Several interesting papers are published in this week in NLP. Here is a list of them:

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Introduction

Recent advancements in large language models have significantly impacted natural language processing tasks, yet the effective application of these models to complex reasoning tasks remains a challenge. While scaling up the size of language models has shown some promise, it has not proven entirely effective in addressing the intricacies of tasks involving multi-step reasoning, such as arithmetic problem-solving, commonsense understanding, and symbolic manipulation. To bridge this gap, this paper introduces a novel method called Chain-of-Thought (CoT) prompting, which aims to enhance the reasoning abilities of large language models by providing them with a coherent series of intermediate reasoning steps.

Research Gap

The research gap addressed in this paper lies in the effectiveness of large language models (LLMs) in performing complex reasoning tasks. While scaling up the size of LLMs has been shown to improve performance in various language tasks, it has not proven to be sufficient for achieving high accuracy in challenging tasks that involve multi-step reasoning, such as arithmetic, commonsense, and symbolic reasoning.

The paper aims to bridge this gap by introducing the concept of chain-of-thought prompting, which focuses on teaching LLMs to perform reasoning through the provision of examples that demonstrate a coherent series of intermediate reasoning steps. By explicitly training the models on how to break down complex problems and generate logical solutions, the authors aim to enhance the reasoning abilities of LLMs, thereby addressing the limitations associated with standard few-shot prompting methods, which typically struggle with tasks requiring significant reasoning abilities.

Solution: CoT

The authors propose enhancing the few-shot prediction ability of large language models (LLMs) by explicitly teaching them how to perform reasoning through the chain-of-thought prompting method. By providing the models with examples that demonstrate a coherent series of intermediate reasoning steps, the authors aim to enable the LLMs to effectively break down complex problems and generate appropriate solutions. This approach is designed to address the limitations of standard few-shot prompting methods, which often struggle with tasks that require significant reasoning abilities. Through this approach, the paper demonstrates how LLMs can learn to perform reasoning tasks more effectively, leading to improved performance on various complex tasks.

Input Construction

To do the CoT reasoning, the authors manually crafted chains of thought for a few examples of each task, providing the models with a series of intermediate reasoning steps that lead to the final answer. By presenting these examples to the model as part of the few-shot prompting process, they aimed to guide the model in learning how to generate similar chains of thought for new inputs.

CoT essentially involves training the model to mimic the reasoning steps demonstrated in the provided examples. By doing so, the model can learn to break down complex tasks into manageable steps, thus enabling it to arrive at the correct solution through a coherent series of intermediate reasoning steps. The manual construction of these chains of thought serves as a teaching mechanism to help the model develop a better understanding of how to perform the reasoning required for each task, ultimately improving its performance on complex reasoning tasks.

Output Evaluation

The evaluation of the CoT performance contains two key aspects: (1) the accuracy of the final output and (2) the correctness of the intermediate reasoning.

For the accuracy of the final output, the paper compared the model’s predictions with the ground truth answers for each task.

Regarding the correctness of the intermediate reasoning, the authors conducted a manually qualitative analysis of the reasoning paths generated by the model. So, they inspected the model reasoning manually to assess how fluent and logical the produced reasoning is.

Experiments

The primary aspects of the experimental setups are as follows:

  • Tasks: They performed the experiments for three reasoning tasks:
    • Arithmetic Reasoning: This involved solving math word problems of varying complexity, assessing the model’s ability to perform arithmetic reasoning.
    • Commonsense Reasoning: This task focused on evaluating the model’s performance in answering questions that require commonsense understanding and reasoning.
    • Symbolic Reasoning: The symbolic reasoning task aimed to test the model’s capacity to perform abstract manipulations and reasoning based on symbolic inputs.
  • Datasets: The datasets used for each task are:
    • Arithmetic Reasoning Datasets: GSM8K, SVAMP, ASDiv, AQuA, MAWPS.
    • Commonsense Reasoning Datasets: CSQA, StrategyQA, Date Understanding, Sports Understanding, SayCan
    • Symbolic Reasoning Datasets:
      • Last Letter Concatenation Task: This task asks the model to concatenate the last letters of words in a name.
      • Coin Flip Task: This task asks the model to answer whether a coin is still heads up after people either flip or don’t flip the coin.
  • Baselines: The baselines included various large language models such as GPT-3, LaMDA, PaLM, UL2, and Codex, with different parameter sizes. The standard few-shot prompting method was used as a baseline for comparison.
  • Evaluation Metrics: The evaluation primarily focused on assessing the accuracy of the model’s predictions for each task. Additionally, the authors conducted qualitative analyses (manually) of the reasoning paths generated by the models to evaluate the correctness and coherence of the intermediate reasoning steps.
  • Results: The experiments demonstrated that the chain-of-thought prompting method enhanced the model’s ability to perform reasoning, leading to improved performance on complex tasks that require multi-step reasoning and logical understanding.

Conclusion

In conclusion, this paper highlights the significance of teachingLLMs how to perform reasoning through the innovative approach of CoT prompting. The experiments conducted on diverse benchmarks and tasks reveal the promising potential of this method in enhancing the few-shot prediction abilities of language models, particularly in tasks that require multi-step reasoning and logical understanding. The findings highlight the substantial performance gains achieved through the incorporation of CoT prompting, outperforming standard prompting methods and even attaining state-of-the-art accuracy on challenging benchmarks.