Evaluating Large Language Models' Theory of Mind Capabilities in Realistic Negotiation Scenarios

Core Concepts

Large language models struggle to effectively track and understand the mental states (desires, beliefs, and intentions) of negotiation participants, performing significantly worse than humans in a realistic negotiation scenario.

Abstract

This paper introduces NegotiationToM, a new benchmark designed to stress-test machine Theory of Mind (ToM) capabilities in real-world negotiation scenarios. The benchmark covers multi-dimensional mental states, including desires, beliefs, and intentions of negotiation participants.
The key highlights and insights from the paper are:

NegotiationToM is the first human-annotated natural conversational benchmark to evaluate large language models' ToM abilities in realistic negotiations. It goes beyond synthetic or game-based settings used in prior ToM benchmarks.

The benchmark assesses how well large language models can track the mental states of negotiation participants and maintain a coherent understanding of others' mental states as the conversation progresses and more information becomes available.

Extensive experiments show that state-of-the-art large language models, including GPT-4, ChatGPT, and Claude, perform significantly worse than humans on the NegotiationToM benchmark, even when employing advanced techniques like chain-of-thought prompting.

The paper provides detailed analysis on the types of errors made by language models, such as incorrectly inferring item preferences, beliefs about others' preferences, and intentions behind utterances.

Incorporating the annotated desire and belief states into the prompts is shown to improve language models' performance on the related task of negotiation strategy prediction, highlighting the value of the multi-dimensional mental state information.

Overall, the NegotiationToM benchmark and the findings from this work provide important insights into the limitations of current large language models in understanding and reasoning about the mental states of others in complex, real-world conversational scenarios.

Stats

"We need all three items.. but I know you might, too. So, I want to make a deal that's fair for you."
"Well, there has to be an item that you need the most. If you tell me what you need the most, we can try to make a deal around it."
"Since the forest is nearby enough, I think we'd be more interested in ensuring there's enough food instead of firewood for my people, I think."
"Hmm. I really need food too. I don't care much for water either. How about I take all 3 firewood, 2 food, and 1 water?"

Quotes

"To the best of our knowledge, NegotiationToM is the first human-annotated natural conversational benchmark to introduce negotiation theory of mind evaluation for large language models in realistic negotiations."
"Our benchmark covered multi-dimensional mental states (i.e., desires, beliefs, and intentions) to assess how well large language models can track the mental states of negotiation participants in conversations and coherent understanding of others' mental states with increased available and accessible information."
"We undertake the necessary empirical experiments to evaluate large language models (LLMs) on the NegotiationToM benchmark and conduct extensive in-depth analysis to explore the LLMs' empirical performance under various settings."

Key Insights Distilled From

NegotiationToM: A Benchmark for Stress-testing Machine Theory of Mind on Negotiation Surrounding

by Chunkit Chan... at arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.13627.pdf

Deeper Inquiries

How can the NegotiationToM benchmark be extended to include more diverse negotiation scenarios beyond the camping trip context?

To extend the NegotiationToM benchmark to encompass a broader range of negotiation scenarios beyond the camping trip context, several strategies can be implemented:

Diverse Scenarios: Introduce a variety of negotiation settings such as business deals, diplomatic negotiations, conflict resolution, or even personal interactions like buying/selling goods or services. This will provide a more comprehensive evaluation of the language models' theory of mind capabilities across different contexts.

Multiple Participants: Include scenarios with more than two participants to simulate group negotiations, where understanding the mental states of multiple individuals becomes crucial. This can add complexity and realism to the benchmark.

Cultural Context: Incorporate cultural nuances and differences in negotiation styles to test the models' ability to adapt to diverse cultural backgrounds. This can help evaluate the models' cross-cultural understanding and sensitivity.

Dynamic Environments: Introduce dynamic elements such as time constraints, changing circumstances, or hidden information during negotiations to assess the models' adaptability and decision-making in unpredictable situations.

Emotional Intelligence: Include scenarios that require the models to infer emotions, intentions, and non-verbal cues to enhance their emotional intelligence and empathy in negotiations.

By incorporating these elements, the benchmark can provide a more comprehensive evaluation of language models' theory of mind abilities in a wide range of negotiation scenarios.

How might the performance of language models on the NegotiationToM benchmark be improved by incorporating additional training data or techniques beyond chain-of-thought prompting?

To enhance the performance of language models on the NegotiationToM benchmark, several strategies can be employed beyond chain-of-thought prompting:

Data Augmentation: Incorporate a more extensive and diverse dataset with a wide range of negotiation dialogues to expose the models to various scenarios and language patterns. This can help improve the models' generalization and adaptability.

Transfer Learning: Pre-train the models on a large corpus of diverse text data before fine-tuning on the NegotiationToM dataset. This can help the models capture a broader understanding of language and improve their performance on the benchmark.

Multi-Task Learning: Train the models on multiple related tasks simultaneously, such as sentiment analysis, emotion recognition, or intent prediction, to enhance their overall understanding of human interactions and improve their theory of mind capabilities.

Adversarial Training: Introduce adversarial examples or challenging scenarios during training to make the models more robust and better equipped to handle complex negotiation dialogues.

Ensemble Methods: Combine predictions from multiple models to leverage the strengths of different architectures and improve overall performance on the benchmark.

By incorporating these additional training data and techniques, language models can enhance their theory of mind capabilities and achieve better performance on the NegotiationToM benchmark.

What insights from the NegotiationToM benchmark could be applied to develop language models with stronger theory of mind capabilities for real-world social interactions beyond just negotiation scenarios?

Insights from the NegotiationToM benchmark can be instrumental in developing language models with stronger theory of mind capabilities for real-world social interactions in various contexts:

Contextual Understanding: By analyzing how language models perform in inferring desires, beliefs, and intentions in negotiation scenarios, developers can enhance the models' contextual understanding in social interactions. This can help the models interpret human behavior more accurately.

Emotional Intelligence: Understanding emotional cues, empathy, and rapport building in negotiations can be extended to other social interactions to improve the models' emotional intelligence. This can enable the models to respond appropriately to users' emotions and sentiments.

Adaptability: Insights from handling dynamic negotiation environments can be applied to real-world social interactions that involve changing circumstances or hidden information. Models can learn to adapt their responses based on evolving situations.

Cultural Sensitivity: Evaluating models on diverse negotiation scenarios can highlight the importance of cultural context in social interactions. By incorporating cultural nuances and differences, models can develop stronger cross-cultural communication skills.

Decision-Making: Observing how models make decisions based on inferred mental states can be valuable in enhancing their decision-making abilities in social interactions. Models can learn to make more informed and contextually appropriate decisions.

By leveraging these insights and applying them to model development, language models can be equipped with stronger theory of mind capabilities for a wide range of real-world social interactions beyond negotiation scenarios.