Update README.md

README.md

@@ -76,7 +76,7 @@ Here is the weekly series:
 | 7) **Pricipled Instructions Are All You Need** - introduces 26 guiding principles designed to streamline the process of querying and prompting large language models; applies these principles to conduct extensive experiments on LLaMA-1/2 (7B, 13B and 70B), GPT-3.5/4 to verify their effectiveness on instructions and prompts design. | [Paper](https://arxiv.org/abs/2312.16171v1), [Tweet](https://x.com/_akhaliq/status/1739857456161759455?s=20) |
 | 8) **A Survey of Reasoning with Foundation Models** - provides a comprehensive survey of seminal foundational models for reasoning, highlighting the latest advancements in various reasoning tasks, methods, benchmarks, and potential future directions; also discusses how other developments like multimodal learning, autonomous agents, and super alignment accelerate and extend reasoning research. | [Paper](https://arxiv.org/abs/2312.11562v4), [Tweet](https://x.com/omarsar0/status/1740729489661874632?s=20) |
 | 9) **Making LLMs Better at Dense Retrieval** - proposes LLaRA which adapts an LLM for dense retrieval; it consists of two pretext tasks: EBAE (Embedding-Based Auto-Encoding) and EBAR (Embedding-Based Auto-Regression), where the text embeddings from LLM are used to reconstruct the tokens for the input sentence and predict the tokens for the next sentence, respectively; a LLaMa-2-7B was improved on benchmarks like MSMARCO and BEIR. | [Paper](https://arxiv.org/abs/2312.15503v1) |
-| 10) **Gemini vs GPT-4V - provides a comprehensive preliminary comparison and combination of vision-language models like Gemini and GPT-4V through several qualitative cases; finds that GPT-4V is precise and succinct in responses, while Gemini excels in providing detailed, expansive answers accompanied by relevant imagery and links.  | [Paper](https://arxiv.org/abs/2312.15011v1), [Tweet](https://x.com/omarsar0/status/1741177994377330895?s=20) |
+| 10) **Gemini vs GPT-4V** - provides a comprehensive preliminary comparison and combination of vision-language models like Gemini and GPT-4V through several qualitative cases; finds that GPT-4V is precise and succinct in responses, while Gemini excels in providing detailed, expansive answers accompanied by relevant imagery and links.  | [Paper](https://arxiv.org/abs/2312.15011v1), [Tweet](https://x.com/omarsar0/status/1741177994377330895?s=20) |
 
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 ## Top ML Papers of the Week (December 18 - December 24)