Summary of Evaluating Theory Of (an Uncertain) Mind: Predicting the Uncertain Beliefs Of Others in Conversation Forecasting, by Anthony Sicilia and Malihe Alikhani
Evaluating Theory of (an uncertain) Mind: Predicting the Uncertain Beliefs of Others in Conversation Forecasting
by Anthony Sicilia, Malihe Alikhani
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper explores a novel approach to evaluating Theory of Mind by quantifying the uncertainty of others’ beliefs in dialogue. The authors design a suite of tasks that challenge language models (LMs) to model the uncertainty of interlocutors in conversation forecasting. The task involves predicting the probability of an unobserved outcome, with LMs asked to forecast the uncertainty of others. The paper conducts experiments on three dialogue corpora using eight LMs, exploring re-scaling methods, variance reduction strategies, and demographic context. While LMs show some ability to explain up to 7% variance in the uncertainty of others, the authors highlight the difficulty of the tasks and the need for future work, particularly in practical applications like anticipating false negatives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how we can better understand what other people think. Right now, we usually just look at whether someone believes something or not. But what if they’re not sure? The authors came up with a new way to test language models by asking them to predict how uncertain someone else might be about their own beliefs. This is like forecasting the outcome of a conversation. They tested different approaches on three types of conversations (social, negotiation, and task-oriented) using eight different language models. While the results were promising, showing that LMs can explain some of the uncertainty in others’ beliefs, there’s still more to learn and explore. |
Keywords
» Artificial intelligence » Probability
