Improving Language Models with Curriculum Learning

Large language models (LLMs) are computer programs that can understand and create human language. They have come a long way and can do many tasks well, like answering questions, generating text, and even solving puzzles. But, there’s a catch! These models sometimes struggle with tasks that are quite different from one another. As they face a mix of simple and complex challenges in the real world, making them better at handling such variations is super important.

#In-context Learning: A New Way to Teach

In-Context Learning (ICL) is a fancy way of saying that these models learn from Examples provided right in the question they are trying to answer. Think of it like a friend sharing examples before asking for help with a tricky problem but without changing any of their brain settings. The tricky part is that it really matters which examples are chosen. Using the right examples can make a huge difference in how well the model performs. Unfortunately, methods for picking these examples often randomly choose or use simple rules, which can lead to forgettable outcomes, especially when faced with tougher problems.

#The Selection Dilemma

Various methods have been suggested to make choosing examples better, including some that don’t need any extra help from humans and others that do. However, these methods often overlook how tough the example might be. This can limit the model's ability to adapt and excel at different tasks, making it hard to tackle everything from simple questions to very difficult ones.

#A New Approach: Curriculum Demonstration Selection

To tackle this challenge, we looked at a teaching style called curriculum learning, where learners start with easier tasks and gradually work up to harder ones-like climbing a ladder instead of jumping straight to the top rung. This inspired us to create a method called Curriculum Demonstration Selection (CDS), which selects examples based on how difficult they are. This way, models get a well-rounded mix of challenges to learn from.

First, we sorted the examples into different difficulty groups; then, we picked examples from each group. With this method, models can build their skills step by step, which helps them do better on both easy and hard tasks.

Our tests showed that CDS worked better than the usual methods, especially when it came to difficult questions where other methods often missed the mark.

#What We Achieved

Our research introduced three main contributions:

1. We created a new method called Curriculum Demonstration Selection (CDS) that helps pick examples in a smart way, making it easier for models to learn.
2. We showed, through tests, that CDS works effectively and improves performance on multiple benchmarks.
3. We looked into how models react to examples of different difficulty levels and showcased how CDS can help solve tougher problems better.

#Looking at Related Ideas

#Choosing the Right Examples

In-Context Learning (ICL) is becoming popular because it allows models to learn from examples without changing their inner workings. A big challenge in ICL is how to pick the best examples, as good choices directly impact performance. Some earlier methods randomly selected examples or used ones created by humans. While these options are simple, they often produce mixed results, as not all examples may help the model effectively.

Researchers have proposed different methods instead of relying on randomness, like picking examples that are similar to the question at hand. Another approach considers how complex the examples are, focusing on those involving more steps to solve. Additionally, there are techniques that use metrics to find the most useful examples.

#Curriculum Learning

The idea of curriculum learning has inspired many studies in various areas. The core concept is simple: present learners with easier tasks first, then gradually increase the challenge. This strategy helps improve learning processes. However, many examples focus on picking similar demonstrations, often ignoring the importance of having a mix of Difficulties.

Going back to CDS, this method takes the idea of curriculum learning and applies it to demonstration selection. CDS ensures that a variety of difficulty levels are represented, making it easier for models to learn effectively.

#How We Set Up Our Study

To figure out how well CDS works, we used different categories of difficulty. We aimed to gather examples from various levels and see how they influenced the model's performance. We looked at what makes a task difficult, like its grade level. Higher grade levels mean tougher questions. If we have examples of the same level, we further classified them based on how well people usually complete those tasks.

We broke the dataset into different difficulty groups, which allows us to create a well-rounded set of examples for models to work with.

#How We Pick Examples for Learning

Once we grouped the examples according to difficulty, CDS followed a straightforward approach. It picked one example from each difficulty group. This method makes sure that models see a balanced set of examples, helping them to learn from different levels of complexity. To choose similar examples, we employed a process that uses the model’s previous knowledge to find those that closely match the question being tested.

After selecting the examples, we mixed their order. This shuffling helps prevent the models from getting too used to seeing the examples in the same order every time.

#The Fun of Math Challenges

Math is a big part of assessing how well LLMs perform. We used a tough math dataset called MATH, which has a range of problems, from easy pre-algebra to tricky number theory questions. With 7,500 training examples and 5,000 testing examples, this dataset is a goldmine for testing models. We leveraged the complexity information to help create our curriculum and ensure that the examples offered a full range of challenges.

#Good Old Commonsense Reasoning

Another important skill for models is commonsense reasoning, which is basically their ability to understand everyday situations. To test this skill, we used the ARC-Challenge dataset, which includes a mix of science questions aimed at students from grades 3 to 9. We organized the questions based on grade level, making sure we had a good mix of easy and challenging tasks for our CDS method.

#Code Generation Magic

In recent times, the ability to generate code has become an essential skill for these models. We used the Mercury dataset specifically designed to evaluate code creation. It features tasks ranging from simple corrections to more complex challenges. Again, the tasks are sorted into difficulty levels, and we used how often people usually succeed in these tasks to determine their complexity.

For our tests, we compared the Performances of several well-known open-source LLMs. We focused on their ability to handle math problems, commonsense reasoning, and code generation, with each task shedding light on how well the models perform.

#Making Sure Everything Works

We employed a straightforward decoding method for all models during testing and created prompts designed to encourage step-by-step reasoning. For each test, we provided the models with five examples. To see how CDS compared to traditional methods, we tested two different selection strategies: one that randomly selected examples and another that relied on similarity.

#Measuring Performance

For the math and commonsense reasoning tasks, we measured performance by calculating how accurate the predictions were. A prediction is correct if it matches the actual answer. For code generation tasks, we had two main measures: whether the code works correctly and how efficiently it runs in comparison to standard solutions.

#LLMs in Action

Our tests explored five widely-used LLMs focusing on math and commonsense reasoning tasks. The results showed that CDS consistently outperformed traditional methods. In the math area, CDS provided significant performance boosts, especially in algebra and number theory, while also showing improvements in geometry and precalculus.

In the commonsense reasoning benchmark, CDS again showed its strength by performing better than both random selection and the similarity-based method. The results suggest that the CDS method is not only effective but also dependable across various tasks.

#Code Generation Success

CDS also performed admirably in code generation tasks. When checked against all models in the Mercury dataset, we found that CDS significantly outperformed random and similarity-based methods. This affirms that our CDS approach is beneficial in producing accurate and efficient code.

#The Power of Selection Methods

We looked into how different retrieval approaches could affect performance in CDS. Both random selection and similarity retrieval used in CDS showed improvements over random selection alone. Interestingly, using similarity retrieval always yielded better outcomes.

#Tackling Harder Challenges

When testing how well CDS handles more difficult questions, we saw that it performs best on the hardest problems. This was evident from both the MATH and ARC-c datasets, where improvements were clear. Interestingly, the ability to handle tough questions grows as the complexity increases, confirming the effectiveness of our method.

#The Order of Examples

It might sound odd, but we found that how examples are ordered didn’t impact overall results. Whether we shuffled the examples or presented them from easy to hard, the performance remained consistent. This indicates that CDS is robust and can work well regardless of how the examples are presented.

#Wrapping It All Up

In this article, we showcased the new method called Curriculum Demonstration Selection (CDS) designed to help large language models perform better in In-Context Learning. By applying the principles of curriculum learning, CDS organizes examples by complexity, allowing models to learn effectively from a variety of challenges. Through numerous tests across different benchmarks-math reasoning, commonsense reasoning, and code generation-we demonstrated that CDS outshines traditional methods, including random selection and similarity-based approaches.

CDS shows great promise when tackling tougher problems, proving its usefulness in refining the selection of examples for in-context learning. With its structured and efficient approach, CDS amplifies the accuracy and capability of large language models, paving the way for exciting advancements in tackling a wide array of real-world tasks.

#What’s Next?

While we made some great strides, there’s still work to be done. We focused on a fixed number of examples during all our experiments, which might not tap into the full potential of CDS. Future studies could look at how changing the number of examples affects performance, especially with more complicated tasks.

Secondly, CDS used predefined complexity measures to build its curriculum. This means that it needs these measures to be available and accurate. In some cases, this information might not exist or be off base. In such scenarios, CDS would need other strategies to estimate task complexity to keep up its effectiveness.

Lastly, while this research mainly focused on three benchmarks-math reasoning, commonsense reasoning, and code generation-there’s still much to learn about how CDS performs with other types of tasks. Broader evaluations will help highlight the strengths and weaknesses of CDS across various situations, helping to refine its implementation for even better results.

By moving forward, we can unlock new potentials in improving large language models for countless problem-solving tasks, making them even smarter and more reliable companions in the world of language understanding and generation.