Geometric Interpretation of Transformers; Survey of Hallucination in LLM; LLama 2 13B vs Mistral 7B LLM; Growth Zone; and More

Editor's Paper Recommendations

Traveling Words: A Geometric Interpretation of Transformers : Transformers have significantly advanced the field of natural language processing, but comprehending their internal mechanisms remains a challenge. In this paper, we introduce a novel geometric perspective that elucidates the inner mechanisms of transformer operations. Our primary contribution is illustrating how layer normalization confines the latent features to a hyper-sphere, subsequently enabling attention to mold the semantic representation of words on this surface. This geometric viewpoint seamlessly connects established properties such as iterative refinement and contextual embeddings. We validate our insights by probing a pre-trained 124M GPT-2 model. Our findings reveal clear query-key attention patterns in early layers and build upon prior observations regarding the subject-specific nature of attention heads at deeper layers. Harnessing these geometric insights, we present an intuitive understanding of transformers, depicting them as processes that model the trajectory of word particles along the hyper-sphere.

Beyond Static Datasets: A Deep Interaction Approach to LLM Evaluation : Large Language Models (LLMs) have progressed in various real-world tasks, stimulating requirements for evaluating LLMs. Existing LLM evaluation methods are mainly supervised signal-based, which depends on static datasets and cannot assess the ability of LLMs in dynamic real-world scenarios where deep interaction widely exists. Other LLM evaluation methods are human-based, costly, time-consuming, and incapable of large-scale evaluation of LLMs. We propose a novel Deep Interaction-based LLM evaluation framework to address the issues above. In our proposed framework, LLMs' performances in real-world domains can be evaluated from their deep interaction with other LLMs in elaborately designed evaluation tasks. Furthermore, our proposed framework is a general evaluation method that can be applied to real-world tasks, such as machine translation and code generation. We demonstrate the effectiveness of our proposed method through extensive experiments on four elaborately designed evaluation tasks.

A Survey of Hallucination in Large Foundation Models : Hallucination in a foundation model (FM) refers to generating content that strays from factual reality or includes fabricated information. This survey paper provides an extensive overview of recent efforts that aim to identify, elucidate, and tackle the problem of hallucination, focusing on ``Large'' Foundation Models (LFMs). The paper classifies various types of hallucination phenomena specific to LFMs and establishes evaluation criteria for assessing the extent of hallucination. It also examines existing strategies for mitigating hallucination in LFMs and discusses potential directions for future research in this area. Essentially, the paper offers a comprehensive examination of the challenges and solutions related to hallucination in LFMs.

Industry Insights

• Researchers from Yale and Google Introduce HyperAttention: An Approximate Attention Mechanism Accelerating Large Language Models for Efficient Long-Range Sequence Processing.
• LLama 2 13B vs. Mistral 7B LLM models compared.
• Meet PIXART-α: A Transformer-Based T2I Diffusion Model Whose Image Generation Quality is Competitive with State-of-the-Art Image Generators

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Growth Zone

• Don’t Give Up on a Great Idea Just Because It Seems Obvious
• 4 Ways to Get More Done in Less Time