<![CDATA[Therapist-like response generation is increasingly discussed in digital mental health, yet most studies either focus on large pretrained systems or show illustrative outputs without a full lightweight benchmark. This paper asks whether small, non-pretrained language models can imitate therapist-style discourse in a reproducible setting. We used EmpatheticDialogues, an empathy-oriented dialogue corpus of roughly 25,000 conversations (Rashkin et al., 2019). Its widely used utterance-level split is 76,673 training records, 12,030 validation records, and 10,943 test records; the parsed conversation release used here contained 19,532/2,769/2,546 dialogues and yielded 40,252/5,736/5,257 listener-turn targets after we restricted supervision to supportive listener responses. We evaluated six lightweight systems on the full validation and test sets: an emotion template, TF-IDF retrieval, retrieval with micro-skill bias, an emotion-conditioned bigram language model, an emotion-conditioned trigram language model, and a trigram model with therapist-style biasing. All reported numbers are measured empirical results. The best overall system, Emotion-TrigramLM+Bias, achieved BLEU-4 of 0.0191 on validation and 0.0183 on test, ROUGE-L of 0.1652/0.1633, and therapist imitation score (TIS) of 0.6500/0.6487. Retrieval remained the most diverse model, reaching test Distinct-2 of 0.2551, but its therapist-style density was low at TIS = 0.2005. Adding therapist micro-skill bias improved retrieval by +0.0042 BLEU-4 and +0.3603 TIS on the test set, and improved the trigram model by +0.0059 BLEU-4 and +0.3056 TIS. Performance was strongest on negative-emotion turns, where acknowledgments and follow-up questions aligned closely with the references. The findings show that very small models can imitate the surface form of therapeutic language surprisingly well, but they do so mainly by compressing support into generic scripts. Lightweight therapist imitation is therefore feasible for low-risk acknowledgment support, but it is not a replacement for licensed mental health care.]]>
