Think Twice: Perspective-Taking Improves Large Language Models’ Theory-of-Mind Capabilities (2024)

Abstract: Human interactions are deeply rooted in the interplay of thoughts, beliefs, and desires made possible by Theory of Mind (ToM): our cognitive ability to understand the mental states of ourselves and others. Although ToM may come naturally to us, emulating it presents a challenge to even the most advanced Large Language Models (LLMs). Recent improvements to LLMs’ reasoning capabilities from simple yet effective prompting techniques such as Chain-of-Thought (CoT) have seen limited applicability to ToM. In this paper, we turn to the prominent cognitive science theory “Simulation Theory” to bridge this gap. We introduce SimToM, a novel two-stage prompting framework inspired by Simulation Theory’s notion of perspective-taking. To implement this idea on current ToM benchmarks, SimToM first filters context based on what the character in question knows before answering a question about their mental state. Our approach, which requires no additional training and minimal prompt-tuning, shows substantial improvement over existing methods, and our analysis reveals the importance of perspective-taking to Theory-of-Mind capabilities. Our findings suggest perspective-taking as a promising direction for future research into improving LLMs’ ToM capabilities.

## introduction what did the group of friends feel as they gathered around the fire, exchanging stories and laughter and knowing glances? underlying this seemingly commonplace setting is an intricate interplay of thoughts, beliefs, and desires weaving together the fabric of human interaction. this is the domain of theory of mind (tom): the cognitive ability to attribute mental states to ourselves and others, and to understand that others have beliefs, desires, and intentions that may differ from our own @xcite . this ## benchmarking theory-of-mind capabilities one well studied method for evaluating theory of mind capabilities is through the sally anne falsebelief tests @xcite . in essence, one agent (sally) knows something about the world, then they leave, and another agent (anne) changes something about the world. for example: sally puts a ball in the basket then leaves the room, after which anne moves the ball to the box. we can then ask a few different types of questions, for example: "where does sally believe the ball is?" if anne has moved the ball, sally's belief will be incorrect -this type of question is called false belief, and has its counterpart in true belief questions, where sally's belief about the world is correct. we can also ask about actions sally would take as a result of those beliefs, for example: "what will sally do when she returns looking for the ball?". and instead of asking about sally directly, we could also ask about what anne thinks sally thinks -this is called a second order question, contrasted with the first order questions above. to the best of our knowledge, there are two existing datasets that test these capabilities in the reading comprehension setting: tomi and bigtom. 1 ## simtom: simulated theory of mind simtom is a simple two-stage prompting framework for that enhances zero-shot tom capabilities in llms. ## experimental details our experiments are intended to investigate the effectiveness of our method by evaluate llms 0-shot on tom benchmarks with and without our sim-tom prompting framework. in this section, we detail how models are prompted and evaluated on the benchmarks described in section 3: bigtom and tomi. ## results and discussion we find that simtom leads to substantial performance improvements over 0-shot mc and cot prompting. our results in bigtom results on the bigtom benchmark, simtom provides substantial performance improvements across models. notably, on the challenging false belief subset, simtom provides 29.5% and 14.2% absolute accuracy improvements over the 0-shot and 0-shot cot gpt-3.5-turbo baselines, along with similarly strong results across other model types. simtom led to a slight decrease with gpt-4, though this result may be partially confounded by the fact that bigtom was generated using gpt-4. across all question types as well, simtom performed strongly compared with 0-shot cot probing, leading to 8.6% and 5.7% absolute improvements on llama2-7bchat and gpt-3.5-turbo for false belief questions. we again saw comparable performance with the strongest model, gpt-4, which had saturated human performance 0-shot @xcite . tomi results simtom also yields improvement over 0-shot baselines across llms on the tomi benchmark. compared to 0-shot cot on false belief (fb) questions simtom increased absolute accuracy by 16%, 19%, and a surprising 47% absolute accuracy for llama2-7b-chat, llama2-13b-chat, and gpt-3.5-turbo. it is worth noting that 0-shot cot did not perform reliably better than 0-shot, though our method performed better than both most of the time. across all question types, simtom also provided performance increases as well, although more modest, as modern models find control conditions much less challenging (e.g., "true belief" tests, which can be answered by understanding the true state of objects without understanding mental states relating to them). these improvements were still substantial, with gpt-4 seeing a 13.4% improvement and llama-2-13b-chat improving by 16.1% over 0-shot cot probing. additionally, we tested two additional prompting strategies: self-consistency cot @xcite and treeof-thoughts @xcite ) on the gpt-3.5-turbo model. sc-cot performs similarly to cot across question categories, underperforming sim-tom significantly (33.50% on fb compared to 81.00%). tree-of-thoughts also underperforms simtom substantially, perhaps because the original tree-of-thoughts voting evaluates creative writing generations instead of rationales. in general, we find that second-order questions are more difficult for most models than first-order questions; a detailed breakdown of these results across question types can be found in appendix g. ## analysis we rigorously analyze simtom with additional experiments and ablations intended to better understand our method explore the opportunities and it uncovers. ## related work evaluating llm capabilities on tom recent approaches on evaluating zero-shot llm capabilities on tom have made use of lm probing @xcite , often replaced by mc @xcite and cot probing @xcite for instructiontuned models. though these prompt-styles can elicit different performance capabilities @xcite , we modify our prompts only minimally to evaluate our hypotheses, motivated by @xcite 's argument that excessive prompt-tuning violates a "reasonable expectation" of evaluation standards for llms on tom. enhancing llm reasoning capabilities motivated in part by early results that transformers can engage in reasoning tasks @xcite , a number of prompt-based methods for enhancing llm reasoning capabilities have been published in recent years, such as proofwriter @xcite . @xcite point out that llms can be easily distracted by irrelevant information during reasoning, and @xcite use llms to select relevant facts before inferring new ones. this selection is somewhat similar to our perspectivetaking, but a key distinction lies in that perspectivetaking attempts not only to isolate relevant facts, but ones that agents should not know about when being simulated -it is not clear how to implement relevance for selection in tom. recent approaches have also combined llm reasoning with symbolic reasoning algorithms. @xcite separates "system 1" and "system 2" into neural-and symbolic-systems respectively to perform reasoning tasks. a relevant contemporaneous work, @xcite , uses llms to construct a symbolic belief state graph and answers questions using an inference-time graph algorithm. it is not clear, however, how to apply these symbolic methods to naturalistic tom datasets in such as bigtom. more broadly, these approaches both represent a distinct direction to ours, as our approach studies how to enable tom capabilities in llms without external memory or human-written inference-time algorithms. ## conclusion in this paper, we propose a two-prompt approach to eliciting theory-of-mind capabilities in llms by first asking llms to perspective-take: to filter context to what the character in question knows. we find that llms are substantially stronger at tom reasoning than they appear when probed with a single inference pass. our key takeaways from rigorously analyzing simtom are that (1) that perspective-taking is best implemented as a separate prompt before question-answering, instead of in a single prompt (section 7.1); (2) that today's llms could close-to-solve tom benchmarks with access to oracle perspective-taking capabilities (section 7.2); and (3) that llms with improved perspective-taking capabilities could achieve even stronger performance on tom tasks (section 7.3).