The paper proposes CoPE, a chain linking dialogue context, momentary personality states, and long-term traits, and turns it into two tasks. EPR-S predicts a Big Five label for each dialogue and the supporting utterances; EPR-T infers a character's trait from multiple dialogues and identifies supporting dialogues. The “explanation” is a supervised textual rationale, summaries and BFI-2-derived descriptors plus evidence identifiers, not a causal model explanation or a direct measurement of a psychological construct.
PersonalityEvd is built from CPED, a Chinese television-series dialogue corpus. After excluding short dialogues, the authors select 72 characters and up to 30 dialogues per character. The final dataset contains 1,924 dialogues and 32,673 utterances. GPT-4 Turbo pre-annotates each dimension; twelve psychology undergraduates correct states, two graduate students inspect quality, and the authors perform a final review. About 30% of samples are re-annotated. Six other undergraduates produce trait annotations: three independently annotate each character, reach consensus, may alter state labels, and convert the mean of twelve BFI-2 items into labels through a median split. Dialogues with all five dimensions uncertain or with contradictory information are removed.
For EPR-S, Qwen1.5-7B-Chat has the highest mean Big Five accuracy at 66.45%, versus 64.62% for ChatGLM3-6B-32K and 62.09% for zero-shot GPT-4 Turbo. Evidence-ID F1 scores are 75.94, 75.42, and 71.20. For three-fold EPR-T, ChatGLM reaches 77.78% accuracy and 40.28 evidence-ID F1, while Qwen reaches 76.59% and 44.39. These accuracies must be read against the imbalance in the released data: among 72 characters, high labels occur 62 times for Openness, 62 for Conscientiousness, 70 for Extraversion, 49 for Agreeableness, and 45 for Neuroticism. Always selecting each dimension's majority class would average about 80%, above both EPR-T results. Raw accuracy therefore does not demonstrate effective trait recognition.
Evaluation combines BERTScore, Claude 3 Sonnet and GPT-4 Turbo ratings, plus a human evaluation of 50 samples from ten characters with five evaluators per sample. References score 4.61/5 for fluency, 4.38 for coherence, and 4.31 for plausibility; ChatGLM scores 3.82/2.51/2.59 and Qwen 3.90/2.68/2.65. No inter-rater agreement, uncertainty, or sampling strategy is reported, so the figures support high perceived quality in a small sample but do not “guarantee” the entire corpus.
The ablations qualify the claim that generating evidence improves recognition. For states, direct fine-tuning reaches 64.84% and CoT 64.62%; hybrid settings reach 66.94% and 66.55%. For traits, CoT improves from 75.83% to 77.78%, but hybrid settings remain at 75.53% and 75.55%. Adding predicted states raises trait accuracy only from 77.78% to 77.99%; reference states raise it to 83.52%, but these annotations are unavailable in real use and tied to the same process that produced trait labels.
The repository allows inspection of the characters, dialogues, splits, labels, and part of the code. It lacks the announced English translation, complete outputs, checkpoints, an end-to-end judge pipeline, a code/derivative-data license, and releases. Many scripts retain local paths or xxx placeholders; Qwen dependencies are unpinned, and seed variability is absent. The defensible contribution is a novel evidence-annotated benchmark over fictional Chinese characters and initial baselines. It does not validate human personality, psychological stability, causal explanations, cross-cultural generalization, or a system ready to profile real users.