A recent Harvard Business Review study of 15,000 interactions across frontier models found a blunt problem for enterprise architecture. Models like ChatGPT, Claude, and Gemini are built to sound helpful, even when the helpful answer is wrong.
They do not reliably analyze your business context. They repeat popular internet patterns, dress them up as strategy, and favour agreement over accuracy. — Read More
Tag Archives: Accuracy
Agent Evaluation: A Detailed Guide
Evaluation is one of the most important research areas for large language models (LLMs). Recently, patterns in LLM usage and evaluation have drastically changed. Whereas we previously evaluated LLMs using benchmarks composed of static questions or short conversations, we now have agent systems that operate over long time horizons and interact with the environment. Agents are difficult to properly evaluate due to their complexity and autonomy. To accurately measure the capabilities of an agent system, we must build harnesses that are realistic and capable of testing agents similarly to how they are used in practice. Building such evaluation capabilities is now more important than ever due to the growing adoption of agents in high-stakes applications like coding and medicine.
This overview will provide a detailed guide of how current agent systems are evaluated. We will begin by developing an understanding of agents in general, covering everything from basic concepts to multi-agent systems. We will then provide a clear framework for the agent evaluation process based upon common patterns observed in practice. Building upon this knowledge, we will end with several case studies of recent agent benchmarks and provide a roadmap that outlines how to build our own agent evaluation by applying similar concepts. Although evaluation is time-consuming and difficult, learning how to properly evaluate agents is incredibly valuable. By rigorously measuring performance and not relying on anecdotal checks, we can rapidly improve agent capabilities. — Read More
How LLMs Distort Our Written Language
LLMs are used by over a billion people globally, and the most frequent use case is to assist with writing. LLMs can provide a huge efficiency boost, but are they actually writing what we want?
Many users recognize the “feel” of LLM prose, but few people realize the extent to which LLMs distort the meaning of writing. We find this across three datasets: a human user study, a dataset of human argumentative essays, and reviews from a top machine learning conference. — Read More
Training language models to be warm can reduce accuracy and increase sycophancy
Artificial intelligence developers are increasingly building language models with warm and friendly personas that millions of people now use for advice, therapy and companionship1. Here we show how this can create a significant trade-off: optimizing language models for warmth can undermine their performance, especially when users express vulnerability. We conducted controlled experiments on five different language models, training them to produce warmer responses, then evaluating them on consequential tasks. Warm models showed substantially higher error rates (+10 to +30 percentage points) than their original counterparts, promoting conspiracy theories, providing inaccurate factual information and offering incorrect medical advice. They were also significantly more likely to validate incorrect user beliefs, particularly when user messages expressed feelings of sadness. Importantly, these effects were consistent across different model architectures, and occurred despite preserved performance on standard tests, revealing systematic risks that standard testing practices may fail to detect. Our findings suggest that training artificial intelligence systems to be warm may come at a cost to accuracy, and that warmth and accuracy may not be independent by default. As these systems are deployed at an unprecedented scale and take on intimate roles in people’s lives, this trade-off warrants attention from developers, policymakers and users alike. — Read More
Banishing LLM Hallucinations Requires Rethinking Generalization
Despite their powerful chat, coding, and reasoning abilities, Large Language Models (LLMs) frequently hallucinate. Conventional wisdom suggests that hallucinations are a consequence of a balance between creativity and factuality, which can be mitigated, but not eliminated, by grounding the LLM in external knowledge sources. Through extensive systematic experiments, we show that these traditional approaches fail to explain why LLMs hallucinate in practice. Specifically, we show that LLMs augmented with a massive Mixture of Memory Experts (MoME) can easily memorize large datasets of random numbers. We corroborate these experimental findings with a theoretical construction showing that simple neural networks trained to predict the next token hallucinate when the training loss is above a threshold as it usually does in practice when training on internet scale data. We interpret our findings by comparing against traditional retrieval methods for mitigating hallucinations. We use our findings to design a first generation model for removing hallucinations — Lamini-1 — that stores facts in a massive mixture of millions of memory experts that are retrieved dynamically. — Read More
DeepMind’s SCoRe shows LLMs can use their internal knowledge to correct their mistakes
While large language models (LLMs) are becoming increasingly effective at complicated tasks, there are many cases where they can’t get the correct answer on the first try. This is why there is growing interest in enabling LLMs to spot and correct their mistakes, also known as “self-correction.” However, current attempts at self-correction are limited and have requirements that often cannot be met in real-world situations.
In a new paper, researchers at Google DeepMind introduce Self-Correction via Reinforcement Learning (SCoRe), a novel technique that significantly improves the self-correction capabilities of LLMs using only self-generated data. SCoRe can be a valuable tool for making LLMs more robust and reliable and opens new possibilities for enhancing their reasoning and problem-solving abilities. — Read More
Training Language Models to Self-Correct via Reinforcement Learning
Self-correction is a highly desirable capability of large language models (LLMs), yet it has consistently been found to be largely ineffective in modern LLMs. Existing approaches for training self-correction either require multiple models or rely on a more capable model or other forms of supervision. To this end, we develop a multi-turn online reinforcement learning (RL) approach, SCoRe, that significantly improves an LLM’s self-correction ability using entirely self-generated data. To build SCoRe, we first show that variants of supervised fine-tuning (SFT) on offline model-generated correction traces are insufficient for instilling self-correction behavior. In particular, we observe that training via SFT either suffers from a distribution mismatch between the training data and the model’s own responses or implicitly prefers only a certain mode of correction behavior that is often not effective at test time. SCoRe addresses these challenges by training under the model’s own distribution of self-generated correction traces and using appropriate regularization to steer the learning process into learning a self-correction strategy that is effective at test time as opposed to simply fitting high-reward responses for a given prompt. This regularization prescribes running a first phase of RL on a base model to generate a policy initialization that is less susceptible to collapse and then using a reward bonus to amplify self-correction during training. When applied to Gemini 1.0 Pro and 1.5 Flash models, we find that SCoRe achieves state-of-the-art self-correction performance, improving the base models’ self-correction by 15.6% and 9.1% respectively on the MATH and HumanEval benchmarks. — Read More
Galileo LLM Hallucination Index
Many enterprise teams have already successfully deployed LLMs in production, and many others have committed to deploying Generative AI products in 2024. However, for enterprise AI teams, the biggest hurdle to deploying production-ready Generative AI products remains the fear of model hallucinations – a catch-all phrase for when the model generates text that is incorrect or fabricated. There can be several reasons for this, such as a lack of the model’s capacity to memorize all of the information it was fed, training data errors, and outdated training data. — Read More
The Index
Why AI’s Tom Cruise problem means it is ‘doomed to fail’
LLMs’ ‘reversal curse’ leads it to fail at drawing relationships between simple facts. It’s a problem that could prove fatal
In 2021, linguist Emily Bender and computer scientist Timnit Gebru published a paper that described the then-nascent field of language models as one of “stochastic parrots”. A language model, they wrote, “is a system for haphazardly stitching together sequences of linguistic forms it has observed in its vast training data, according to probabilistic information about how they combine, but without any reference to meaning.”
… If a human learns the fact, “Valentina Tereshkova was the first woman to travel to space”, they can also correctly answer, “Who was the first woman to travel to space?” This is such a basic form of generalization that it seems trivial. Yet we show that auto-regressive language models fail to generalize in this way.
This is an instance of an ordering effect we call the Reversal Curse.
[R]esearchers “taught” a bunch of fake facts to large language models, and found time and again that they simply couldn’t do the base work of inferring the reverse. — Read More
Meta’s AI image generator can’t imagine an Asian man with a white woman
Have you ever seen an Asian person with a white person, whether that’s a mixed-race couple or two friends of different races? Seems pretty common to me — I have lots of white friends!
To Meta’s AI-powered image generator, apparently this is impossible to imagine. I tried dozens of times to create an image using prompts like “Asian man and Caucasian friend,” “Asian man and white wife,” and “Asian woman and Caucasian husband.” Only once was Meta’s image generator able to return an accurate image featuring the races I specified. — Read More
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