SimpleToM: Exposing the Gap between Explicit ToM Inference and Implicit ToM Application in LLMs

• URL: http://arxiv.org/abs/2410.13648v1
• Date: Thu, 17 Oct 2024 15:15:00 GMT
• Title: SimpleToM: Exposing the Gap between Explicit ToM Inference and Implicit ToM Application in LLMs
• Authors: Yuling Gu, Oyvind Tafjord, Hyunwoo Kim, Jared Moore, Ronan Le Bras, Peter Clark, Yejin Choi,
• Abstract summary: We test whether large language models (LLMs) can implicitly apply a "theory of mind" (ToM) to predict behavior. We create a new dataset, SimpleTom, containing stories with three questions that test different degrees of ToM reasoning. To our knowledge, SimpleToM is the first dataset to explore downstream reasoning requiring knowledge of mental states in realistic scenarios.
• Score: 72.06808538971487
• License:
• Abstract: While prior work has explored whether large language models (LLMs) possess a "theory of mind" (ToM) - the ability to attribute mental states to oneself and others - there has been little work testing whether LLMs can implicitly apply such knowledge to predict behavior, or to judge whether an observed behavior is rational. Such skills are critical for appropriate interaction in social environments. We create a new dataset, SimpleTom, containing concise, diverse stories (e.g., "The can of Pringles has moldy chips in it. Mary picks up the can in the supermarket and walks to the cashier."), each with three questions that test different degrees of ToM reasoning, asking models to predict (a) mental state ("Is Mary aware of the mold?"), (b) behavior ("Will Mary pay for the chips or report the mold?"), and (c) judgment ("Mary paid for the chips. Was that reasonable?"). To our knowledge, SimpleToM is the first dataset to systematically explore downstream reasoning requiring knowledge of mental states in realistic scenarios. Our experimental results are intriguing: While most models can reliably predict mental state on our dataset (a), they often fail to correctly predict the behavior (b), and fare even worse at judging whether given behaviors are reasonable (c), despite being correctly aware of the protagonist's mental state should make such secondary predictions obvious. We further show that we can help models do better at (b) and (c) via interventions such as reminding the model of its earlier mental state answer and mental-state-specific chain-of-thought prompting, raising the action prediction accuracies (e.g., from 49.5% to 93.5% for GPT-4o) and judgment accuracies (e.g., from 15.3% to 94.7% in GPT-4o). While this shows that models can be coaxed to perform well, it requires task-specific interventions, and the natural model performances remain low, a cautionary tale for LLM deployment.

Related papers

• Looking Inward: Language Models Can Learn About Themselves by Introspection [7.544957585111317]
  Introspection gives a person privileged access to their current state of mind. We define introspection as acquiring knowledge that is not contained in or derived from training data. We study introspection by finetuning LLMs to predict properties of their own behavior in hypothetical scenarios.
  arXiv  Detail & Related papers  (2024-10-17T17:24:10Z)
• Eliciting Uncertainty in Chain-of-Thought to Mitigate Bias against Forecasting Harmful User Behaviors [29.892041865029803]
  Conversation forecasting tasks a model with predicting the outcome of an unfolding conversation. It can be applied in social media moderation to predict harmful user behaviors before they occur. This paper explores what extent model uncertainty can be used as a tool to mitigate potential biases.
  arXiv  Detail & Related papers  (2024-10-17T15:07:53Z)
• The Devil is in the Neurons: Interpreting and Mitigating Social Biases in Pre-trained Language Models [78.69526166193236]
  Pre-trained Language models (PLMs) have been acknowledged to contain harmful information, such as social biases. We propose sc Social Bias Neurons to accurately pinpoint units (i.e., neurons) in a language model that can be attributed to undesirable behavior, such as social bias. As measured by prior metrics from StereoSet, our model achieves a higher degree of fairness while maintaining language modeling ability with low cost.
  arXiv  Detail & Related papers  (2024-06-14T15:41:06Z)
• LACIE: Listener-Aware Finetuning for Confidence Calibration in Large Language Models [69.68379406317682]
  We introduce a listener-aware finetuning method (LACIE) to calibrate implicit and explicit confidence markers. We show that LACIE models the listener, considering not only whether an answer is right, but whether it will be accepted by a listener. We find that training with LACIE results in 47% fewer incorrect answers being accepted while maintaining the same level of acceptance for correct answers.
  arXiv  Detail & Related papers  (2024-05-31T17:16:38Z)
• How FaR Are Large Language Models From Agents with Theory-of-Mind? [69.41586417697732]
  We propose a new evaluation paradigm for large language models (LLMs): Thinking for Doing (T4D) T4D requires models to connect inferences about others' mental states to actions in social scenarios. We introduce a zero-shot prompting framework, Foresee and Reflect (FaR), which provides a reasoning structure that encourages LLMs to anticipate future challenges.
  arXiv  Detail & Related papers  (2023-10-04T06:47:58Z)
• Probing the Multi-turn Planning Capabilities of LLMs via 20 Question Games [14.063311955315077]
  Large language models (LLMs) are effective at answering questions that are clearly asked. When faced with ambiguous queries they can act unpredictably and produce incorrect outputs. This underscores the need for the development of intelligent agents capable of asking clarification questions to resolve ambiguities effectively.
  arXiv  Detail & Related papers  (2023-10-02T16:55:37Z)
• Language Models (Mostly) Know What They Know [10.836210010868932]
  We study whether language models can evaluate the validity of their own claims and predict which questions they will be able to answer correctly. We investigate whether models can be trained to predict "P(IK)", the probability that "I know" the answer to a question, without reference to any particular proposed answer.
  arXiv  Detail & Related papers  (2022-07-11T22:59:39Z)
• Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanations [97.91630330328815]
  We conduct a crowdsourcing study, where participants interact with deception detection models that have been trained to distinguish between genuine and fake hotel reviews. We observe that for a linear bag-of-words model, participants with access to the feature coefficients during training are able to cause a larger reduction in model confidence in the testing phase when compared to the no-explanation control.
  arXiv  Detail & Related papers  (2021-12-17T18:29:56Z)
• How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering [80.82194311274694]
  We examine the question "how can we know when language models know, with confidence, the answer to a particular query?" We examine three strong generative models -- T5, BART, and GPT-2 -- and study whether their probabilities on QA tasks are well calibrated. We then examine methods to calibrate such models to make their confidence scores correlate better with the likelihood of correctness.
  arXiv  Detail & Related papers  (2020-12-02T03:53:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.