Temporal Biases in Large Language Models: Divergent Preferences for Before and After Relations

Core Concepts

Large language models, such as GPT-3.5 and GPT-4, exhibit divergent inductive biases when processing temporal data, with GPT-3.5 favoring "AFTER" relations and GPT-4 preferring "BEFORE" relations in question-answering tasks, and GPT-3.5 tending towards "TRUE" and GPT-4 towards "FALSE" in textual entailment tasks.

Abstract

This research explores the temporal comprehension abilities of GPT-3.5 and GPT-4, two prominent large language models (LLMs), with a focus on understanding their grasp of temporal relationships.
The analysis was conducted using two prompt formats: Question Answering (QA) and Textual Entailment (TE). In the QA format, the models were tasked with determining the temporal relation ("BEFORE" or "AFTER") between two events, for both implicit and explicit events. In the TE format, the models were presented with a context and a sentence declaring the temporal relation between two events, and were asked to assess its truthfulness.
The findings reveal notable trends and disparities in the performance of GPT-3.5 and GPT-4. In the QA format, GPT-3.5 demonstrated a preference for the "AFTER" relation, while GPT-4 leaned towards the "BEFORE" relation, for both implicit and explicit events. In the TE format, a consistent pattern emerged where GPT-3.5 tended towards "TRUE" and GPT-4 exhibited a preference for "FALSE" for both implicit and explicit events.
The persistent discrepancy between GPT-3.5 and GPT-4 in handling temporal data highlights the intricate nature of inductive bias in LLMs, suggesting that the evolution of these models may not merely mitigate bias but may introduce new layers of complexity.

Stats

GPT-3.5 favored "AFTER" in 51.71% of explicit events and 53.48% of implicit events in the QA format.
GPT-4 favored "BEFORE" in 67.07% of explicit events and 58.45% of implicit events in the QA format.
In the TE format, GPT-3.5 exhibited a bias towards "TRUE" in 75.07% of implicit events and 99.12% of explicit events.
In the TE format, GPT-4 exhibited a bias towards "FALSE" in 92.17% of implicit events and 58.94% of explicit events.

Quotes

"GPT-3.5 demonstrated a bias towards 815 prompts as "AFTER" and 761 as "BEFORE", indicating a preference for AFTER, as shown in Figure 3."
"In contrast, GPT-4 exhibited a preference for "BEFORE", leaning towards 1057 prompts as "BEFORE" and 519 as "AFTER", revealing a divergent pattern between the two models."
"GPT-3.5 tends to show a bias towards "True", while GPT-4 leans towards "False" as shown in Figure 4. This bias was consistently observed in both implicit and explicit events, revealing a contradicting bias between the models."

Key Insights Distilled From

Unveiling Divergent Inductive Biases of LLMs on Temporal Data

by Sindhu Kisho... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01453.pdf

Deeper Inquiries

How do the observed biases in GPT-3.5 and GPT-4 relate to their underlying architectures and training methodologies?

The observed biases in GPT-3.5 and GPT-4 can be attributed to their underlying architectures and training methodologies. GPT-3.5 and GPT-4 are Large Language Models (LLMs) that rely on transformer architectures, specifically utilizing the transformer-based architecture developed by OpenAI. These models are trained on vast amounts of text data to learn patterns and relationships within the data.
The biases seen in these models can stem from various factors in their architecture and training. For instance, the architecture of the transformer model, with its self-attention mechanism, may lead to certain patterns being amplified or favored during training. Additionally, the training data used to train these models may contain inherent biases that are inadvertently learned and reflected in the model's predictions.
Moreover, the training methodologies employed, such as the choice of hyperparameters, the size of the training data, and the specific tasks used for pre-training and fine-tuning, can also influence the biases exhibited by the models. These factors interact in complex ways during the training process, leading to the emergence of biases in the models' predictions, as observed in the study comparing GPT-3.5 and GPT-4 on temporal data.

What are the potential implications of these biases in real-world applications that rely on temporal reasoning, such as event summarization, future event prediction, and medical information processing?

The biases observed in GPT-3.5 and GPT-4 in temporal reasoning have significant implications for real-world applications that rely on accurate temporal understanding. In event summarization, where the chronological order of events is crucial, biases towards specific temporal relationships like "BEFORE" or "AFTER" can lead to inaccuracies in summarizing events. This can result in misleading or incomplete summaries, impacting decision-making based on these summaries.
In future event prediction, biases in temporal reasoning can affect the model's ability to forecast upcoming events accurately. If the model consistently favors certain temporal relationships, it may overlook important cues that indicate a different sequence of events, leading to erroneous predictions. This can have serious consequences in various domains, such as finance, weather forecasting, or supply chain management.
In medical information processing, where understanding the temporal sequence of events is vital for diagnosis and treatment planning, biases in temporal reasoning can result in incorrect interpretations of patient data. Misjudging the order of medical events or treatments can lead to inappropriate interventions or delayed care, jeopardizing patient outcomes and safety.
Overall, the biases in temporal reasoning exhibited by GPT-3.5 and GPT-4 can undermine the reliability and accuracy of applications that heavily rely on temporal data, potentially impacting decision-making, risk assessment, and overall performance in various fields.

Could the introduction of new temporal reasoning mechanisms or specialized training on temporal data help mitigate the observed biases in these large language models?

Introducing new temporal reasoning mechanisms or providing specialized training on temporal data could indeed help mitigate the observed biases in large language models like GPT-3.5 and GPT-4. By incorporating explicit temporal reasoning mechanisms into the model architecture, such as modules designed to handle temporal dependencies and relationships more effectively, the models can learn to make more accurate predictions in temporal tasks.
Specialized training on temporal data can involve fine-tuning the models on datasets specifically curated to emphasize diverse temporal relationships and dependencies. By exposing the models to a wide range of temporal scenarios and providing feedback on their predictions, the models can learn to generalize better and reduce biases towards specific temporal relationships.
Furthermore, incorporating techniques like adversarial training, where the model is trained to recognize and mitigate biases in its predictions, can also be beneficial. By explicitly encouraging the model to make unbiased predictions in temporal tasks, it can learn to balance its responses and avoid favoring certain temporal relationships over others.
Overall, a combination of new temporal reasoning mechanisms, specialized training on temporal data, and techniques to address biases in predictions can collectively help mitigate the observed biases in large language models like GPT-3.5 and GPT-4, enhancing their performance and reliability in temporal reasoning tasks.