Reasoning Abilities of LLMs
Types of reasoning. Challenges. How to enhance LLMs.
Welcome to Infinite Curiosity, a weekly newsletter that explores the intersection of Artificial Intelligence and Startups. Tech enthusiasts across 200 countries have been reading what I write. Subscribe to this newsletter for free to receive it in your inbox every week:
First of all, what is reasoning? It’s the process of solving problems, drawing conclusions, or making inferences. And you do it based on logic, evidence, and the information available to you.
Reasoning is a cognitive process. Humans do all the time. We reason through things to take relevant next steps in our daily lives. As Large Language Models (LLMs) are increasingly penetrating all the products, we expect those products to reason as well. In this post, I’ll talk about the mechanics of reasoning and what we expected from an LLM product that can reason.
Types of reasoning
LLMs use various types of reasoning to process and generate responses. Here are some key types of reasoning associated with LLMs and the corresponding benchmarks that are used to assess them:
Arithmetic Reasoning: This involves the ability to understand and apply mathematical concepts. The goal is to solve problems that require arithmetic operations. Benchmarks like MathQA and SVAMP are used to evaluate this capability.
Common Sense Reasoning: This uses everyday knowledge to make judgments and predictions about new situations. It is fundamental for decision-making in scenarios where we have incomplete information. Benchmarks like CSQA and StrategyQA assess commonsense reasoning.
Symbolic Reasoning: This involves manipulating symbols according to formal rules to deduce or solve problems. It is evaluated using benchmarks like LastLetterConcat and Coin Flip.
Logical Reasoning: This involves the process of using a structured and coherent sequence of steps to arrive at a conclusion based on given evidence. It can be further divided into 3 types:
Deductive Reasoning: LLMs can perform deductive reasoning with structured prompts and clear logical sequences. They are good at following logical steps if the problem is framed well but can struggle with complex or deeply nested logical structures.
Inductive Reasoning: LLMs can generalize from specific examples to broader patterns. This makes them useful for tasks like classification, pattern recognition, and prediction. But they can be biased by the data they were trained on and may overfit to certain patterns.
Abductive Reasoning: LLMs can generate plausible explanations for given observations. This is often used in creative writing or hypothesis generation. But their explanations are often not grounded in physical or causal realities but in patterns seen in the data.
Causal Reasoning: This involves understanding cause-effect relationships and is crucial for predicting outcomes based on given data.
Challenges
There are several challenges when it comes to reasoning with LLMs. Here are 4 key challenges:
They struggle with very long contexts: While LLMs can manage large contexts, they can still struggle with very long or complex contexts. This leads to degradation in reasoning accuracy. They are prone to lose track of details or revert to generalities.
They lack true common sense: LLMs lack true common sense and often provide responses that are logically consistent but practically nonsensical. They can handle common sense queries to some extent but fail in edge cases.
They struggle with complex mathematics: LLMs can perform basic arithmetic and algebraic operations but struggle with complex mathematics. They also struggle with problems requiring step-by-step reasoning over multiple operations. Specialized models or tools are often required to supplement these tasks.
They have limited understanding of cause-and-effect: LLMs have limited capability in understanding cause-and-effect relationships, often confusing correlation with causation. This is a significant area of ongoing research.
Techniques to enhance the reasoning capabilities
There are several techniques to enhance the reasoning capabilities of LLMs:
Chain-of-Thought Prompting: This technique involves guiding the model through a series of logical steps to arrive at a conclusion. This enhances reasoning by structuring the thought process.
Instruction Tuning: This method involves refining the model's responses by reminding it of the reasoning patterns and knowledge it has already learned.
Tool Use: LLMs can be augmented with external tools (e.g. calculators, code interpreters, knowledge databases) to improve reasoning in specialized domains. This hybrid approach enhances their overall capabilities but requires careful orchestration.
Self-Reflection: Newer approaches involve LLMs reflecting on their own outputs to assess consistency or correctness. This can improve reasoning accuracy. This form of self-verification is an active area of research.
LLMs demonstrate proficiency in reasoning tasks that align with their training data. But they often face challenges in out-of-distribution scenarios. And this highlights the need for more sophisticated reasoning assessments.
If you're a founder or an investor who has been thinking about this, I'd love to hear from you. I’m at prateek at moxxie dot vc.
If you are getting value from this newsletter, consider subscribing for free and sharing it with 1 friend who’s curious about AI: