The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Are LLMs Good at Causal Reasoning? with Robert Osazuwa Ness

Mon Jul 17 2023

causalitylarge language modelscausal reasoningcausal analysismodel evaluation

Description

This episode explores the topic of causality and large language models (LLMs). It discusses the capabilities of LLMs in causal reasoning, the challenges and limitations of using LLMs for causal analysis, and the evaluation of LLMs' performance in causal reasoning tasks. The episode also highlights concerns and future directions in causal reasoning, the generation of causal explanations by LLMs, and the alignment of LLMs with human causal judgments. The importance of human-in-the-loop analysis and prompt engineering when deploying causal inference models is emphasized. The episode concludes with a recommendation for a tool to work with large language models.

Insights

Causal reasoning models need to be evaluated for their reasoning process

Evaluation should focus on how well the model uses causal assumptions and its grounding in causal abstractions.

Large language models (LLMs) can assist human causality researchers

LLMs have the potential to supercharge causal analyses and provide valuable insights.

GPT-3.5-turbo and GPT-4 show improved capabilities for causal reasoning

These models outperform previous models like GPT-2 in causal inference tasks.

Prompt engineering is crucial when deploying causal inference models

Careful design of prompts can improve models' performance in causal judgment tasks.

Models' alignment with human causal judgments can be evaluated

Components of human causal judgments can be used to assess how well a model aligns with human reasoning.

Personalization and task-specific training can improve model performance

RLHF approaches and interleaved intervention training methods can align models with specific causal models for tasks.

Language models bridge the gap between domain knowledge and statistical analysis

They have the potential to assist in translating domain knowledge into computable artifacts for causal inference.

Human-in-the-loop analysis is crucial for detecting blind spots in models

Models should be robust enough to capture domain knowledge and consider factors beyond what is explicitly provided.

Evaluation of models based on actual causality is an interesting area of exploration

Assessing how well a model understands and reasons about causality beyond benchmark performance is important.

A tool for working with large language models is recommended

The tool provides features like answer selection, token healing, and structured JSON output.

Chapters

Causality and Large Language Models
Challenges and Limitations of Large Language Models
Evaluation and Performance of Causal Reasoning Models
Concerns and Future Directions in Causal Reasoning
Generating Causal Explanations and Model Performance
Model Limitations and Human-in-the-Loop Analysis
Evaluation of Model's Alignment with Human Causal Judgments
Tool for Working with Large Language Models

Summary

Transcript

Causality and Large Language Models

00:01 - 07:25

Robert Ness, a senior researcher at Microsoft Research, professor at Northeastern University and founder of Altdeep.ai, discusses the topic of causality and large language models (LLMs).
The paper 'Causal Reasoning and Large Language Models: Opening a New Frontier for Causality' explores the capabilities of LLMs in the field of causality.
Causal analysis is important across various fields, including econometrics, epidemiology, statistics, and natural sciences. It involves making causal conclusions from observational or experimental data.
The goal is to enable learning agents to reason causally and make correct causal conclusions comparable to human reasoning.
Pairwise causal discovery is one example discussed in the paper. It involves determining which variable causes another variable using covariance-based analysis or by asking LLMs directly.
Full graph causal discovery aims to learn an entire causal graph of relationships between variables.
In fields like metrics and epidemiology, researchers may use LLMs to analyze whether something causes a specific effect.
The paper examines how well GPT-3.5 and GPT-4 perform on causal inference tasks and whether they can reliably answer natural language causal queries.

Challenges and Limitations of Large Language Models

07:05 - 14:27

The podcast discusses the challenge of posing natural language causal queries and getting reliable answers.
Existing benchmarks are not effective in answering these specific causal questions.
Access to model weights, training data, and architecture may be necessary to answer these questions accurately.
Large language models (LLMs) can supercharge causal analyses and assist human causality researchers, modelers, and analysts.
LLMs perform well on causality benchmarks but have limitations in certain areas.
GPT-3.5-turbo and GPT-4 show improved capabilities for causal reasoning compared to previous models like GPT-2.
The reasons behind the improved performance of LLMs in causal reasoning are still mysterious.
Accuracy metrics on benchmarks may not be sufficient when deploying LLMs for decision-making involving causal logic.
An example is given where an LLM is used to evaluate job applications based on educational and professional background while excluding gender or ethnicity factors.

Evaluation and Performance of Causal Reasoning Models

14:01 - 21:12

Causal reasoning models need to be evaluated for their reasoning process and not just the final decision.
Identification is important in causal reasoning, ensuring that the model uses causal assumptions.
Probing procedures can help understand how models are grounded in causal abstractions.
The benchmarks showed good performance for causal reasoning tasks compared to the state of the art.
The models did not read the benchmarks but were given variable names and prompts.
System prompts and chain of thought reasoning mattered in improving performance.
Models made some errors due to lack of clarification or asking clarifying questions.
Better prompting could improve models' performance in causal judgment tasks.

Concerns and Future Directions in Causal Reasoning

20:52 - 27:31

Concerns about the validity of pairwise causal discovery
Memorization as a concern in large language models
Desire for some level of memorization in broad facts like altitude and temperature
Testing whether the benchmark was in the training data
Partitioning the task into two probabilities: learning causal facts and answering specific questions based on those facts
Benchmarks only evaluate the second probability, not how well a model has learned causal effects
The need to know how well the model can generalize beyond the benchmark
Exploring ways to prompt the model with inductive biases for better generalization
Using Occam's razor as an inductive bias experiment

Generating Causal Explanations and Model Performance

27:03 - 34:30

The study explored the use of large language models in generating causal explanations.
Occam's razor suggests that the explanation with the fewest external factors is preferred.
Generating arguments that introduce additional factors resulted in lower accuracy compared to GPT-4.
The models demonstrated the ability to create causal graphs based on given variables.
Performance was comparable to state-of-the-art discovery algorithms.
Informative variable names are crucial for accurate graph construction.
Models excel at pairwise and full-graph discovery tasks but have limitations in other areas.
Language models can bridge the gap between domain knowledge and statistical analysis.
However, their brittleness and failure to understand certain relationships pose challenges.
Translating domain knowledge into computable artifacts remains a challenge for causal inference practitioners.

Model Limitations and Human-in-the-Loop Analysis

34:01 - 40:37

The model misses obvious factors while performing well in other areas.
The example of a spike in sales in December highlights the need for human-in-the-loop analysis.
The model failed to consider that December is the holiday season, which could have influenced sales.
The prompt was designed to lead the model towards focusing on the ad rather than considering other factors.
The model should be robust enough to detect blind spots and capture domain knowledge.
Prompt engineering becomes crucial when deploying causal inference models.
Future iterations of models may address these issues by becoming more focused on causal tasks.
Evaluating models based on actual causality and causal judgments is an interesting area of exploration.
GPT-4 performed well on benchmarks related to actual causality but showed lower accuracy in channel thought reasoning.
Causal judgments benchmark evaluates how well a model aligns with human reasoning about what causes what.

Evaluation of Model's Alignment with Human Causal Judgments

40:18 - 47:23

The model's ability to align with human causal judgments is evaluated.
Components of human causal judgments include necessary cause, sufficient cause, norm violation, outcome desirability, and omission vs action.
The model performs well in predicting these components, ranging from 70% to 80% accuracy.
The model struggles in determining whether other causes were norm violating.
The model accurately determines whether the outcome was undesirable or neutral.
The model achieves about 70% accuracy in determining whether the cause was an omission or action.
Prompting the model with causal components could lead to better reasoning and judgment outcomes.
Training models to emulate human responses and align with a recipe for a good answer is an interesting approach.
Personalization and task-specific training can be achieved using RLHF approaches.
Atticus Geiger's interleaved intervention training method aligns a large language model with a specific causal model for a task.
Guidance, a tool developed by Microsoft Research, helps control large language models and constrain their behavior.

Tool for Working with Large Language Models

46:57 - 47:46

The speaker recommends trying out a tool for working with large language models.
The tool is good at selecting from a specific set of answers, doing token healing, and returning a JSON with a specific structure.
The speaker encourages the audience to visit TwimleAI.com for more information about today's guest and the topics discussed in the interview.
Listeners are invited to subscribe, rate, and review the podcast on their favorite podcatcher.