Making Sure Super-Smart AI Plays Nice: Testing Knowledge, Goals, and Safety

Abhishek Kumar 31 May, 2024
7 min read

Introduction

Imagine super-powered tools that can understand and generate human language, that’s what Large Language Models (LLMs) are. They’re like brainboxes built to work with language, and they use special designs called transformer architectures. These models have become crucial in the fields of natural language processing (NLP) and artificial intelligence (AI), demonstrating remarkable abilities across various tasks. However, the swift advancement and widespread adoption of LLMs bring up concerns about potential risks and the development of superintelligent systems. This highlights the importance of thorough evaluations. In this article, we will learn how to evaluate LLMs in different ways.

Making Sure Super-Smart AI Plays Nice: Testing Knowledge, Goals, and Safety

Why Evaluate LLMs?

Language models like GPT, BERT, RoBERTa, and T5 are getting really impressive, almost like having a super-powered conversation partner. They’re being used everywhere, which is great! But there’s a worry that they might also be used to spread lies or even make mistakes in important areas like law or medicine. That’s why it’s super important to double-check how safe they are before we rely on them for everything.

Benchmarking LLMs is essential as it helps gauge their effectiveness across different tasks, pinpointing areas where they excel and identifying those needing improvement. This process aids in continuously refining these models and addressing any concerns related to their deployment.

To comprehensively assess LLMs, we divide the evaluation criteria into three main categories: knowledge and capability evaluation, alignment evaluation, and safety evaluation. This approach ensures a holistic understanding of their performance and potential risks.

Large Language Model evaluation

Knowledge & Capability Evaluation of LLMs

Evaluating the knowledge and capabilities of LLMs has become a crucial research focus as these models expand in scale and functionality. As they are increasingly deployed in various applications, it is essential to rigorously assess their strengths and limitations across diverse tasks and datasets.

Question Answering

Imagine asking a super-powered research assistant anything you want – about science, history, even the latest news! That’s what LLMs are supposed to be. But how do we know they’re giving us good answers? That’s where question-answering (QA) evaluation comes in.

Here’s the deal: We need to test these AI helpers to see how well they understand our questions and give us the right answers. To do this properly, we need a bunch of different questions on all sorts of topics, from dinosaurs to the stock market. This variety helps us find the AI’s strengths and weaknesses, making sure it can handle anything thrown its way in the real world.

There are actually some great datasets already built for this kind of testing, even though they were made before these super-powered LLMs came along. Some popular ones include SQuAD, NarrativeQA, HotpotQA, and CoQA. These datasets have questions about science, stories, different viewpoints, and conversations, making sure the AI can handle anything. There’s even a dataset called Natural Questions that’s perfect for this kind of testing.

By using these diverse datasets, we can be confident that our AI helpers are giving us accurate and helpful answers to all sorts of questions. That way, you can ask your AI assistant anything and be sure you’re getting the real deal!

Question answering AI

Knowledge Completion

LLMs serve as the foundation for multi-tasking applications, ranging from general chatbots to specialized professional tools, requiring extensive knowledge. Therefore, evaluating the breadth and depth of knowledge these LLMs possess is essential. For this, we commonly use tasks such as Knowledge Completion or Knowledge Memorization, which rely on existing knowledge bases like Wikidata.

Reasoning

Reasoning refers to the cognitive process of examining, analyzing, and critically evaluating arguments in ordinary language to draw conclusions or make decisions. reasoning involves effectively understanding and utilizing evidence and logical frameworks to deduce conclusions or aid decision-making processes.

  • Commonsense: Encompasses the capacity to comprehend the world, make decisions, and generate human-like language based on commonsense knowledge.
  • Logical reasoning: Involves evaluating the logical relationship between statements to determine entailment, contradiction, or neutrality.
  • Multi-hop reasoning: Involves connecting and reasoning over multiple pieces of information to arrive at complex conclusions, highlighting limitations in LLMs’ capabilities for handling such tasks.
  • Mathematical reasoning: Involves advanced cognitive skills such as reasoning, abstraction, and calculation, making it a crucial component of large language model assessment.
How to evaluate the reasoning capabilities of a model

Tool Learning

Tool learning in LLMs involves training the models to interact with and use external tools to boost their capabilities and performance. These external tools can include anything from calculators and code execution platforms to search engines and specialized databases. The main objective is to expand the model’s abilities beyond its original training by enabling it to perform tasks or access information that it wouldn’t be able to handle on its own. There are two things to evaluate here:

  1. Tool Manipulation: Foundation models empower AI to manipulate tools. This paves the way for the creation of more robust solutions tailored to real-world tasks.
  2. Tool Creation: Evaluate scheduler models’ ability to recognize existing tools and create tools for unfamiliar tasks using diverse datasets.

Applications of Tool Learning

  • Search Engines: Models like WebCPM use tool learning to answer long-form questions by searching the web.
  • Online Shopping: Tools like WebShop leverage tool learning for online shopping tasks.
Tool learning framework for large language models

Alignment Evaluation of LLMs

Alignment evaluation is an essential part of the LLM evaluation process. This ensures the models generate outputs that align with human values, ethical standards, and intended objectives. This evaluation checks whether the responses from an LLM are safe, unbiased, and meet user expectations as well as societal norms. Let’s understand the several key aspects typically involved in this process.

Ethics & Morality

First, we assess whether LLMs align with ethical values and generate content within ethical standards. This is done in four ways:

  1. Expert-defined: Determined by academic experts.
  2. Crowdsourced: Based on judgments from non-experts.
  3. AI-assisted: AI aids in determining ethical categories.
  4. Hybrid: Combining expert and crowdsourced data on ethical guidelines.
Ethics and morals of LLMs

Bias

Language modeling bias refers to the generation of content that can inflict harm on different social groups. These include stereotyping, where certain groups are depicted in oversimplified and often inaccurate ways; devaluation, which involves diminishing the worth or importance of particular groups; underrepresentation, where certain demographics are inadequately represented or overlooked; and unequal resource allocation, where resources and opportunities are unfairly distributed among different groups.

Types of Evaluation Methods to Check Biases

  • Societal Bias in Downstream Tasks
  • Machine Translation
  • Natural Language Inference
  • Sentiment Analysis
  • Relation Extraction
  • Implicit Hate Speech Detection
Strategies for mitigating LLM bias

Toxicity

LLMs are typically trained on vast online datasets that may contain toxic behavior and unsafe content such as hate speech, offensive language. It’s crucial to assess how effectively trained LLMs handle toxicity. We can categorize toxicity evaluation into two tasks:

  1. Toxicity identification and classification assessment.
  2. Evaluation of toxicity in generated sentences.
Toxicity in AI output

Truthfulness

LLMs possess the capability to generate natural language text with a fluency that resembles human speech. This is what expands their applicability across diverse sectors including education, finance, law, and medicine. Despite their versatility, LLMs run the risk of inadvertently generating misinformation, particularly in critical fields like law and medicine. This potential undermines their reliability, emphasizing the importance of ensuring accuracy to optimize their effectiveness across various domains.

Testing truthfulness of LLMs

Safety Evaluation of LLMs

Before we release any new technology for public use, we need to check for safety hazards. This is especially important for complex systems like large language models.  Safety checks for LLMs involve figuring out what could go wrong when people use them.  This includes things like the LLM spreading mean-spirited or unfair information, accidentally revealing private details, or being tricked into doing bad things. By carefully evaluating these risks, we can make sure LLMs are used responsibly and ethically, with minimal danger to users and the world.

Robustness Evaluation

Robustness assessment is crucial for stable LLM performance and safety, guarding against vulnerabilities in unforeseen scenarios or attacks. Recent evaluations categorize robustness into prompt, task, and alignment aspects.

  • Prompt Robustness: Zhu et al. (2023a) propose PromptBench, assessing LLM robustness through adversarial prompts at character, word, sentence, and semantic levels.
  • Task Robustness: Wang et al. (2023b) evaluate ChatGPT’s robustness across NLP tasks like translation, QA, text classification, and NLI.
  • Alignment Robustness: Ensuring alignment with human values is essential. “Jailbreak” methods are used to test LLMs for generating harmful or unsafe content, enhancing alignment robustness.
Risk evaluation of LLMs

Risk Evaluation

It’s crucial to develop advanced evaluations to handle catastrophic behaviors and tendencies of LLMs. This progress focuses on two aspects:

  1. Evaluating LLMs by discovering their behaviors, and assessing their consistency in answering questions and making decisions.
  2. Evaluating LLMs by interacting with the real environment, testing their ability to solve complex tasks by imitating human behaviors.

Evaluation of Specialized LLMs

  1. Biology and Medicine: Medical Exam, Application Scenarios, Humans
  2. Education: Teaching, Learning
  3. Legislation: Legislation Exam, Logical Reasoning
  4. Computer Science: Code Generation Evaluation, Programming Assistance Evaluation
  5. Finance: Financial Application, Evaluating GPT

Conclusion

Categorizing evaluation into knowledge and capability assessment, alignment evaluation, and safety evaluation provides a comprehensive framework for understanding LLM performance and potential risks. Benchmarking LLMs across diverse tasks aids in identifying areas of excellence and improvement.

Ethical alignment, bias mitigation, toxicity handling, and truthfulness verification are critical aspects of alignment evaluation. Safety evaluation, encompassing robustness and risk assessment, ensures responsible and ethical deployment, guarding against potential harms to users and society.

Specialized evaluations tailored to specific domains further enhance our understanding of LLM performance and applicability. By conducting thorough evaluations, we can maximize the benefits of LLMs while mitigating risks, ensuring their responsible integration into various real-world applications.

Abhishek Kumar 31 May, 2024

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