diff --git a/presentation/latex/talk.pdf b/presentation/latex/talk.pdf
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diff --git a/presentation/latex/talk.tex b/presentation/latex/talk.tex
index 7946f21..7f787ed 100644
--- a/presentation/latex/talk.tex
+++ b/presentation/latex/talk.tex
@@ -86,7 +86,6 @@
 \begin{itemize}
 	\item Ontology of domains
 	\begin{itemize}
-		\item Represents knowledge \& information required for specific tasks
 		\item Contains pre-defined set of slots and all possible values for each slot
 		\item Some Neural-based models solve the DST as classification task
 	\end{itemize}
@@ -113,7 +112,6 @@
 		\begin{itemize}
 			\item New way of efficiently using the generation capabilities of PLMs to solve different language tasks
 			\item Downstream task is converted to a textual prompt and given as input, the PLM directly generates the outputs from prompts
-			\item Prompting methods can be effectively used under \textsl{zero-shot} and \textsl{few-shot} settings when there's not enough training data
 			\item GPT-3 \parencite{brown2020gpt3}, Few-shot Bot \parencite{madotto2021fsb}, \textsc{PET} \parencite{schick2021pet} explored prompt-based methods for several tasks
 		\end{itemize}
 	\end{itemize}
@@ -128,7 +126,6 @@
 \end{figure}
 \vspace{-4pt}
 \begin{itemize}
-	\item Prompt Types: \textsl{prefix} \& \textsl{cloze} prompts
 	\item Prompt selection: manual, discrete, \& continuous prompts
 	\item Training strategy: Fixed-prompt LM Fine Tuning
 	\begin{itemize}
@@ -159,8 +156,7 @@
 	\end{itemize}
 	\item Research Objectives
 	\begin{itemize}
-		\item Can the dialog states be extracted from the PLM using prompts?
-		\item Can the prompt-based methods learn the DST task under low-resource settings without depending on the ontology of domains?
+		\item Can the prompt-based methods learn the DST task efficiently under low-resource settings without depending on the ontology?
 		\item Compare prompt-based approach with the baseline model
 		\item Identify the drawbacks \& limitations of prompt-based approach
 		\item Can different multi-prompt techniques help improve the performance of DST task?
@@ -184,7 +180,6 @@
 		\item Six data splits are created to perform few-shot experiments
 		\begin{itemize}
 			\item Different proportions of dialogues in each split
-			\item All the five domains are evenly distributed in each split
 		\end{itemize}
 	\end{itemize}
 	
@@ -253,9 +248,7 @@
 	\end{table}
 	
 	\begin{itemize}
-	
 		\item The pre-trained Soloist is used to fine-tune the prompting methods
-		\item All MultiWOZ data splits are used in the fine-tuning phase
 		\item Loss function for value-based prompt
 		$$\mathcal{L}=-\sum_{t}^{|D|} \log P\left(s_{t} \mid c_{t}, f\left(v_{t}\right)\right)$$
 		\item Loss function for inverse prompt
@@ -435,7 +428,7 @@
 	\end{block}
 	
 	\begin{itemize}
-		\item Open-ended generation
+		%% \item Open-ended generation
 		\item Susceptible to generating random slot-value pairs
 		\item Repeated slot-value generations
 		\item From the above example:
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