An In-depth Look at Gemini's Language Abilities
Repo for the paper An In-depth Look at Gemini's Language Abilities by CMU, Zeno, and BerriAI LiteLLM
In this paper, we do an in-depth exploration of Google Gemini's language abilities, making two contributions:
- We provide a third-party, objective comparison of the abilities of the OpenAI GPT and Google Gemini models with reproducible code and fully transparent results.
- we take a closer look at the results, identifying areas where one of the two model classes excels.
Results
We perform this analysis over 10 datasets testing a variety of language abilities, including reasoning, answering knowledge-based questions, solving math problems, translating between languages, generating code, and acting as instruction-following agents. From this analysis, we find that (as of this writing on December 18th, 2023):
- Gemini's Pro model achieved comparable but slightly inferior accuracy compared to the current version of OpenAI's GPT 3.5 Turbo for all English tasks, but superior ability to translate into other languages.
- Gemini fails in mathematical reasoning with many digits, and is sensitive to multiple-choice answer ordering, and others.
- Gemini demonstrates comparably high performance in areas such as generation into non-English languages, handling longer and more complex reasoning chains, and word sorting/rearrangement problems.
The overall results table can be found below:
| Task | Dataset | Gemini Pro | GPT 3.5 Turbo | GPT 4 Turbo | Mixtral |
|---|---|---|---|---|---|
| Knowledge-based QA | MMLU (5-shot) | 65.22 | 67.75 | 80.48 | 68.81 |
| MMLU (CoT) | 62.09 | 70.07 | 78.95 | 59.57 | |
| Reasoning | BIG-Bench-Hard | 67.53 | 71.02 | 83.90 | 60.76 |
| Mathematics | GSM8K | 76.42 | 78.01 | 92.72 | 71.65 |
| SVAMP | 81.10 | 82.30 | 92.60 | 81.60 | |
| ASDIV | 85.31 | 89.07 | 92.75 | 83.16 | |
| MAWPS | 96.50 | 98.00 | 98.67 | 96.00 | |
| Code Generation | HumanEval | 59.76 | 74.39 | 76.83 | 45.12 |
| ODEX | 39.86 | 52.62 | 45.79 | 40.55 | |
| Machine Translation | FLORES (5-shot) Unblocked | 56.14 | 55.78 | 57.15 | 44.27 |
| FLORES (5-shot) All | 22.83 | 43.12 | 51.63 | 33.45 | |
| Web Agents | WebArena | 7.12 | 8.87 | 14.90 | 1.39 |
You can find more details on results from each task, and comprehensive analysis at each of the below links:
- Knowledge-based QA (MMLU)
- Reasoning (BIG-Bench Hard)
- Mathematics (GSM8K, SVAMP, ASDIV, MAWPS)
- Code Generation (HumanEval, ODEX)
- Machine Translation (FLORES)
- Web Navigation Agents (WebArena)
File Structure
/outputs/{dataset}/{model}: contains the outputs of the systems, separated by dataset and model/benchmarking/{dataset}: contains the code for benchmarking, separated by dataset/visualization: contains the code for visualization, possibly separated by task type
Setup
Create a .env file in the root of the repository with your Zeno API key:
ZENO_API_KEY=your_api_key
This is loaded by dotenv in the visualization files.