## Bar Chart: Indexical 'I'
### Overview
This image displays a 2x2 grid of bar charts evaluating the "Model Accuracy" of four different Large Language Models (LLMs) regarding the linguistic concept of the "Indexical 'I'". The models evaluated are Claude 3.5 Sonnet, Deepseek V3, Gemini 1.5 pro, and GPT-4o. The accuracy is measured across two sentence types: "Non-quoted" and "Quoted". Each category contains two bars (Light Blue and Dark Blue) with associated error bars, indicating performance variance.
### Components/Axes
* **Y-Axis:** "Model Accuracy", scaled from 0.00 to 1.00. This axis is shared across the left-hand charts (Claude 3.5 Sonnet and Gemini 1.5 pro).
* **X-Axis:** "Sentence Type", categorized into "Non-quoted" and "Quoted". This axis is shared across the bottom charts (Gemini 1.5 pro and GPT-4o).
* **Data Series:** Two distinct bar types are used in each category:
* **Light Blue Bar:** Positioned on the left within each category.
* **Dark Blue Bar:** Positioned on the right within each category.
* **Error Bars:** Vertical black lines with horizontal caps are present on all bars, representing uncertainty or standard deviation.
* **Grid Layout:**
* **Top-Left:** Claude 3.5 Sonnet
* **Top-Right:** Deepseek V3
* **Bottom-Left:** Gemini 1.5 pro
* **Bottom-Right:** GPT-4o
### Detailed Analysis
#### 1. Claude 3.5 Sonnet (Top-Left)
* **Non-quoted:** Light Blue bar at 1.00; Dark Blue bar at 0.98.
* **Quoted:** Light Blue bar at 0.82; Dark Blue bar at 0.96.
* **Trend:** High accuracy across all conditions, with a slight dip in the Light Blue bar for "Quoted" sentences.
#### 2. Deepseek V3 (Top-Right)
* **Non-quoted:** Light Blue bar at 1.00; Dark Blue bar at 1.00.
* **Quoted:** Light Blue bar at 0.17; Dark Blue bar at 0.76.
* **Trend:** This model shows a drastic performance drop in the "Quoted" category, particularly for the Light Blue bar, which falls to 0.17.
#### 3. Gemini 1.5 pro (Bottom-Left)
* **Non-quoted:** Light Blue bar at 1.00; Dark Blue bar at 0.99.
* **Quoted:** Light Blue bar at 0.91; Dark Blue bar at 0.98.
* **Trend:** Very high consistency. There is a minor decrease in the Light Blue bar for "Quoted" sentences, but it remains above 0.90.
#### 4. GPT-4o (Bottom-Right)
* **Non-quoted:** Light Blue bar at 1.00; Dark Blue bar at 0.98.
* **Quoted:** Light Blue bar at 0.94; Dark Blue bar at 0.98.
* **Trend:** Extremely stable performance. The model maintains high accuracy across all conditions, with the Dark Blue bar remaining perfectly flat at 0.98.
### Key Observations
* **Universal Baseline:** All four models achieve near-perfect accuracy (1.00) on "Non-quoted" sentences for the Light Blue bar condition.
* **The "Quoted" Challenge:** The "Quoted" condition introduces complexity that causes performance degradation in all models, though the severity varies significantly.
* **Deepseek V3 Anomaly:** Deepseek V3 is a significant outlier. While it performs perfectly on "Non-quoted" sentences, its ability to handle the Light Blue condition in "Quoted" sentences collapses (0.17 accuracy).
* **Dark Blue Robustness:** In almost every instance where the Light Blue bar performance drops (Claude, Deepseek, Gemini), the Dark Blue bar maintains a higher accuracy, suggesting the Dark Blue condition represents a more robust or "smarter" reasoning path for the models.
### Interpretation
The data demonstrates how LLMs handle "Indexical 'I'"—a linguistic challenge where the word "I" must be resolved to the correct speaker. In "Non-quoted" sentences, the reference is straightforward. In "Quoted" sentences, the models must perform a "Theory of Mind" shift, recognizing that "I" refers to the person being quoted, not the current speaker.
The consistent high performance of the Dark Blue bars suggests that whatever method or prompt structure they represent is highly effective at maintaining reference tracking even in complex quoted speech. Conversely, the Light Blue bars likely represent a more literal or "surface-level" processing method that is prone to failure when the context requires shifting the referent of "I". Deepseek V3's failure in the Light Blue/Quoted condition (0.17) suggests a specific, brittle failure mode in its reasoning architecture when dealing with reported speech.
