Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering
Evaluates instruction-following models for QA correctness and faithfulness, proposing new metrics.
Key Findings
Methodology
The study uses retriever-augmented instruction-following models by prepending retrieved documents and instructions to adapt to various domains and tasks. It employs automatic and human evaluations to assess correctness (satisfying user needs) and faithfulness (based on provided knowledge). Proposes simple token-overlap and model-based metrics.
Key Results
- Result 1: Instruction-following models are competitive with fine-tuned models in correctness, sometimes outperforming them.
- Result 2: These models struggle with faithfulness, often hallucinating in responses.
- Result 3: Proposed token-overlap metrics correlate highly with human judgments.
Significance
The study highlights the shortcomings of traditional evaluation metrics for instruction-following models, proposing new metrics that better reflect model performance. This is significant for promoting more holistic evaluations in information retrieval and QA.
Technical Contribution
The study introduces new evaluation metrics that overcome the limitations of traditional metrics in handling verbose responses. By incorporating token-overlap and model-based evaluation methods, it provides a more human-aligned assessment approach.
Novelty
First to systematically evaluate instruction-following models for QA correctness and faithfulness, proposing new metrics that address traditional metrics' limitations in handling verbosity.
Limitations
- Limitation 1: Models perform poorly in faithfulness, prone to hallucinations.
- Limitation 2: Evaluation metrics may have limited applicability across different tasks.
Future Work
Future research could explore improving model faithfulness, developing more universally applicable evaluation metrics, and validating them across more tasks.
AI Executive Summary
Instruction-following models show great potential in QA tasks, but traditional evaluation metrics like exact match and F1 score perform poorly with verbose responses. The study evaluates these models across various domains and tasks by prepending retrieved documents and instructions.
The study finds that instruction-following models are competitive with fine-tuned models in correctness, sometimes outperforming them. However, these models struggle with faithfulness, often hallucinating in responses. To address this, the study proposes simple token-overlap and model-based metrics, which correlate highly with human judgments.
The significance of this study lies in promoting more holistic evaluations, especially in information retrieval and QA. Future research directions include improving model faithfulness, developing more universally applicable evaluation metrics, and validating them across more tasks.
Deep Analysis
Background
In recent years, instruction-following models have gained attention for their ability to perform tasks based on natural language instructions. These models are typically trained by exposing them to a wide range of NLP tasks formulated as instructions, enabling them to adapt to various domains and tasks.
Core Problem
Traditional QA evaluation metrics like exact match and F1 score perform poorly with verbose responses, failing to accurately quantify model performance. New evaluation methods are needed to better reflect model correctness and faithfulness.
Innovation
The study's innovation lies in proposing new evaluation metrics that overcome the limitations of traditional metrics in handling verbose responses. By incorporating token-overlap and model-based evaluation methods, it provides a more human-aligned assessment approach.
Methodology
- �� Use retriever-augmented instruction-following models
- �� Prepend retrieved documents and instructions to input
- �� Employ automatic and human evaluations
- �� Propose token-overlap and model-based metrics
Experiments
Experiments are conducted on three information-seeking QA datasets: Natural Questions, HotpotQA, and TopiOCQA. Automatic and human evaluations assess correctness and faithfulness.
Results
The study finds that instruction-following models are competitive with fine-tuned models in correctness, sometimes outperforming them. However, these models struggle with faithfulness, often hallucinating in responses.
Applications
The study's findings can be used to improve evaluation methods for information retrieval and QA systems, promoting more holistic model evaluations.
Limitations & Outlook
Models perform poorly in faithfulness, prone to hallucinations. Evaluation metrics may have limited applicability across different tasks.
Plain Language Accessible to non-experts
Imagine you're in a library looking for answers. Traditional QA systems are like librarians who only give you the exact page number. Instruction-following models are like friendly assistants who not only give you the page number but also tell you about the book's background and related information. While this approach gives you a better understanding, sometimes the assistant provides unrelated information. The study focuses on how to better evaluate this assistant's performance, ensuring it not only provides correct answers but also stays true to the provided information.
ELI14 Explained like you're 14
Imagine you're playing a trivia game. Traditional evaluation is like only counting how many questions you got right, regardless of how well you explained them. Instruction-following models are like a smarter player who not only answers questions but also gives detailed explanations. But sometimes, they talk too much or even get things wrong. The study aims to find better ways to evaluate this smart player, making sure they're both smart and reliable.
Glossary
Instruction-following models
Models capable of performing tasks based on natural language instructions.
Used for QA tasks.
Exact match
An evaluation metric comparing the exact match between model output and reference answer.
Traditional QA evaluation method.
F1 score
An evaluation metric combining precision and recall.
Used to evaluate QA model performance.
Faithfulness
Whether model responses are based on provided knowledge.
Evaluating if models stay true to provided information.
Hallucination
Model-generated information inconsistent with provided knowledge.
Common issue when evaluating model faithfulness.
Open Questions Unanswered questions from this research
- 1 How to improve model faithfulness and reduce hallucinations.
- 2 How to enhance evaluation metrics' applicability across different tasks.
Applications
Immediate Applications
Information retrieval systems
Improve evaluation methods for information retrieval systems, enhancing user satisfaction.
Long-term Vision
Intelligent assistants
Develop smarter, more reliable assistants providing comprehensive information services.
Abstract
Retriever-augmented instruction-following models are attractive alternatives to fine-tuned approaches for information-seeking tasks such as question answering (QA). By simply prepending retrieved documents in its input along with an instruction, these models can be adapted to various information domains and tasks without additional fine-tuning. While the model responses tend to be natural and fluent, the additional verbosity makes traditional QA evaluation metrics such as exact match (EM) and F1 unreliable for accurately quantifying model performance. In this work, we investigate the performance of instruction-following models across three information-seeking QA tasks. We use both automatic and human evaluation to evaluate these models along two dimensions: 1) how well they satisfy the user's information need (correctness), and 2) whether they produce a response based on the provided knowledge (faithfulness). Guided by human evaluation and analysis, we highlight the shortcomings of traditional metrics for both correctness and faithfulness. We then propose simple token-overlap based and model-based metrics that reflect the true performance of these models. Our analysis reveals that instruction-following models are competitive, and sometimes even outperform fine-tuned models for correctness. However, these models struggle to stick to the provided knowledge and often hallucinate in their responses. We hope our work encourages a more holistic evaluation of instruction-following models for QA. Our code and data is available at https://github.com/McGill-NLP/instruct-qa