Does “Reasoning” with Large Language Models Improve Recognizing, Generating and Reframing Unhelpful Thoughts? (2025)

Abstract: Cognitive Reframing, a core element of Cognitive Behavioral Therapy (CBT), helps individuals reinterpret negative experiences by finding positive meaning. Recent advances in Large Language Models (LLMs) have demonstrated improved performance through reasoning-based strategies. This inspires a promising direction of leveraging the reasoning capabilities of LLMs to improve CBT and mental reframing by simulating the process of critical thinking, potentially enabling more effective recognition, generation and reframing of cognitive distortions. In this work, we investigate the role of various reasoning methods, including pre-trained reasoning LLMs, such as DeepSeek-R1, and augmented reasoning strategies, such as CoT (Wei et al., 2022) and self-consistency (Wang et al., 2022), in enhancing LLMs’ ability to perform cognitive reframing tasks. We find that augmented reasoning methods, even when applied to older LLMs like GPT-3.5, consistently outperform state-of- the-art pretrained reasoning models such as DeepSeek-R1 (Guo et al., 2025) and o1 (Jaech et al., 2024) on recognizing, generating and reframing unhelpful thoughts.

## introduction cognitive behavioral therapy (cbt) @xcite is one of the most widely used and well-supported approaches in psychotherapy @xcite . cbt focuses on both the process and content of thoughts, including core beliefs, assumptions, and automatic thoughts @xcite . cognitive reframing is central to cbt, helping individuals reinterpret negative experiences by critically reasoning through and aligning them with their belief systems to find purpose or positive meaning in adversity @xcite . recent advancement in large language models (llms) research have focused on reasoning, which stands * equal contribution. out as a fundamental element of human intelligence that drives key processes like problem-solving, decision-making, and critical thinking @xcite . furthermore, llms that incorporate reasoning in its pretraining phase or as a post-hoc augmentation procedure have shown significant improvement in performance across many tasks @xcite . in this paper, we investigate the extent to which reasoning can improve llm's ability in cognitive reframing. we implement and evaluate three conditions of llm reasoning on established cognitive reframing tasks, which include generating, recognizing, and reframing unhelpful thoughts. in addition, we propose a novel task of reframing thoughts conditioned on reframing strategies based on positive psychology @xcite . the reasoning conditions we evaluate include: (1) llms pre-trained specifically for reasoning; (2) llms augmented with state-of-the-art reasoning methods such as cot @xcite , tot @xcite , and self-consistency @xcite and dot @xcite ; and (3) non-reasoning llms that were not explicitly trained or augmented with reasoning capabilities. we find that reasoningaugmented models consistently outperform pretrained reasoning models, suggesting that simply augmenting llms with reasoning strategies can provide strong performance gains on cognitive reframing tasks without the cost and complexity of pretraining explicitly for reasoning. ## related work early ai systems for cognitive reframing early mental health chatbots and apps incorporated elements of cognitive reframing, but relied on scripted responses or simple ai @xcite . systems like the cbt-based chatbot wysa could walk users through cbt-style prompts by using ai to select from pre-written therapist re-sponses, but they lacked the flexibility to produce personalized new reframes @xcite . llms for identifying and reframing unhelpful thoughts recent studies have begun leveraging llms to identify and reframe unhelpful thoughts in more flexible ways. previous work explored llmassisted cognitive reframing by training a retrievalaugmented model to suggest alternative thoughts with controlled therapeutic attributes @xcite . others introduced a "diagnosis of thought" prompting technique that guides the model to separate facts from subjective interpretations and reason about evidence, significantly improving the detection of distorted thinking patterns while producing expert-approved explanatory rationales @xcite . these works demonstrate the feasibility of llms both in generating helpful reframed thoughts and in pinpointing unhelpful thinking. ## experiments in this work, we investigate the contribution of reasoning methods in cognitive reframing. we utilize the patternreframe dataset @xcite , where each sample contains (1) a persona (i.e "i enjoy gardening. my favorite drink is red wine. i work for a clothes retailer. i have one child."), (2) unhelpful thought (i.e. "my child wishes they had another sibling. i bet they think i'm a horrible parent for stopping at one child."), (3) the unhelpful thinking pattern (i.e. "jumping to conclusions: mind reading"), and (4) the reframed positive thought (i.e. "my child wishes they had another sibling, but i'm grateful i can focus all my attention on one child.") and the aligned reframe strategy (i.e. "optimism"). the unhelpful thinking patterns as well as strategies used to reframe unhelpful thoughts are both grounded in psychology literature @xcite , @xcite . we sample a set of 1,000 examples from the dataset such that the occurence of each unhelpful thinking pattern is distributed uniformly (∼100 per category, e.g., personalization, catastrophizing) for use across all tasks. ## tasks & results to evaluate the effectiveness of varying conditions of modern llm reasoning methods, we incorporate the following tasks: (1) recognizing unhelpful thought patterns, (2) generating unhelpful thoughts, and (3) generating reframes of unhelpful thoughts, in line with the proposed tasks from patternreframe @xcite . given the advances of instruction tuning and alignment @xcite , we propose a novel (4)-th task: generating strategic reframes of unhelpful thought, strictly enforcing the reframe of the unhelpful thought to be aligned to a specific reframing strategy. the performance of representative models from each condition (pr, nr, ar) are shown in fig. 1 , where we find that simple augmented reasoning methods perform well across all tasks, and obtain massive performance gains for the task of unhelpful thought pattern recogntion. task 1: recognition of unhelpful thought patterns assesses whether llms can recognize the unhelpful thinking pattern given a description of the persona and the unhelpful thought. an example prompt for this task can be found in app. b.1. we conduct an automatic performance evaluation using f1-score, accuracy, precision, and recall from prior literature @xcite . the results for task 1 are presented in task 2: generation of unhelpful thought assesses how well llms can generate an unhelpful thought given a persona and unhelpful thought pattern as shown in app. b.2. for automatic performance evaluation on this task, we report the rouge @xcite , bertscore @xcite , and a sentence similarity metric using the multilingual-e5-large-instruct embedding model @xcite -one of the top-5 best performing embedding models for retrieval on the - augmented models; • : non-reasoning models; • : pretrained reasoning models). as indicated by the best performing model, encoded with a larger circle, we find that reasoning-augmented models can outperform pretrained reasoning models. : linear regression fit on average output tokens to performance. we observe a positive linear relationship for the task of recognition and a negative relationship for reframe generation. mteb benchmark @xcite . as seen in task 3: reframing of unhelpful thought is used to assess how well llms can generate a reframe of the persona's unhelpful thought given a persona, an unhelpful thought, and the unhelpful thinking pattern. an example is shown in app. b.3. as displayed in