Banishing LLM Hallucinations Requires Rethinking Generalization (2406.17642v1)

Abstract: Despite their powerful chat, coding, and reasoning abilities, 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.

Rethinking Generalization to Mitigate LLM Hallucinations

The paper "Banishing LLM Hallucinations Requires Rethinking Generalization" explores the persistent issue of hallucinations in LLMs and proposes an alternative approach to understanding and mitigating these hallucinations. The authors contend that traditional views and solutions regarding generalization in LLMs are inadequate for addressing the hallucination problem.

Key Findings and Methodology

The authors challenge the conventional wisdom that hallucinations stem from a trade-off between creativity and factuality, suggesting that merely grounding LLMs in external knowledge is insufficient. Through extensive experiments, they illustrate that LLMs augmented with a Mixture of Memory Experts (MoME) demonstrate significant memorization capacity, even for random datasets. Notably, LLMs pre-trained to predict the next token are shown to hallucinate when training loss surpasses a specific threshold—common when dealing with large-scale internet data.

Significantly, their investigation reveals that LLMs can memorize random labels yet retain their capacity to generalize effectively. This is evidenced by experiments showing zero finetuning error when LLMs, trained with random character string answers, still succeed in correctly answering unrelated questions. Through these methods, they ascertain that memorizing key facts necessitates about 100 times more Stochastic Gradient Descent (SGD) steps than usual.

Computational and Systematic Insights

The research highlights the extensive computational demands for training models to completely eradicate hallucinations. For instance, eliminating hallucinations in a Llama 3 model would require an immense 3.43 yottaFLOPs, equating to an enormous energy and financial expenditure. Despite the computational challenge, this suggests the feasibility of non-hallucinating LLMs, albeit at a significant scale and cost which may not be feasible with current computational infrastructure.

Implications and Proposed Model

The paper outlines that typical causes cited for hallucinations, such as missing, outdated, or biased data, conflicting information, and decoder sampling noise, are not sufficient explanations within the experimental context. As a solution, the authors propose Lamini Memory Tuning, alongside the introduction of a new model architecture, Lamini-1. This model forsakes traditional transformer architectures, relying instead on a massive MoME approach for knowledge retrieval. With millions of dynamically retrieved memory experts, Lamini-1 architecture aims for superior factual recall performance with considerably reduced training time—claiming state-of-the-art levels with just one hour of training on 8 MI300X GPUs.

Broader Implications and Future Directions

The insights offered in this paper suggest a paradigm shift in how we approach the design and training of LLMs. The notion that models require substantially more tailored training to improve factual accuracy beyond just creative language generation extends the frontier for AI model training. Moreover, considerations for developing robust metrics to evaluate fact memorization and recall capabilities in LLMs are crucial.

Future developments in AI, particularly in the high-performance computing domain, will play a significant role in the feasibility of these approaches. Beyond addressing hallucinations, they could foster the development of LLMs that are more reliable and valuable for applications demanding high precision and factual accuracy. The research invites further exploration into innovative architectures and training algorithms that leverage LLM capabilities for optimized memorization without exacerbating generalization errors.