Common Sense and LLMs: A Complex Relationship

Large Language Models (LLMs) have made significant strides in recent years, demonstrating impressive capabilities in tasks ranging from text generation to translation. However, a notable limitation of these models is their frequent lack of common sense, often leading to nonsensical or contradictory outputs. This article delves into the efforts to imbue LLMs with common sense, exploring the motivations, techniques, benchmarks, and measurement methods in this burgeoning field.

Why Common Sense Matters

Common sense, often defined as the basic ability to perceive, understand, and judge things in a practical way, is crucial for human intelligence. In the context of LLMs, it is essential for several reasons:

• Improved Realism: Common sense enables LLMs to generate more realistic and coherent text.

• Enhanced Understanding: By understanding common sense knowledge, LLMs can better comprehend and respond to user queries.

• Reduced Errors: Common sense can help to prevent the generation of nonsensical or contradictory outputs.

• Trustworthiness: Users are more likely to trust an LLM that exhibits common sense reasoning.

Techniques for Incorporating Common Sense

Several techniques are being explored to infuse LLMs with common sense:

Knowledge Graphs

Knowledge graphs represent information as a network of interconnected nodes and edges. Each node represents an entity (e.g., person, place, object) and edges represent relationships between entities. To incorporate knowledge graphs into LLMs, several techniques are employed:

• Embedding-based methods: Entities and relations are mapped into low-dimensional vector spaces. These embeddings can be used as input to LLMs or to augment their internal representations.

• Graph neural networks (GNNs): GNNs can process information from knowledge graphs directly, capturing complex relationships between entities. They can be used to generate embeddings or to augment LLM architectures.

World Models

World models aim to simulate the real world within an LLM. This involves training the model on a simulated environment where it can learn about physical laws, object interactions, and causal relationships.

• Generative models: These models can generate realistic simulations of the world, providing diverse training data for LLMs. Examples include Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs).

• Reinforcement learning: LLMs can be trained to interact with the simulated environment, learning to make decisions and achieve goals. This can help them develop a sense of causality and physical intuition.

Common Sense Reasoning Datasets

Large-scale datasets containing common sense knowledge are essential for training LLMs. These datasets often consist of question-answer pairs, multiple-choice questions, or textual descriptions of common sense scenarios.

• Supervised learning: LLMs can be trained on these datasets using supervised learning techniques, such as sequence-to-sequence models or transformers.

• Unsupervised learning: Language models can be pre-trained on massive text corpora and then fine-tuned on common sense datasets.

Transfer Learning

Transfer learning involves leveraging knowledge from one task to improve performance on another. In the context of common sense, this can involve:

• Pre-training on large text corpora: LLMs can be pre-trained on massive amounts of text data to acquire general language understanding.

• Fine-tuning on specific tasks: The pre-trained model can be fine-tuned on common sense reasoning datasets to adapt to the specific task.

Benchmarks and Evaluation

To measure the progress in imbuing common sense into LLMs, researchers have developed various benchmarks:

• CommonSenseQA: This benchmark evaluates an LLM's ability to answer common sense questions.

• Winograd Schema Challenge: This benchmark tests an LLM's ability to resolve ambiguities based on common sense knowledge.

• Story Cloze Test: This benchmark assesses an LLM's understanding of story narratives and its ability to make common sense inferences.

While these benchmarks provide valuable insights, it's important to note that evaluating common sense in LLMs remains a challenging task. Human-level common sense is complex and multifaceted, making it difficult to capture all aspects in a single metric.

Measuring Common Sense in LLMs

Currently, there is no definitive method to measure common sense in LLMs. However, researchers are exploring various approaches:

• Benchmark Performance: Comparing LLM performance on common sense benchmarks to human performance can provide a relative measure.

• Qualitative Evaluation: Human experts can assess the common sense exhibited by LLMs in generated text.

• Subjective Ratings: User surveys can be used to gather feedback on an LLM's common sense capabilities.

It is essential to develop more robust and standardized methods for measuring common sense in LLMs to accelerate progress in this area.

Conclusion

Incorporating common sense into LLMs is a complex but crucial challenge. While significant progress has been made, there is still much work to be done. By understanding the motivations, techniques, benchmarks, and measurement methods, we can gain valuable insights into the current state of the field and potential future directions. As research in this area continues to advance, we can expect to see LLMs that are increasingly capable of reasoning and understanding the world in a human-like way.

Reference

• Chen, Q., Zhu, J., and Ling, X. (2018). Knowledge Graph Embedding for Common Sense Reasoning. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL).