CogBench: Evaluating Large Language Models with Cognitive Psychology Experiments

Core Concepts

The author introduces CogBench, a benchmark utilizing cognitive psychology experiments to evaluate large language models. The study highlights the importance of model size and reinforcement learning from human feedback in improving performance.

Abstract

CogBench introduces ten behavioral metrics derived from cognitive psychology experiments to evaluate 35 large language models. Results show the impact of model size, reinforcement learning, and prompt-engineering techniques on model behavior. Open-source models exhibit less risk-taking behavior compared to proprietary models.

CogBench provides insights into artificial agents' behaviors through cognitive psychology experiments. The study emphasizes the significance of behavioral metrics in evaluating large language models comprehensively.

Stats

CogBench applies ten behavioral metrics derived from cognitive psychology experiments.
35+ large language models were evaluated using statistical multilevel modeling techniques.
Larger models generally perform better and are more model-based than smaller ones.
Reinforcement learning from human feedback enhances human-like behavior in LLMs.
Open-source models exhibit less risk-prone behavior compared to proprietary ones.

Quotes

"RLHF’ed LLMs behave generally more human-like and are more accurate in estimating uncertainty."
"Our results recover the unequivocal importance of size: larger models generally perform better and are more model-based than smaller models."
"While open-source models are often believed to be more risky due to the lack of pre-prompts, we find that they make less risky decisions than proprietary models."

Key Insights Distilled From

CogBench

by Julian Coda-... at arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.18225.pdf

Deeper Inquiries

How can cognitive psychology experiments enhance our understanding of artificial intelligence beyond performance metrics?

Cognitive psychology experiments offer a unique perspective on evaluating artificial intelligence (AI) models beyond traditional performance metrics. These experiments focus on behavioral insights rather than just measuring how well the AI model performs on specific tasks. By incorporating tasks from cognitive psychology, researchers can gain a deeper understanding of the underlying mechanisms and behaviors exhibited by AI models. This approach allows for a more comprehensive evaluation of AI models, moving beyond mere performance scores to assess how they reason, learn, and make decisions in a manner that aligns with human cognition.
One key advantage of using cognitive psychology experiments is their extensive validation over many years in studying human behavior. These experiments have been designed to capture general cognitive constructs and have been rigorously tested and refined over time. By applying these validated experimental paradigms to evaluate AI models, researchers can draw parallels between human behavior and the behaviors exhibited by large language models (LLMs). This not only provides valuable insights into how LLMs function but also helps in identifying areas where they may fall short or excel compared to human-like reasoning.
Furthermore, cognitive psychology experiments often involve procedurally generated tasks that minimize data leakage concerns while offering diverse scenarios for evaluating AI models' behaviors. This ensures that the benchmarking process remains robust and unbiased, providing a more accurate assessment of an AI model's capabilities.
In essence, integrating cognitive psychology experiments into the evaluation of AI models goes beyond conventional performance metrics by focusing on understanding the inner workings and behavioral patterns of these models. It offers a holistic view of an AI system's abilities, shedding light on its decision-making processes, learning mechanisms, biases, and overall alignment with human-like behavior.

How can prompt-engineering techniques like CoT and SB influence different behavioral characteristics in large language models?

Prompt-engineering techniques such as Chain-of-Thought (CoT) prompting and Take-a-Step-Back (SB) prompting play a crucial role in influencing various behavioral characteristics in large language models (LLMs). These techniques are designed to guide LLMs through reasoning steps or abstract problem-solving strategies to improve their responses when faced with complex tasks or questions.

Chain-of-Thought Prompting (CoT):

Enhancing Probabilistic Reasoning: CoT prompts encourage LLMs to break down problems into smaller logical steps before arriving at solutions. This method aids probabilistic reasoning by helping LLMs navigate through intricate probability calculations systematically.
Improving Model-Based Behaviors: CoT promotes model-based thinking by encouraging structured problem-solving approaches that rely on building step-by-step logical chains of thought. This technique enhances an LLM's ability to consider multiple factors sequentially when making decisions.

Take-a-Step-Back Prompting (SB):

Facilitating Abstract Problem Solving: SB prompts instruct LLMs to take a broader perspective by abstracting key concepts relevant to solving complex questions before delving into detailed reasoning steps.
Promoting Meta-Cognition: SB encourages meta-cognitive processes by guiding LLMs through reflective thinking strategies that help them assess their own knowledge gaps or uncertainties during problem-solving tasks.

Overall,
prompt-engineering techniques like CoT
and SB serve as effective tools for shaping
the behavioral characteristics
of large language
models,
enhancing their reasoning abilities,
problem-solving skills,
and meta-cognitive functions across various domains.

What factors contribute to the differences in risk-taking behavior between open-source

and proprietary large language
models?
The differences observed in risk-taking behavior between open-source
and proprietary large language
models can be attributed
to several factors:

Engineering Techniques:
Proprietary
large language
models often undergo specialized engineering efforts,
including hidden pre-prompts,
that guide their responses towards safer behaviors,
potentially making them less likely
to take risks.
On the other hand,
open-source
models may lack such tailored engineering interventions,
allowing them more freedom
in decision-making processes,
resulting
in potentially higher levels
of risk-taking.

Data Quality:
The quality
of training data used
for both types
of
LLMs could impact
their risk propensity.
Proprietary
models might be trained
on high-quality datasets,
which could emphasize cautionary approaches,
leading
to lower-risk decisions.
Open-source
models might leverage diverse datasets,
potentially exposing them
to varying degrees
of uncertainty
and encouraging bolder choices.

Transparency:
The level
of transparency around training methodologies
could also play
a role.
Proprietary
model developers may employ undisclosed methods
that prioritize safety measures,
while open-source projects tend
to be more transparent about their training procedures,
potentially fostering greater experimentation
and willingness
to take risks.

Model Size:
Larger LLMS are generally associated with better performance
but may exhibit differing levels
of risk tolerance based
on individual architectural complexities.

Contextual Adaptability:
The adaptability
of each type
of LM within different contexts could influence
their respective tendencies toward risky decisions.
These factors collectively contribute
toward shaping
the distinct patterns
observed
in risk-taking behavior between open-source
proprietary LLMS.