Subscribe to Toloka News
Subscribe to Toloka News
As advanced Natural Language Processing (NLP) models, Large Language Models (LLMs) are the talk of the town these days given their uncanny ability to generate human-like text by leveraging large quantities of pre-existing language data and multifaceted machine learning algorithms. Think GPT-3, BERT, and XLNet. However, as we become increasingly dependent on these models, finding ways to effectively evaluate them and safeguard against potential threats is more imperative than ever.
In this article, we cover in detail the importance of evaluating large language models to improve their performance and why human insight is critical to this process. While much of the information out there indicates that incorporating human input can be costly, with the help of Toloka’s crowd contributors, it can be done much faster and in a more cost-effective way.
As large language models become more deeply embedded in machine learning, having thorough evaluation methods and comprehensive regulation frameworks in place can help ensure that AI is serving everyone’s best interests.
The number of large language models on the market has increased exponentially, but there’s no set standard by which to evaluate them. As a result, it’s becoming significantly more challenging to both evaluate models and determine which one is the best — and safest. That’s why we need a reliable, comprehensive evaluation framework by which to precisely assess the quality of large language models. A standardized framework will not only help regulators evaluate the safety, accuracy, and reliability of a model, but it will also hold developers accountable when releasing these models — rather than simply slapping a disclaimer on their product. Moreover, large language model users will be able to see whether they need to fine-tune a model on supplementary data.
It’s vital to evaluate large language models to assess their quality and usefulness in different applications. We’ve outlined some real-life examples of why it’s important to evaluate large language models:
A company must choose between several models for its foundational enterprise generative model based on relevance, accuracy, fluency, and more. The given LLMs must be assessed according to their ability to generate text and respond to input.
A company selects and fine-tunes a model for better performance on industry-specific tasks by carrying out a comparative evaluation of LLMs to choose the one that best suits their needs.
Detecting and preventing bias
By having a holistic evaluation framework, companies can work to detect and eliminate bias found in large language model outputs and training data to create fairer outcomes.
Building user trust
Evaluating user feedback and trust in the answers provided by LLMs is paramount to building reliable systems that are aligned with user expectations and societal norms.
Evaluating an LLM’s performance includes measuring features such as language fluency, coherence, context comprehension, speech recognition, fact-based accuracy, and its aptitude to generate pertinent and significant responses. Metrics like perplexity, BLEU score, and human evaluations can be used to assess and compare language model performance.
This process requires a closer look at the following factors:
By ensuring the points above are taken into account in our evaluation of LLMs, we can maximize the potential of these formidable models.
Here are five of the most common methods used to evaluate LLMs today:
Moreover, zero-shot evaluation can now be used for large language models. A cost-effective and well-known way to assess the performance of LLMs, zero-shot evaluation measures the probability that a trained model will produce a particular set of tokens without needing any labeled training data.
Multiple frameworks have been developed to evaluate LLMs, such as Big Bench, GLUE Benchmark, SuperGLUE Benchmark, and others, each of them focusing on its own domain. For example, Big Bench takes into account generalization capabilities when evaluating LLMs, GLUE Benchmark considers grammar, paraphrasing, text similarity, inference, and several other factors, while SuperGLUE Benchmark looks at Natural Language Understanding, reasoning, reading comprehension, and how well an LLM understands complex sentences beyond training data, among other considerations. See the table below for an overview of the current evaluation frameworks and the factors they consider when evaluating LLMs.
|Grammar, paraphrasing, text similarity, inference, textual entailment, resolving pronoun references
|Natural Language Understanding, reasoning, understanding complex sentences beyond training data, coherent and well-formed Natural Language Generation, dialogue with humans, common sense reasoning, information retrieval, reading comprehension
|OpenAI Moderation API
|Filtering out harmful or unsafe content
|Language understanding across various tasks and domains
|EleutherAI LM Eval
|Few-shot evaluation and performance across a wide range of tasks with minimal fine-tuning
|Accuracy, diversity, consistency, robustness, transferability, efficiency, fairness of text generated
|Adversarial NLI (ANLI)
|Robustness, generalization, coherent explanations for inferences, consistency of reasoning across similar examples, efficiency of resource usage (memory usage, inference time, and training time)
|LIT (Language Interpretability Tool)
|Platform to evaluate user defined metrics - insights into their strengths, weaknesses, and potential biases
|Accuracy, F1 score, perplexity, human evaluation on relevance, fluency, and coherence, speed and resource usage, robustness, generalization
|Understanding a text passage and answering a series of interconnected questions that appear in a conversation
|Long-term understanding by predicting the last word of a passage
|Logical reasoning abilities
|Understanding relationships between sentences across genres
|Reading comprehension tasks
Apart from OpenAI’s Moderation API, most of the ones available today don’t take safety into account as a factor for their evaluation results. And not one of them is inclusive enough to be self-reliant.
There are still quite a lot of factors to overcome when it comes to evaluating large language models. Some of the most frequent challenges include:
The development of large language models has forever changed the field of Natural Language Processing and the fulfillment of natural language tasks, but we still need a comprehensive and standardized evaluation framework to assess the quality, accuracy, and reliability of these models. Current frameworks offer valuable insights, but they lack a uniform, more comprehensive evaluation approach and don’t factor in safety concerns.
Before we dive into the best practices for evaluating LLMs, let’s take a look at the five steps required for setting up an LLM:
As mentioned above, a dependable evaluation framework should factor in authenticity, speed, context, and more, which will help developers release LLMs responsibly. Collaboration among key stakeholders and regulators is key to creating a reliable and comprehensive evaluation framework.
In the meantime, we've outlined several best practices for circumventing some of the challenges of evaluating large language models:
Human input plays a key role in making large language models safer and more reliable. One of the main ways in which human input is valuable is in the establishment and enforcement of guardrails.
While AI has the potential to simplify and improve our lives in many ways, it also comes with inherent risks and some real dangers, which cannot be ignored. That’s where guardrails come into play. In short, guardrails are essentially defenses — policies, strategies, mechanisms — that are put into place to ensure the ethical and responsible application of AI-based technologies. Guardrails are designed to preclude misuse, defend user privacy, and encourage transparency and equality. Without them, it could be a world gone mad!
With technology evolving at the speed of light, let’s take a closer look at how guardrails work and why they’re so important in effectively managing AI.
Given the nature of the risks posed by AI — such as ethical and privacy concerns, bias and prejudice, as well as environmental and computational costs — guardrails work to prevent many of these challenges by establishing a set of guidelines and controls. Imagine if someone were to take unauthorized medical advice from an AI system. The results could be disastrous, and even deadly! That’s why building controls into AI systems that determine acceptable behavior and responses is so critical. We need them to ensure that AI technologies work within our societal norms and standards.
Below are some examples of guardrails:
Transparency and accountability go hand-in-hand when it comes to the importance of guardrails in AI systems. You need transparency to ensure that responses generated by these systems can be explained and mistakes can be found and fixed. Guardrails can also ensure that human input is taken into account in regard to important decisions in areas like medical care and self-driving cars. Basically, the point of guardrails is to make sure that AI technologies are being used for the betterment of humanity and are serving us in helpful and positive ways without posing significant risks, harm, or danger.
As AI systems continue to develop, they are endowed with an ever-increasing amount of responsibility and tasked with more important decisions. That means they have the power to make a greater impact, but also to inflict more damage if misused.
Here are some of the key benefits of guardrails:
Not only do guardrails ensure the safe, responsible, and ethical use of AI technologies, but they can also be used to detect and eliminate bias, promote openness and accountability, comply with local laws, and most importantly, implement human oversight to ensure that AI doesn’t replace human decision-making.
By making sure that humans are involved in key decision-making processes, guardrails can help guarantee that AI technologies stay under our control and that their outputs follow our societal norms and values. That’s why keeping humans in the loop (HITL) — where both humans and machines work together to create machine learning models — is imperative to making sure that AI doesn’t go off the rails. HITL ensures that people are involved at every stage of the algorithm cycle, including training, tuning, and testing. As a result, AI outputs become safer and more reliable.
Implementing guardrails is no easy task, however, there are a couple of ways to overcome the technical complexities involved in this process, namely:
What’s more, the actual process of setting up guardrails poses a challenge in itself. You need predefined roles and responsibilities within your company, along with board oversight and tools for recording and surveying AI system outputs, not to mention legal and regulatory compliance measures. All this requires continual monitoring, assessment, and tweaking — it’s by no means an easy or static process! As AI evolves, so too will the guardrails needed to keep us safe.
Human evaluation can be time-consuming and expensive, particularly when it comes to large-scale evaluations. That’s where crowdsourcing comes into play.
As mentioned above, crowdsourcing can be particularly helpful in improving human evaluation by obtaining diverse feedback on large projects. Human evaluation via crowdsourcing involves crowd participants evaluating the output or performance of a large language model within a given context. By leveraging the capabilities of millions of crowd contributors, you can obtain qualitative feedback and identify subtle nuances that traditional LLM-based evaluation might otherwise miss.
Overall, crowdsourcing can be a great solution for evaluating large language model projects. At Toloka, we help clients solve real-life business challenges and offer best-in-class solutions for fine-tuning and evaluating language models. We help clients monitor quality in production applications and obtain unbiased feedback for model improvement using the collective power of the crowd.
Large language models are revolutionizing many sectors from medicine and banking to academia and the media. As these models become increasingly sophisticated, regulatory oversight is critical. Data privacy, accountability, transparency, and elimination of bias all need to be taken into account. LLMs must also be able to substantiate and provide sources for their outputs and decisions in order to build public trust. As such, public involvement can be an effective way to ensure that these systems comply with societal norms and expectations.
To unlock the full potential of such a language model, we need to incorporate suitable evaluation methods that cover accuracy, fairness, robustness, explainability, and generalization. That way, we can make the most of their strengths and successfully address their weaknesses.
We invite you to read through our blog to learn more about large language models and how Toloka can help in their evaluation process.