GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models

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Paper Summary

Conflicts of Interest

Identified Weaknesses

Rating Explanation

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Paper Summary

Paperzilla title

GLM-4.5: A Multi-Talented Language Model, But Hold the GPT-4 Comparison

This paper introduces GLM-4.5, a large language model designed to excel at reasoning, coding, and controlling external tools. Automated evaluations show promising results, but the paper lacks a head-to-head comparison against GPT-4 and has limited independent human evaluation. The model uses a Mixture-of-Experts architecture and claims better parameter efficiency than some competitors.

Possible Conflicts of Interest

The authors are affiliated with Zhipu AI and Tsinghua University, which have a vested interest in the success of the GLM-4.5 model.

Identified Weaknesses

Missing Key Baseline Comparison

The paper lacks a comparison against GPT-4, likely the strongest baseline. While it compares against some other commercial models, the absence of GPT-4 makes it hard to assess how truly strong GLM-4.5 is.

Limited Independent Human Evaluation

While the paper presents many automated evaluations, there's limited independent human evaluation of the model's outputs. More human evaluation would provide a better sense of quality and areas for improvement.

Missing Information on Training Compute

The paper states that GLM-4.5 has fewer parameters than some competitors but doesn't discuss training compute requirements. Parameter counts alone are an incomplete picture of resource usage and model development costs.

Rating Explanation

The paper presents a strong large language model with good performance across diverse tasks. However, the absence of a comparison against GPT-4 and limited independent human evaluation hold it back from a top rating. The reported performance suggests it represents a valuable contribution to open-source LLMs.

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