Update papers.en.mdx

pages/papers.en.mdx

@@ -23,6 +23,7 @@ The following are the latest papers (sorted by release date) on prompt engineeri
 
 ## Approaches
 
+- [Large Language Models as Analogical Reasoners](https://arxiv.org/abs/2310.01714) (October 2023)
 - [Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RL](https://arxiv.org/abs/2309.06653) (September 2023)
 - [Chain-of-Verification Reduces Hallucination in Large Language Models](https://arxiv.org/abs/2309.11495) (September 2023)
 - [Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers](https://arxiv.org/abs/2309.08532) (September 2023)
@@ -175,6 +176,21 @@ The following are the latest papers (sorted by release date) on prompt engineeri
 
 ## Applications
 
+- [PromptRE: Weakly-Supervised Document-Level Relation Extraction via Prompting-Based Data Programming](https://arxiv.org/abs/2310.09265) (October 2023)
+- [Prompting Large Language Models with Chain-of-Thought for Few-Shot Knowledge Base Question Generation](https://arxiv.org/abs/2310.08395) (October 2023)
+- [Who Wrote it and Why? Prompting Large-Language Models for Authorship Verification](https://arxiv.org/abs/2310.08123) (October 2023)
+- [Promptor: A Conversational and Autonomous Prompt Generation Agent for Intelligent Text Entry Techniques](https://arxiv.org/abs/2310.08101) (October 2023)
+- [Thought Propagation: An Analogical Approach to Complex Reasoning with Large Language Models](https://arxiv.org/abs/2310.03965) (October 2023)
+- [From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting](https://arxiv.org/abs/2309.04269) (September 2023)
+- [Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation](https://arxiv.org/abs/2310.02304) (October 2023)
+- [Think before you speak: Training Language Models With Pause Tokens](https://arxiv.org/abs/2310.02226) (October 2023)
+- [(Dynamic) Prompting might be all you need to repair Compressed LLMs](https://arxiv.org/abs/2310.00867) (October 2023)
+- [In-Context Learning in Large Language Models: A Neuroscience-inspired Analysis of Representations](https://arxiv.org/abs/2310.00313) (September 2023)
+- [Understanding In-Context Learning from Repetitions](https://arxiv.org/abs/2310.00297) (September 2023)
+- [Investigating the Efficacy of Large Language Models in Reflective Assessment Methods through Chain of Thoughts Prompting](https://arxiv.org/abs/2310.00272) (September 2023)
+- [Automatic Prompt Rewriting for Personalized Text Generation](https://arxiv.org/abs/2310.00152) (September 2023)
+- [Efficient Streaming Language Models with Attention Sinks](https://arxiv.org/abs/2309.17453) (September 2023)
+- [The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)](https://arxiv.org/abs/2309.17421) (September 2023)
 - [Graph Neural Prompting with Large Language Models](https://arxiv.org/abs/2309.15427) (September 2023)
 - [Large Language Model Alignment: A Survey](https://arxiv.org/abs/2309.15025) (September 2023)
 - [Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logic](https://arxiv.org/abs/2309.13339) (September 2023)