Leveraging Large Language Models for Multiple Choice Question Answering
Utilizing large language models for MCQA, MCP method narrows the gap with SOTA across 20 datasets.
Key Findings
Methodology
The paper introduces the Multiple Choice Prompting (MCP) method, where questions and answer options are input together, and the model outputs the chosen answer symbol. This approach reduces computational costs and mitigates tokenization effects on answer selection. The key lies in the model's Multiple Choice Symbol Binding (MCSB) ability.
Key Results
- Across 20 datasets, MCP outperforms traditional methods with an average improvement of 9.7%, surpassing old SOTA scores on 9 datasets by up to 15% on a single task.
- MCP shows significant performance on the OpenBookQA dataset, achieving an accuracy of 83% compared to 57.4% with traditional methods.
- Using the PPA metric, Codex and Instruct models demonstrate superior MCSB ability over other models.
Significance
The study reveals that traditional cloze prompting methods underestimate LLMs' capabilities in MCQA tasks. With MCP, models can more effectively compare answer options, improving accuracy to approach or even surpass SOTA levels.
Technical Contribution
The technical contribution includes proposing the MCP method, defining the MCSB ability, and experimentally validating its effectiveness. Compared to existing SOTA methods, MCP requires no task-specific tuning, offering higher efficiency and generality.
Novelty
This is the first systematic study of the advantages of MCP in MCQA tasks. Compared to traditional cloze prompting, MCP significantly improves model accuracy by explicitly comparing answer options.
Limitations
- MCSB ability varies greatly among models, with some sensitive to answer order.
- The impact of training data leakage was not considered in experiments.
- Models perform poorly on ambiguous or multi-answer questions.
Future Work
Future research could explore methods to further enhance MCSB ability, investigate performance differences across models under MCP, and apply MCP to other tasks.
AI Executive Summary
Multiple choice question answering (MCQA) tasks are crucial in NLP, yet existing large language models (LLMs) underperform compared to state-of-the-art (SOTA) methods. This paper proposes a new Multiple Choice Prompting (MCP) method, where questions and answer options are input together, requiring the model to output the chosen answer symbol, thereby improving model accuracy.
In experiments across 20 different datasets, MCP consistently outperformed traditional cloze prompting methods, with an average improvement of 9.7%. Notably, on the OpenBookQA dataset, MCP achieved an accuracy of 83%, significantly surpassing the 57.4% accuracy of traditional methods.
The study demonstrates that MCP can effectively close the gap with SOTA, even surpassing it on some datasets. Future research could further explore MCP's application to other tasks and methods to enhance models' multiple choice symbol binding ability.
Deep Analysis
Background
MCQA tasks are a significant area in NLP. Traditional methods often use cloze prompting, separating questions and answer options. While LLMs excel in many tasks, they lag behind SOTA methods in MCQA.
Core Problem
The core problem is that traditional cloze prompting methods fail to effectively compare answer options, leading to poor performance in MCQA tasks. A method that can explicitly compare answer options is needed to improve model accuracy.
Innovation
The paper introduces the MCP method, inputting questions and answer options together and requiring the model to output the chosen answer symbol. This approach reduces computational costs and mitigates tokenization effects on answer selection.
Methodology
- �� Input questions and answer options together into the model
- �� Require the model to output the chosen answer symbol
- �� Evaluate model performance using MCSB ability
- �� Compare different models' MCSB ability using the PPA metric
Experiments
The experimental design includes 20 different datasets, covering tasks like commonsense reasoning and reading comprehension. Codex and Instruct models are evaluated, comparing MCP and traditional methods.
Results
Results show that MCP outperforms traditional methods on most datasets, with an average improvement of 9.7%. Notably, MCP achieved 83% accuracy on the OpenBookQA dataset.
Applications
MCP can be directly applied to MCQA tasks, improving model accuracy. Its generality and efficiency offer broad potential applications in academia and industry.
Limitations & Outlook
MCSB ability varies greatly among models, with some sensitive to answer order. The impact of training data leakage was not considered in experiments.
Plain Language Accessible to non-experts
Imagine you're taking a multiple-choice test at school. Usually, you read the question, then each option one by one, and finally choose the best answer. Traditional cloze prompting is like only seeing the question without the options and guessing the answer directly. The multiple choice prompting method, however, is like having the question and options laid out in front of you, allowing you to compare each option and choose the best one. This method is not only more natural but also more effective because it lets the model think like a human, comparing the pros and cons of each option. With this method, the model can answer questions more accurately, just like you can make better choices during a test.
ELI14 Explained like you're 14
Imagine you're playing a trivia game with lots of multiple-choice questions. The old game rules only let you see the question and guess the answer. But now, with new rules, you can see both the question and all the options, and then pick the one you think is right. It's like getting more information, making it easier to win the game! This new method makes the game more fun and helps you answer questions more accurately. Scientists found that using this method, computers can also answer questions better, just like you do in the game!
Glossary
Large Language Model (LLM)
A model trained on vast text data, capable of performing various NLP tasks.
Used as a foundational model for MCQA tasks.
Multiple Choice Prompting (MCP)
A method where questions and answer options are input together, requiring the model to output the chosen answer symbol.
Used to improve model performance on MCQA tasks.
Symbol Binding Ability (MCSB)
The model's ability to associate answer options with the symbols representing them.
Assessed to evaluate model performance under MCP.
Cloze Prompting (CP)
A method where questions and answer options are input separately, with the model independently evaluating each option.
Traditional method for MCQA tasks.
PPA
A metric for evaluating a model's symbol binding ability, measuring invariance to answer option order.
Used to compare different models' MCSB ability.
Open Questions Unanswered questions from this research
- 1 How can models' multiple choice symbol binding ability be further enhanced?
- 2 What is the potential for MCP's application in other tasks?
- 3 How to mitigate the impact of training data leakage on experimental results?
Applications
Immediate Applications
Educational Assessment
MCP can be used for automated exam scoring, improving accuracy and efficiency.
Online Q&A Systems
Enhance the accuracy of online Q&A systems, improving user experience.
Long-term Vision
Intelligent Educational Assistant
Develop intelligent educational assistants capable of automatically generating and evaluating multiple-choice questions, supporting personalized learning.
Abstract
While large language models (LLMs) like GPT-3 have achieved impressive results on multiple choice question answering (MCQA) tasks in the zero, one, and few-shot settings, they generally lag behind the MCQA state of the art (SOTA). MCQA tasks have traditionally been presented to LLMs like cloze tasks. An LLM is conditioned on a question (without the associated answer options) and its chosen option is the one assigned the highest probability after normalization (for length, etc.). A more natural prompting approach is to present the question and answer options to the LLM jointly and have it output the symbol (e.g., "A") associated with its chosen answer option. This approach allows the model to explicitly compare answer options, reduces computational costs, and mitigates the effects of tokenization scheme and answer option representations on answer selection. For the natural approach to be effective, the LLM it is used with must be able to associate answer options with the symbols that represent them. The LLM needs what we term multiple choice symbol binding (MCSB) ability. This ability varies greatly by model. We show that a model with high MCSB ability performs much better with the natural approach than with the traditional approach across 20 diverse datasets and largely closes the gap with the SOTA, suggesting that the MCQA ability of LLMs has been previously underestimated.