[Update] pdf links to the new structure of lecture_dl4nlp repo

content/chapters/08_decoding/08_01_intro.md

@@ -9,4 +9,4 @@ Here we introduce the concept of decoding. Given a prompt and a generative langu
 
 ### Lecture Slides
 
-{{< pdfjs file="https://github.com/slds-lmu/lecture_dl4nlp/blob/main/slides/chapter12-decoding/slides-121-intro.pdf" >}}
+{{< pdfjs file="https://github.com/slds-lmu/lecture_dl4nlp/blob/main/slides/chapter08-decoding/slides-81-intro.pdf" >}}

content/chapters/08_decoding/08_02_determ.md

@@ -8,7 +8,7 @@ Here we introduce two deterministic decoding strategies, greedy & beam search. B
 
 ### Lecture Slides
 
-{{< pdfjs file="https://github.com/slds-lmu/lecture_dl4nlp/blob/main/slides/chapter12-decoding/slides-122-determ.pdf" >}}
+{{< pdfjs file="https://github.com/slds-lmu/lecture_dl4nlp/blob/main/slides/chapter08-decoding/slides-82-determ.pdf" >}}
 
 ### Additional Resources 
 

content/chapters/08_decoding/08_03_sampling.md

@@ -9,7 +9,7 @@ In this chapter you will learn about more methods beyond simple deterministic de
 
 ### Lecture Slides
 
-{{< pdfjs file="https://github.com/slds-lmu/lecture_dl4nlp/blob/main/slides/chapter12-decoding/slides-123-sampling.pdf" >}}
+{{< pdfjs file="https://github.com/slds-lmu/lecture_dl4nlp/blob/main/slides/chapter08-decoding/slides-83-sampling.pdf" >}}
 
 ### References
 

content/chapters/08_decoding/08_04_hyper_param.md

@@ -9,7 +9,7 @@ In this chapter you will learn how to use the different decoding strategies in p
 
 ### Lecture Slides
 
-{{< pdfjs file="https://github.com/slds-lmu/lecture_dl4nlp/blob/main/slides/chapter12-decoding/slides-124-hyper-param.pdf" >}}
+{{< pdfjs file="https://github.com/slds-lmu/lecture_dl4nlp/blob/main/slides/chapter08-decoding/slides-84-hyper-param.pdf" >}}
 
 ### Additional Resources 
 

content/chapters/08_decoding/08_05_eval_metrics.md

@@ -9,7 +9,7 @@ Here we answer the question on how to evaluate the generated outputs in open end
 
 ### Lecture Slides
 
-{{< pdfjs file="https://github.com/slds-lmu/lecture_dl4nlp/blob/main/slides/chapter12-decoding/slides-125-eval_metrics.pdf" >}}
+{{< pdfjs file="https://github.com/slds-lmu/lecture_dl4nlp/blob/main/slides/chapter08-decoding/slides-85-eval_metrics.pdf" >}}
 
 ### References
 

content/chapters/09_llm/09_01_instruction_tuning.md

@@ -10,7 +10,7 @@ This approach involves fine-tuning LLMs on task-specific instructions or prompts
 
 ### Lecture Slides
 
-{{< pdfjs file="https://github.com/slds-lmu/lecture_dl4nlp/blob/main/slides/chapter08-llm/slides-81-instruction-tuning.pdf" >}}
+{{< pdfjs file="https://github.com/slds-lmu/lecture_dl4nlp/blob/main/slides/chapter09-llm/slides-91-instruction-tuning.pdf" >}}
 
 ### Additional Resources
 

content/chapters/09_llm/09_02_cot.md

@@ -8,7 +8,7 @@ Chain of thought (CoT) prompting [1] is a prompting method that encourage Large
 
 ### Lecture Slides
 
-{{< pdfjs file="https://github.com/slds-lmu/lecture_dl4nlp/blob/main/slides/chapter08-llm/slides-82-chain-of-thought.pdf" >}}
+{{< pdfjs file="https://github.com/slds-lmu/lecture_dl4nlp/blob/main/slides/chapter09-llm/slides-92-chain-of-thought.pdf" >}}
 
 ### References 
 

content/chapters/09_llm/09_03_emerging.md

@@ -8,7 +8,7 @@ Various researchers have reported that LLMs seem to have emergent abilities. The
 
 ### Lecture Slides
 
-{{< pdfjs file="https://github.com/slds-lmu/lecture_dl4nlp/blob/main/slides/chapter08-llm/slides-83-emergent-abilities.pdf" >}}
+{{< pdfjs file="https://github.com/slds-lmu/lecture_dl4nlp/blob/main/slides/chapter09-llm/slides-93-emergent-abilities.pdf" >}}
 
 ### Additional Resources
 

content/chapters/10_rlhf/_index.md

@@ -6,7 +6,7 @@ In the context of natural language processing (NLP), RLHF (Reinforcement Learnin
 
 ### Lecture Slides
 
-{{< pdfjs file="https://github.com/slds-lmu/lecture_dl4nlp/blob/main/slides/chapter09-rlhf/slides-91-rlhf.pdf" >}}
+{{< pdfjs file="https://github.com/slds-lmu/lecture_dl4nlp/blob/main/slides/chapter10-rlhf/slides-10-rlhf.pdf" >}}
 
 ### Additional Resources
 

content/chapters/11_training_llms/11_04_x_optimize.md

@@ -9,7 +9,7 @@ In this Chapter we discuss ways to optimize the performance of Large Language Mo
 
 ### Lecture Slides 
 
-{{< pdfjs file="https://github.com/slds-lmu/lecture_dl4nlp/blob/main/slides/chapter11-training-llms/112-slides-x-optimize.pdf" >}}
+{{< pdfjs file="https://github.com/slds-lmu/lecture_dl4nlp/blob/main/slides/chapter11-training-llms/slides-114-x-optimize.pdf" >}}
 
 ### Additional Resources 
 

content/chapters/12_multilingual/12_01_why_multilinguality.md

@@ -9,4 +9,4 @@ We need multilingual models to bridge language barriers, enhance global communic
 
 ### Lecture Slides 
 
-{{< pdfjs file="https://github.com/slds-lmu/lecture_dl4nlp/blob/main/slides/chapter10-multilinguality/slides-101-why_multilingual.pdf" >}}
+{{< pdfjs file="https://github.com/slds-lmu/lecture_dl4nlp/blob/main/slides/chapter12-multilinguality/slides-121-why_multilingual.pdf" >}}

content/chapters/12_multilingual/12_02_cross_lingual_word_embeddings.md

@@ -9,4 +9,4 @@ Cross-lingual word embeddings create a shared vector space for words from multip
 
 ### Lecture Slides 
 
-{{< pdfjs file="https://github.com/slds-lmu/lecture_dl4nlp/blob/main/slides/chapter10-multilinguality/slides-102-multilingual-wordembs.pdf" >}}
+{{< pdfjs file="https://github.com/slds-lmu/lecture_dl4nlp/blob/main/slides/chapter12-multilinguality/slides-122-multilingual-wordembs.pdf" >}}

content/chapters/12_multilingual/12_03_multi_lingual_transformers.md

@@ -9,4 +9,4 @@ As we have previously seen, transformers are the working horse for modern NLP. I
 
 ### Lecture Slides 
 
-{{< pdfjs file="https://github.com/slds-lmu/lecture_dl4nlp/blob/main/slides/chapter10-multilinguality/slides-103-multilingual-transformers.pdf" >}}
+{{< pdfjs file="https://github.com/slds-lmu/lecture_dl4nlp/blob/main/slides/chapter12-multilinguality/slides-123-multilingual-transformers.pdf" >}}