Arabic	36/50 (72%)	36/50 (72%)	3/50 (6%)
English	44/50 (88%)	44/50 (88%)	2/50 (4%)
Overall	80/100 (80%)	80/100 (80%)	5/100 (5%)

Reason supported, by category: temporal 13/20, contradiction 17/20, missing evidence 15/20, false premise 19/20, causal 16/20.

Every response with a supported reason also had a correct final answer. The single correct answer without a supported reason is AR-TMP-05A, where the right label came with a misordered timeline. Eight further responses gave an explanation that pointed to the correct evidence but then a wrong final label. Under the rubric these count as neither correct nor reason-supported, and they are tracked separately as answer–reason mismatches (§5).

Yes/No polarity (binary categories, 40 items per language)

	Expected Yes: correct	Expected No: correct	Model answered No
Arabic	8/20 (40%)	20/20 (100%)	32/40 (80%)
English	16/20 (80%)	18/20 (90%)	22/40 (55%)

Minimal-pair consistency (both A and B correct)

Category	Arabic	English
temporal_reasoning	1/5	3/5
contradiction_detection	3/5	4/5
missing_evidence	2/5	3/5
false_premise	4/5	5/5
causal_reasoning	2/5	4/5
Overall	12/25 (48%)	19/25 (76%)

Cross-language matched scenarios (50 scenarios)

Outcome	Count
Both correct	35
Arabic only correct	2
English only correct	9
Both incorrect	4

Exact McNemar test on the 11 discordant scenarios (2 vs 9): two-sided p = 0.065.

4. Main Error Patterns

Error labels from the human review (20 rows carry a label other than none; a row may have several labels):

Label	Arabic	English	Total
answer_reason_mismatch	6	2	8
format_self_correction	2	1	3
temporal_order_error	1	1	2
unsupported_reconciliation	1	1	2
overconstraint_extra_item	1	1	2
evidence_extraction_error	1	0	1
false_premise_acceptance	1	0	1
reasoning_contradiction	1	0	1
unsupported_medical_assumption	1	0	1
unsupported_requirement	1	0	1
truncated_output	1	0	1
causal_temporal_error	0	1	1
unsupported_assumption	0	1	1

Arabic–English gap. The model was correct on 37/50 Arabic and 44/50 English cases, and English was at least as accurate in every category. Among the 11 matched scenarios where the two languages disagree, English was correct in 9. With 50 scenarios this is a descriptive difference: the exact McNemar test does not reach p < 0.05 (p = 0.065).

Yes/No polarity. This is the clearest pattern in the data. Every Arabic error on a binary item (12/12) was an expected نعم (Yes) answered لا (No). Arabic accuracy was 20/20 when the expected answer was No and 8/20 when it was Yes. The model answered لا on 32 of 40 Arabic binary items, against 22 of 40 English binary items answered No. Question polarity was counterbalanced (e.g. "contradict?" vs "consistent?"), so a "No" here does not always mean the same clinical judgment. The skew therefore points to a bias toward the negative label in Arabic, not a bias toward one clinical conclusion. In English, the errors were split (4 expected-Yes, 2 expected-No).

Answer–reason mismatch. This was the most frequent error label (8 rows: 6 Arabic, 2 English). In all 8, the expected answer was Yes and the model answered No. It is discussed in §5.

Temporal-order mistakes. These were infrequent. Two final answers were wrong because dates were put in the wrong order: AR-TMP-01B (correct dates, reversed conclusion) and EN-TMP-05A (treats 9 June as earlier than 7 June). AR-TMP-05A reached the correct label with an explanation that made the same 7/9 June ordering error. Most of the Arabic temporal errors were answer–reason mismatches (AR-TMP-02A, -03A, -05B), not misread timelines.

Unsupported reconciliation of contradictions. In scenario CON-01A, the model failed in both languages. It explained the conflict between "no regular medications" and "takes metformin" with record timing or scope that the case does not state. The Arabic response later corrected itself (§8).

Over-constraining missing-evidence questions. In scenario MIS-05A, the model failed in both languages. It treated an extra, non-required field (ward) as a reason the label did not meet the rule. AR-MIS-04A added requirements that were not in the rule (patient name/history). AR-MIS-01A claimed the weight was missing although the case states it.

False-premise failures. These were rare (1/20). AR-FPR-05A treated a discussed ICU transfer as an actual transfer and returned a date. The English counterpart rejected the premise correctly. All other false-premise items were handled correctly in both languages, including the B variants, where the premise holds and an entity must be returned.

Causal reasoning mistakes. These were few and varied. AR-CAU-02A relied on outside medical knowledge ("seven days is usually too short"), although the prompt restricted the model to the case. EN-CAU-02B accepted a causal link even though the medication started after the swelling. AR-CAU-04B and AR-CAU-05A are answer–reason mismatches.

Unsupported assumptions were uncommon overall (5/100: 3 Arabic, 2 English).

5. Answer–Reason Mismatch

In 8 responses (AR-TMP-02A, AR-TMP-03A, AR-TMP-05B, AR-CON-05B, AR-CAU-04B, AR-CAU-05A, EN-TMP-03A, EN-MIS-04A), the REASON stated the decisive evidence correctly, but FINAL_ANSWER gave the opposite label. Representative cases:

• AR-TMP-02A: the reason states that the blood sample (09:15) was drawn before the first antibiotic dose (10:00), but the final answer is لا.
• AR-CON-05B: the reason totals the transfusions to 3 units and says the two records match, but the final answer to "are they consistent?" is لا.
• AR-CAU-04B: the reason reports pain on 6 of 7 milk days against 1 of 7 non-milk days and calls milk the likely cause, but the final answer is لا.
• EN-TMP-03A: the reason says the low reading (14:00) came after the infusion started (12:00), but the final answer is No.

All 8 mismatches have an expected answer of Yes and a final answer of No, and 6 of the 8 are Arabic. In this sample, many errors scored as wrong final answers reflect a failure to map correct reasoning onto the output label, not a misreading of the case. Because the rubric requires the reason to be consistent with the final answer, these 8 rows are scored human_reason_supported = no. They carry the answer_reason_mismatch label, so the error can be distinguished from a genuine misreading of the evidence.

6. Minimal-Pair Robustness

A pair is consistent only when both A and B are answered correctly, i.e. when the model updates its conclusion after the single evidence flip.

• English: 19/25 pairs (76%) were consistent.
• Arabic: 12/25 pairs (48%) were consistent. The weakest category was temporal reasoning (1/5).

Among the 20 binary pairs, the model gave the same label to both variants in 12 Arabic pairs and 6 English pairs. Such a pair cannot be fully correct, because the evidence flip reverses the expected label. In all 12 Arabic cases the shared label was لا. So in Arabic, most pair failures happened because the model did not move from No to Yes when the evidence required it. Random errors on either side of the flip were not the main cause. False-premise pairs were the most robust in both languages (Arabic 4/5, English 5/5).

7. Cross-Language Consistency

The final-answer outcome was the same in both languages for 39 of 50 scenarios (35 both correct, 4 both incorrect).

• English only correct (9): TMP-01B, TMP-02A, TMP-05B, CON-05B, MIS-01A, FPR-05A, CAU-02A, CAU-04B, CAU-05A. Eight of these have an expected answer of Yes. The only exception is FPR-05A, a false-premise item.
• Arabic only correct (2): TMP-05A and CAU-02B. In both, the English response made a temporal error (treating a later date as earlier, or accepting causation when the drug started after the symptom). AR-TMP-05A reached the correct label with a flawed explanation.
• Both incorrect (4): CON-01A (unsupported reconciliation), MIS-04A (extra requirements / self-correction), MIS-05A (over-constraint on an extra field) and TMP-03A (answer–reason mismatch in both languages). These look like weaknesses in the item type itself, not language effects.

The same reasoning problem therefore tends to fail in Arabic in one direction: when the correct answer is affirmative.

8. Truncation / Generation Anomalies

• Truncation: one response hit max_new_tokens = 512 without an end-of-sequence token: AR-MIS-04A (512 tokens; the next longest response was 365 tokens). It opens with FINAL_ANSWER: لا and then argues back and forth about whether the answer should be Yes, until generation stops. The reviewer graded the visible first answer as incorrect, with labels unsupported_requirement;truncated_output. The error was recorded because the stated answer is wrong, not because the output was truncated.
• Multiple FINAL_ANSWER lines: AR-CON-01A, AR-CAU-05A and EN-MIS-04A each give an initial answer, reason at length, and then output a second FINAL_ANSWER that reverses the first. Following the grading rule in §2, the first answer was graded, so all three are counted as incorrect. In each case the second answer matches the reference label. Under a "last answer" rule, accuracy would be 39/50 for Arabic and 45/50 for English. This is a sensitivity note, not a separate grading.
• Non-standard answer text: EN-FPR-01A answered FINAL_ANSWER: None. The explanation shows that this means "no medication was stopped", and it was graded as a correct premise rejection.

9. Limitations

• All cases are synthetic, short and controlled. They are simpler than real clinical records.
• Only one model, Qwen/Qwen3.5-4B, was evaluated, with thinking disabled.
• One prompt template and one deterministic decoding setup were used. The results may change with other prompts, answer formats or sampling.
• The evaluation is small: 100 cases, 25 pairs per language and 10 cases per language × category cell. All differences are descriptive, and per-cell percentages move 10 points with a single case.
• The Arabic items are Modern Standard Arabic only, with no dialects or other registers.
• The labels come from one review pass with no second reviewer. Judgments about reasoning support and unsupported assumptions involve some subjectivity, and inter-annotator agreement was not measured.
• The items have not been independently reviewed by clinicians or professional Arabic linguists.
• These results make no claim about real-world clinical safety or medical competence.

10. Path Forward

• Expand to a larger multilingual benchmark with more pairs per category, so that language and category effects can be tested with adequate statistical power.
• Evaluate more models and sizes, including thinking-enabled modes, to see whether the Arabic negative-label bias is specific to this model.
• Add Arabic dialects and other registers, and have clinicians and native speakers review the items.
• Use structured answer formats, e.g. a constrained label field or a JSON schema, to separate label-mapping errors from reasoning errors.
• Add automatic answer–reason consistency checks that flag responses whose explanation contradicts the label, for human follow-up.
• Study calibration and abstention: let the model mark items as undecidable, and measure confidence on flipped pairs.
• Strengthen minimal-pair testing with multiple flips per scenario, paraphrased questions, and swapped question polarity on the same evidence, to isolate label bias from evidence sensitivity.
• Use two independent reviewers and report inter-annotator agreement.