Enhance README "Some other examples"

Modified so that the "benchmark on a bunch of different tasks" sends the reader to a README.md file explaining the benchmarking process, instead of just dropping them into the directory on GitHub.

Added a link to ArXiV for "The Unreasonable Effectiveness..." article, so that now the interested reader can jump directly to ArXiV instead of having to go through the Register article. The link to the Register article *is* retained, though.

Author: rpgoldman

README.md

@@ -142,12 +142,12 @@ You can find other examples tweeted by [@lateinteraction](https://twitter.com/la
   - [Generating Tweets for QA, by Arnav Singhvi](https://colab.research.google.com/github/stanfordnlp/dspy/blob/main/examples/tweets/tweets_assertions.ipynb)
 - [Compiling LCEL runnables from LangChain in DSPy](https://github.com/stanfordnlp/dspy/blob/main/examples/tweets/compiling_langchain.ipynb)
 - [AI feedback, or writing LM-based metrics in DSPy](https://github.com/stanfordnlp/dspy/blob/main/examples/tweets/tweet_metric.py)
-- [DSPy Optimizers Benchmark on a bunch of different tasks, by Michael Ryan](https://github.com/stanfordnlp/dspy/tree/main/testing/tasks)
+- [DSPy Optimizers Benchmark on a bunch of different tasks, by Michael Ryan](https://github.com/stanfordnlp/dspy/tree/main/testing/README.md)
 - [Indian Languages NLI with gains due to compiling by Saiful Haq](https://github.com/saifulhaq95/DSPy-Indic/blob/main/indicxlni.ipynb)
 - [Sophisticated Extreme Multi-Class Classification, IReRa, by Karel D’Oosterlinck](https://github.com/KarelDO/xmc.dspy)
 - [DSPy on BIG-Bench Hard Example, by Chris Levy](https://drchrislevy.github.io/posts/dspy/dspy.html)
 - [Using Ollama with DSPy for Mistral (quantized) by @jrknox1977](https://gist.github.com/jrknox1977/78c17e492b5a75ee5bbaf9673aee4641)
-- [Using DSPy, "The Unreasonable Effectiveness of Eccentric Automatic Prompts" (paper) by VMware's Rick Battle & Teja Gollapudi, and interview at TheRegister](https://www.theregister.com/2024/02/22/prompt_engineering_ai_models/)
+- [Using DSPy, "The Unreasonable Effectiveness of Eccentric Automatic Prompts" (paper) by VMware's Rick Battle & Teja Gollapudi](https://arxiv.org/abs/2402.10949), and [interview at TheRegister](https://www.theregister.com/2024/02/22/prompt_engineering_ai_models/)
 - [Optimizing Performance of Open Source LM for Text-to-SQL using DSPy and vLLM, by Juan Ovalle](https://github.com/jjovalle99/DSPy-Text2SQL)
 - Typed DSPy (contributed by [@normal-computing](https://github.com/normal-computing))
   - [Using DSPy to train Gpt 3.5 on HumanEval by Thomas Ahle](https://github.com/stanfordnlp/dspy/blob/main/examples/functional/functional.ipynb)