Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering

Centrala begrepp

Instruction-following models can perform question answering by leveraging provided text passages, but their verbose responses make traditional evaluation metrics unreliable. This work proposes evaluating these models along two dimensions - correctness in satisfying the user's information need, and faithfulness in grounding the response in the provided knowledge.

Sammanfattning

The paper evaluates the performance of several instruction-following models, including Flan-T5, Alpaca, GPT-3.5, and Llama-2, on three question answering datasets - Natural Questions, HotpotQA, and TopiOCQA.

The key highlights are:

1. Traditional QA metrics like Exact Match and F1 are not well-aligned with the verbose nature of instruction-following models, often penalizing them unjustly. The authors identify two main failure modes - "More Elaborate Answers" and "Open-ended Questions".

2. The authors propose simple token-overlap metrics - Recall for correctness and K-Precision for faithfulness - that correlate highly with human judgments, outperforming more complex semantic similarity metrics.

3. Evaluating the models using the proposed metrics reveals a tradeoff between correctness and faithfulness. While GPT-3.5 and Llama-2 perform comparably to task-specific fine-tuned models in terms of correctness, they struggle to be faithful to the provided knowledge, often hallucinating information.

4. The authors also investigate the models' ability to abstain from answering when provided with irrelevant knowledge, finding that GPT-3.5 and Llama-2 are more successful at this than Flan-T5 and Alpaca.

Overall, the work highlights the need for a more holistic evaluation of instruction-following models for question answering, focusing on both correctness and faithfulness.

Statistik

Instruction-following models can perform question answering by leveraging provided text passages.
Traditional QA metrics like Exact Match and F1 are not well-aligned with the verbose nature of instruction-following models.
The authors propose Recall and K-Precision as simple token-overlap metrics that correlate highly with human judgments for correctness and faithfulness, respectively.
Evaluating instruction-following models reveals a tradeoff between correctness and faithfulness, with GPT-3.5 and Llama-2 performing better on correctness but struggling with faithfulness.
The models' ability to abstain from answering when provided with irrelevant knowledge varies, with GPT-3.5 and Llama-2 being more successful than Flan-T5 and Alpaca.

Citat

"Instruction-following models can perform QA when provided with a task description, question, and relevant text passages."
"Traditional QA metrics such as exact match (EM) and F1 are unreliable, raising new challenges for evaluation."
"We posit that an optimal model should not only correctly respond to user queries but also be faithful, i.e. it should only disseminate information that is inferrable or directly stated by external documents."

Viktiga insikter från

Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering

by Vaibhav Adla... på arxiv.org 04-18-2024

https://arxiv.org/pdf/2307.16877.pdf

Djupare frågor

How can the tradeoff between correctness and faithfulness in instruction-following models be further investigated and potentially resolved?

The tradeoff between correctness and faithfulness in instruction-following models can be further investigated and potentially resolved through several approaches:

Model Architecture Optimization: Researchers can explore modifying the architecture of instruction-following models to better balance correctness and faithfulness. This could involve incorporating mechanisms that prioritize grounding responses in provided knowledge while maintaining accuracy in answering user queries.

Fine-tuning Strategies: Experimenting with different fine-tuning strategies that emphasize both correctness and faithfulness could help in finding a better balance. Fine-tuning the models on datasets that specifically focus on faithfulness could potentially improve their performance in this aspect.

Data Augmentation: Introducing additional training data that emphasizes the importance of faithfulness in responses could help instruction-following models learn to prioritize grounding information in provided knowledge. This could involve creating datasets with a focus on factual accuracy and knowledge grounding.

Multi-Task Learning: Implementing multi-task learning frameworks where models are trained on tasks that require both correctness and faithfulness could help in jointly optimizing for these dimensions. By exposing the models to diverse tasks, they can learn to balance between providing accurate answers and staying faithful to the provided knowledge.

Human-in-the-Loop Evaluation: Incorporating human evaluators in the training and fine-tuning process can provide valuable feedback on the tradeoff between correctness and faithfulness. Human annotators can help identify instances where models struggle with faithfulness and guide improvements in model behavior.

By exploring these strategies and potentially combining them, researchers can further investigate the tradeoff between correctness and faithfulness in instruction-following models and work towards resolving this challenge.

What other dimensions, beyond correctness and faithfulness, should be considered when evaluating the performance of instruction-following models for question answering?

In addition to correctness and faithfulness, several other dimensions should be considered when evaluating the performance of instruction-following models for question answering:

Relevance: Assessing the relevance of the model's responses to the user query and provided knowledge is crucial. Models should provide answers that are directly related to the information sought by the user.

Coherence: Evaluating the coherence of responses is important to ensure that the information provided is logically connected and flows well. Incoherent responses can lead to user confusion and dissatisfaction.

Conciseness: Considering the conciseness of responses is essential, especially in question-answering scenarios where users expect clear and succinct answers. Models should avoid unnecessary verbosity and provide information efficiently.

Consistency: Ensuring consistency in responses across different queries and contexts is vital for building user trust. Models should provide consistent answers to similar questions and avoid contradicting themselves.

Contextual Understanding: Evaluating the model's ability to understand and incorporate contextual information from the query and provided knowledge is crucial for generating accurate and relevant responses. Models should demonstrate a deep understanding of the context to provide meaningful answers.

Generalization: Assessing the generalization capabilities of instruction-following models across diverse datasets and tasks is important. Models should be able to adapt to new information domains and tasks without significant drops in performance.

By considering these additional dimensions in the evaluation of instruction-following models, researchers can gain a more comprehensive understanding of their performance and capabilities in question answering tasks.

How can the ability of instruction-following models to abstain from answering when provided with irrelevant knowledge be further improved and leveraged in practical applications?

The ability of instruction-following models to abstain from answering when provided with irrelevant knowledge can be further improved and leveraged in practical applications through the following strategies:

Enhanced Relevance Detection: Implementing more sophisticated relevance detection mechanisms within the models can help them better identify when the provided knowledge is irrelevant to the user query. This could involve incorporating attention mechanisms or additional training on relevance detection tasks.

Threshold Setting: Setting appropriate confidence thresholds for model responses can help in determining when to abstain from answering. Models can be trained to abstain from providing responses when their confidence in the relevance of the knowledge is below a certain threshold.

Fine-tuning on Irrelevant Knowledge: Introducing training data that specifically focuses on irrelevant knowledge scenarios can help instruction-following models learn to recognize and abstain from answering in such situations. Fine-tuning the models on datasets with irrelevant passages can improve their ability to detect and handle irrelevant information.

Human Feedback Integration: Incorporating human feedback in the training process can provide valuable insights into when models should abstain from answering. Human annotators can help identify instances of irrelevant knowledge and guide the models in learning to abstain appropriately.

Dynamic Threshold Adjustment: Implementing dynamic threshold adjustment mechanisms that adapt based on the context and relevance of the provided knowledge can help models make real-time decisions on whether to abstain from answering. This flexibility can enhance the models' ability to handle varying scenarios.

By implementing these strategies and focusing on improving the models' ability to abstain from answering when provided with irrelevant knowledge, instruction-following models can enhance their overall performance and reliability in practical question-answering applications.