From b7fc2b06772665eab101cf49ddbf69546b0f75e6 Mon Sep 17 00:00:00 2001 From: Yashas Balaji Date: Tue, 12 Nov 2024 03:55:45 -0500 Subject: [PATCH] finished assignment 5 --- .../Assignment05/Balajy_assignment5.Rmd | 788 ++++++++++++++++++ .../Assignment05/Balajy_assignment5.pdf | Bin 0 -> 296270 bytes .../CT-Pub-Hallucination-Metrics.Rds | Bin 0 -> 4369 bytes .../CT-Pub-Trials-With-Hallucinations.Rds | Bin 0 -> 14798 bytes .../CT-Repo-Trials-With-Hallucinations.Rds | Bin 0 -> 65424 bytes 5 files changed, 788 insertions(+) create mode 100644 StudentNotebooks/Assignment05/Balajy_assignment5.Rmd create mode 100644 StudentNotebooks/Assignment05/Balajy_assignment5.pdf create mode 100644 StudentNotebooks/Assignment05/CT-Pub-Hallucination-Metrics.Rds create mode 100644 StudentNotebooks/Assignment05/CT-Pub-Trials-With-Hallucinations.Rds create mode 100644 StudentNotebooks/Assignment05/CT-Repo-Trials-With-Hallucinations.Rds diff --git a/StudentNotebooks/Assignment05/Balajy_assignment5.Rmd b/StudentNotebooks/Assignment05/Balajy_assignment5.Rmd new file mode 100644 index 0000000..5c5f95f --- /dev/null +++ b/StudentNotebooks/Assignment05/Balajy_assignment5.Rmd @@ -0,0 +1,788 @@ +--- +title: "DAR F24 Project Status Notebook 5" +author: "Yashas Balaji" +date: "`r Sys.Date()`" +output: + pdf_document: + toc: yes + html_document: + toc: yes +subtitle: "CTBench" +--- +```{r setup, include=FALSE} + +# Required R package installation; RUN THIS BLOCK BEFORE ATTEMPTING TO KNIT THIS NOTEBOOK!!! +# This section install packages if they are not already installed. +# This block will not be shown in the knit file. +if (!require("knitr")) { + install.packages("knitr") + library(knitr) +} + +knitr::opts_chunk$set(echo = TRUE) + +# Set the default CRAN repository +local({r <- getOption("repos") + r["CRAN"] <- "http://cran.r-project.org" + options(repos=r) +}) +if(!require("jsonlite")){ + install.packages("jsonlite") + library(jsonlite) +} + +if (!require("devtools")) { + install.packages("devtools") + library(devtools) +} +# For package conflict resolution (esp. dplyr functions) +# run con +if (!require("conflicted")) { + devtools::install_github("r-lib/conflicted") + library(conflicted) +} + +# Required packages for CTEval analysis +if (!require("rmarkdown")) { + install.packages("rmarkdown") + library(rmarkdown) +} + + +if (!require("tidyverse")) { + install.packages("tidyverse") + library(tidyverse) +} +# Our preferences +conflicts_prefer(dplyr::summarize()) +conflicts_prefer(dplyr::filter()) +conflicts_prefer(dplyr::select()) +conflicts_prefer(dplyr::mutate()) +conflicts_prefer(dplyr::arrange()) + +if (!require("stringr")) { + install.packages("stringr") + library(stringr) +} + +if (!require("ggbiplot")) { + install.packages("ggbiplot") + library(ggbiplot) +} + +if (!require("pheatmap")) { + install.packages("pheatmap") + library(pheatmap) +} +if (!require("plotrix")) { + install.packages("plotrix") + library(plotrix) +} +if (!require("kableExtra")) { + install.packages("kableExtra") + library(kableExtra) +} + +``` + +## Weekly Work Summary + +This week I worked with the Corey's hallucination data and did some analysis on the data I had recieved. I first ran his code to get the hallucination numbers, summarized each model by number of hallucination, and summarized the hallucinations by modell and trial group. Then I did a statistical test to find some correlations. + +## Personal Contribution + +I just pushed this notebook to github, it has all my analysis and such. + +## Analysis: Question 1 + +How does the distribution of the hallucinations look per model? + +### Data Preparation + +Here I import the dataframes required to run corey's code, and run it to get the hallucination data + +```{r, result01_data} +pub_data <- readRDS("../../CTBench_source/corrected_data/ct_pub/CT_Pub_data_updated.Rds") +pub_matches <- readRDS("../../CTBench_source/corrected_data/ct_pub/trials.matches_gpt_updated.Rds") +pub_responses <- readRDS("../../CTBench_source/corrected_data/ct_pub/trials.responses_updated.Rds") +trial_df <- read.csv("../../CTBench_source/corrected_data/ct_pub/CT-Pub-With-Examples-Corrected-allgen.csv", stringsAsFactors = FALSE) +matches_df <- read.csv("../../CTBench_source/corrected_data/ct_pub/CT-Pub-With-Examples-Corrected-allgpteval.csv", stringsAsFactors = FALSE) +``` + +```{r, result02_analysis} + +# extracting elements ----------------------------------------------------- + +extract_elements_v2 <- function(s) { + # Extract elements enclosed within backticks using regex + pattern <- "`(.*?)`" + elements <- regmatches(s, gregexpr(pattern, s))[[1]] + + # Remove the backticks from the matched elements + elements <- gsub("`", "", elements) + + return(elements) + +} + + + +id_hallucinations_v2<-function(trial_df,matches_df){ + + # takes the raw input from Nafis and returns the counts of each type of + # hallucination for each trial row + + # extracts the model from its corresponding column for each of the models + gptzs_matches<-data.frame(trial_id=matches_df$NCTId,model='gpt4-omni-zs', + matches=matches_df$gpt4o_zs_gen_matches) + + gptts_matches<-data.frame(trial_id=matches_df$NCTId,model='gpt4-omni-ts', + matches=matches_df$gpt4o_ts_gen_matches) + + llamazs_matches<-data.frame(trial_id=matches_df$NCTId, + model='llama3-70b-in-zs', + matches=matches_df$llama3_70b_it_zs_gen_matches) + + llamats_matches<-data.frame(trial_id=matches_df$NCTId, + model='llama3-70b-in-ts', + matches=matches_df$llama3_70b_it_ts_gen_matches) + + # combine the above; essentially transferred from wide to long form + matches<-rbind(gptzs_matches,gptts_matches,llamazs_matches,llamats_matches) + + # remove the trials used for three shot prompting, convert from Json, then + # throw out the old matches column + matches_parsed <- matches %>% + filter(!trial_id %in% c("NCT00000620", "NCT01483560", "NCT04280783")) %>% + mutate(new_matches = lapply(matches, fromJSON)) %>% + select(trial_id, model, new_matches) + + # keep the original df in case this part messes everything up (mostly for + # debugging, no longer needed as it works fine) + matches_parsed_test<-matches_parsed + + # ok, bear with me here + # loop through each row of the matches df + # + # The if conditions account for if any of the lists are empty; otherwise, it + # returns NA values which mess with the later code chunks + for (ind in 1:nrow(matches_parsed)){ + # extract the matched reference features into its own column + matches_parsed_test$matched_reference_features[[ind]]=if(length( + matches_parsed$new_matches[[ind]]$matched_features)>0){ + matches_parsed$new_matches[[ind]]$matched_features[,1]} else {list()} + # extract the matched candidate features into its own column + matches_parsed_test$matched_candidate_features[[ind]]=if(length( + matches_parsed$new_matches[[ind]]$matched_features)>0){ + matches_parsed$new_matches[[ind]]$matched_features[,2]}else{list()} + # extract the remaining reference features into its own column + matches_parsed_test$remaining_reference_features[[ind]]=if(length( + matches_parsed$new_matches[[ind]]$remaining_reference_features)>0){ + matches_parsed$new_matches[[ind]]$remaining_reference_features}else{ + list()} + # extract the remaining candidate features into its own column + matches_parsed_test$remaining_candidate_features[[ind]]=if(length( + matches_parsed$new_matches[[ind]]$remaining_candidate_features)>0){ + matches_parsed$new_matches[[ind]]$remaining_candidate_features}else{list() + } + + # concatenate a sequence of NAs to separate the remaining candidate features + # from the remaining reference features (i.e. make it look more like what + # the class had originally for the matches) + matches_parsed_test$reference[[ind]]=as.list(c( + matches_parsed_test$matched_reference_features[[ind]], + matches_parsed_test$remaining_reference_features[[ind]], + rep(NA,length(matches_parsed_test$remaining_candidate_features[[ind]])))) + matches_parsed_test$candidate[[ind]]=as.list(c( + matches_parsed_test$matched_candidate_features[[ind]], + rep(NA,length(matches_parsed_test$remaining_reference_features[[ind]])), + matches_parsed_test$remaining_candidate_features[[ind]])) + } + + # just take the columns with the trial id, generative model, reference feature + # list, and candidate feature list (the ones we just created with the NAs), + # then expand it out and sort by trial id + full_matches<-matches_parsed_test %>% + select(trial_id,model,reference,candidate) %>% + unnest(c(reference,candidate)) %>% + arrange(trial_id) + + + + # going from wide to long form for the trial info dataframe + # remove the trial group (as that was not in the original class data in this + # table) and all generated columns + trial_gptzs<-select(trial_df,-c(TrialGroup,gpt4o_zs_gen,gpt4o_ts_gen, + llama3_70b_it_zs_gen,llama3_70b_it_ts_gen)) + # identify model as gpt 0 shot + trial_gptzs$model='gpt4-omni-zs' + # re-add the gpt 0 shot generated results + trial_gptzs$candidate=trial_df$gpt4o_zs_gen + + # same as above but for gpt 3 shot + trial_gptts<-select(trial_df,-c(TrialGroup,gpt4o_zs_gen,gpt4o_ts_gen, + llama3_70b_it_zs_gen,llama3_70b_it_ts_gen)) + trial_gptts$model='gpt4-omni-ts' + trial_gptts$candidate=trial_df$gpt4o_ts_gen + + # same as above but for llama 0 shot + trial_llamazs<-select(trial_df,-c(TrialGroup,gpt4o_zs_gen,gpt4o_ts_gen, + llama3_70b_it_zs_gen,llama3_70b_it_ts_gen)) + trial_llamazs$model='llama3-70b-in-zs' + trial_llamazs$candidate=trial_df$llama3_70b_it_zs_gen + + # same as above but for llama 3 shot + trial_llamats<-select(trial_df,-c(TrialGroup,gpt4o_zs_gen,gpt4o_ts_gen, + llama3_70b_it_zs_gen,llama3_70b_it_ts_gen)) + trial_llamats$model='llama3-70b-in-ts' + trial_llamats$candidate=trial_df$llama3_70b_it_ts_gen + + # combine the above; it is now long form :) + new_trial_df<-rbind(trial_gptzs,trial_gptts,trial_llamazs,trial_llamats) + + # this is to differentiate between CT-Pub and CT-Repo; the true reference + # features are stored in different column names between the two + # + # In both cases, take the trial id, reference feature list, candidate feature + # list, and generative model columns, and remove the trials used for 3 shot + # prompting + if ('Paper_BaselineMeasures_Corrected' %in% colnames(new_trial_df)){ + trial_features<-new_trial_df %>% + dplyr::select(NCTId,Paper_BaselineMeasures_Corrected,candidate,model) %>% + dplyr::filter(!NCTId %in% c("NCT00000620", "NCT01483560", "NCT04280783")) + colnames(trial_features)<-c('trial_id','true_ref_features', + 'true_can_features','model') + } else { + trial_features<-new_trial_df %>% + dplyr::select(NCTId,API_BaselineMeasures_Corrected,candidate,model) %>% + dplyr::filter(!NCTId %in% c("NCT00000620", "NCT01483560", "NCT04280783")) + colnames(trial_features)<-c('trial_id','true_ref_features', + 'true_can_features','model') + } + + # remove factors from matches df; it was giving me some issues when trying to + # get rid of NAs, so this fixed that + full_matches<-data.frame(matrix(unlist(full_matches),nrow=nrow(full_matches)), + stringsAsFactors=FALSE) + colnames(full_matches)<-c('trial_id','model','reference','candidate') + + # extract the reference features for each trial according to the evaluator + eval_ref_features <- full_matches %>% + dplyr::select(trial_id,model,reference) %>% + dplyr::filter(!trial_id%in%c("NCT00000620","NCT01483560","NCT04280783"))%>% + drop_na() + + # surround these features in backticks and add a comma and space after each + eval_ref_features$reference<-paste0("`",eval_ref_features$reference,"`, ") + + # roll up the evaluators reference feature list into a df with 1 row for each + # trial instance + eval_ref_features<-eval_ref_features %>% + dplyr::group_by(trial_id,model) %>% + dplyr::mutate(match_ref_features=paste0(reference,collapse="")) %>% + dplyr::select(trial_id,model,match_ref_features) %>% + dplyr::distinct() + + # extract the candidate features for each trial according to the evaluator + eval_can_features <- full_matches %>% + dplyr::select(trial_id,model,candidate) %>% + dplyr::filter(!trial_id%in%c("NCT00000620","NCT01483560","NCT04280783"))%>% + drop_na() + + # surround these features in backticks and add a comma and space after each + eval_can_features$candidate<-paste0("`",eval_can_features$candidate,"`, ") + + # roll up the evaluators candidate feature list into a df with 1 row for each + # trial instance + eval_can_features<-eval_can_features %>% + dplyr::group_by(trial_id,model) %>% + dplyr::mutate(match_can_features=paste0(candidate,collapse="")) %>% + dplyr::select(trial_id,model,match_can_features) %>% + dplyr::distinct() + + # combine the dfs with the true features and evaluator-reported features + features<-merge(merge(trial_features,eval_ref_features),eval_can_features) + + # loop through each row of this df to count each of the 3 types of + # hallucinations + for (i in 1:nrow(features)){ + # calculate addition hallucinations by counting how many reference features + # the evaluator reported, counting how many of the reference features the + # evaluator reported are in the true reference feature list, then finding + # the difference between those two numbers, then doing the same thing for + # the candidate features, and summing those 2 final numbers + features$num_pos_halls[i]<-(length(extract_elements_v2( + features$match_ref_features[[i]]))-sum(extract_elements_v2( + features$match_ref_features[[i]]) %in% extract_elements_v2( + features$true_ref_features[[i]])))+(length(extract_elements_v2( + features$match_can_features[[i]]))-sum(extract_elements_v2( + features$match_can_features[[i]]) %in% extract_elements_v2( + features$true_can_features[[i]]))) + # calculate removal hallucinations by counting how many true reference + # features there were, counting how many true reference features were + # reported by the evaluator, then finding the difference between those two + # numbers, then doing the same thing for the candidate features, and summing + # those 2 final numbers + features$num_neg_halls[i]<-(length(extract_elements_v2( + features$true_ref_features[[i]]))-sum(extract_elements_v2( + features$true_ref_features[[i]]) %in% extract_elements_v2( + features$match_ref_features[[i]])))+(length(extract_elements_v2( + features$true_can_features[[i]]))-sum(extract_elements_v2( + features$true_can_features[[i]]) %in% extract_elements_v2( + features$match_can_features[[i]]))) + + # calculate the multi-match hallucinations + # create a table of counts for each true reference feature + true_ref_count=table(extract_elements_v2(features$true_ref_features[[i]])) + # create a table of counts for each reference feature according to the + # evaluator + match_ref_count=table(extract_elements_v2(features$match_ref_features[[i]])) + # initialize the reference multi-match hallucination counter + multi_halls_ref=c() + # loop through each true reference feature + for (feat1 in extract_elements_v2(features$true_ref_features[[i]])){ + # calculate the multi-match hallucinations for that feature by counting + # how many times it appears in the true reference feature list, counting + # how many times it appears in the evaluators reference feature list, and + # finding the difference between those two numbers. If the difference is + # negative, that is a negative hallucination, not a multi-match, so set + # those to 0 to count correctly + multi_halls_ref[feat1]=max(sum(as.numeric( + match_ref_count[feat1])-as.numeric(true_ref_count[feat1])),0,na.rm=TRUE) + } + + # create a table of counts for each true candidate feature + true_can_count=table(extract_elements_v2(features$true_can_features[[i]])) + # create a table of counts for each candidate feature according to the + # evaluator + match_can_count=table(extract_elements_v2(features$match_can_features[[i]])) + # initialize the reference multi-match hallucination counter + multi_halls_can=c() + # loop through each true reference feature + for (feat2 in extract_elements_v2(features$true_can_features[[i]])){ + # calculate the multi-match hallucinations for that feature by counting + # how many times it appears in the true candidate feature list, counting + # how many times it appears in the evaluators candidate feature list, and + # finding the difference between those two numbers. If the difference is + # negative, that is a negative hallucination, not a multi-match, so set + # those to 0 to count correctly + multi_halls_can[feat2]=max(sum(as.numeric( + match_can_count[feat1])-as.numeric(true_can_count[feat2])),0,na.rm=TRUE) + } + # the number of multi-match hallucinations for the trial is the sum of the + # multi-match hallucinations for each of its features + features$num_multi_halls[[i]]=sum(multi_halls_ref,multi_halls_can) + + } + # the above returned the multi-match hallucinations as a list, which is not + # ideal, so convert it to a number + features$num_multi_halls=as.numeric(features$num_multi_halls) + return(features) +} + + + + + +id_hallucinations_class<-function(trial_df,matches_df){ + + ### This function does not work because the class data does not have the + ### generated features in the trial info dataframe + + + + + # this is to differentiate between CT-Pub and CT-Repo; the true reference + # features are stored in different column names between the two + # + # In both cases, take the trial id, reference feature list, candidate feature + # list, and generative model columns, and remove the trials used for 3 shot + # prompting + if ('Paper_BaselineMeasures_Corrected' %in% colnames(trial_df)){ + trial_features<-trial_df %>% + dplyr::select(NCTId,Paper_BaselineMeasures_Corrected,candidate,model) %>% + dplyr::filter(!NCTId %in% c("NCT00000620", "NCT01483560", "NCT04280783")) + colnames(trial_features)<-c('trial_id','true_ref_features', + 'true_can_features','model') + } else { + trial_features<-trial_df %>% + dplyr::select(NCTId,API_BaselineMeasures_Corrected,candidate,model) %>% + dplyr::filter(!NCTId %in% c("NCT00000620", "NCT01483560", "NCT04280783")) + colnames(trial_features)<-c('trial_id','true_ref_features', + 'true_can_features','model') + } + + # remove factors from matches df; it was giving me some issues when trying to + # get rid of NAs, so this fixed that + full_matches<-data.frame(matrix(unlist(matches_df),nrow=nrow(matches_df)), + stringsAsFactors=FALSE) + colnames(full_matches)<-c('trial_id','model','reference','candidate') + + # extract the reference features for each trial according to the evaluator + eval_ref_features <- full_matches %>% + dplyr::select(trial_id,model,reference) %>% + dplyr::filter(!trial_id%in%c("NCT00000620","NCT01483560","NCT04280783"))%>% + drop_na() + + # surround these features in backticks and add a comma and space after each + eval_ref_features$reference<-paste0("`",eval_ref_features$reference,"`, ") + + # roll up the evaluators reference feature list into a df with 1 row for each + # trial instance + eval_ref_features<-eval_ref_features %>% + dplyr::group_by(trial_id,model) %>% + dplyr::mutate(match_ref_features=paste0(reference,collapse="")) %>% + dplyr::select(trial_id,model,match_ref_features) %>% + dplyr::distinct() + + # extract the candidate features for each trial according to the evaluator + eval_can_features <- full_matches %>% + dplyr::select(trial_id,model,candidate) %>% + dplyr::filter(!trial_id%in%c("NCT00000620","NCT01483560","NCT04280783"))%>% + drop_na() + + # surround these features in backticks and add a comma and space after each + eval_can_features$candidate<-paste0("`",eval_can_features$candidate,"`, ") + + # roll up the evaluators candidate feature list into a df with 1 row for each + # trial instance + eval_can_features<-eval_can_features %>% + dplyr::group_by(trial_id,model) %>% + dplyr::mutate(match_can_features=paste0(candidate,collapse="")) %>% + dplyr::select(trial_id,model,match_can_features) %>% + dplyr::distinct() + + # combine the dfs with the true features and evaluator-reported features + features<-merge(merge(trial_features,eval_ref_features),eval_can_features) + + # loop through each row of this df to count each of the 3 types of + # hallucinations + for (i in 1:nrow(features)){ + # calculate addition hallucinations by counting how many reference features + # the evaluator reported, counting how many of the reference features the + # evaluator reported are in the true reference feature list, then finding + # the difference between those two numbers, then doing the same thing for + # the candidate features, and summing those 2 final numbers + features$num_pos_halls[i]<-(length(extract_elements_v2( + features$match_ref_features[[i]]))-sum(extract_elements_v2( + features$match_ref_features[[i]]) %in% extract_elements_v2( + features$true_ref_features[[i]])))+(length(extract_elements_v2( + features$match_can_features[[i]]))-sum(extract_elements_v2( + features$match_can_features[[i]]) %in% extract_elements_v2( + features$true_can_features[[i]]))) + # calculate removal hallucinations by counting how many true reference + # features there were, counting how many true reference features were + # reported by the evaluator, then finding the difference between those two + # numbers, then doing the same thing for the candidate features, and summing + # those 2 final numbers + features$num_neg_halls[i]<-(length(extract_elements_v2( + features$true_ref_features[[i]]))-sum(extract_elements_v2( + features$true_ref_features[[i]]) %in% extract_elements_v2( + features$match_ref_features[[i]])))+(length(extract_elements_v2( + features$true_can_features[[i]]))-sum(extract_elements_v2( + features$true_can_features[[i]]) %in% extract_elements_v2( + features$match_can_features[[i]]))) + + # calculate the multi-match hallucinations + # create a table of counts for each true reference feature + true_ref_count=table(extract_elements_v2(features$true_ref_features[[i]])) + # create a table of counts for each reference feature according to the + # evaluator + match_ref_count=table(extract_elements_v2(features$match_ref_features[[i]])) + # initialize the reference multi-match hallucination counter + multi_halls_ref=c() + # loop through each true reference feature + for (feat1 in extract_elements_v2(features$true_ref_features[[i]])){ + # calculate the multi-match hallucinations for that feature by counting + # how many times it appears in the true reference feature list, counting + # how many times it appears in the evaluators reference feature list, and + # finding the difference between those two numbers. If the difference is + # negative, that is a negative hallucination, not a multi-match, so set + # those to 0 to count correctly + multi_halls_ref[feat1]=max(sum(as.numeric( + match_ref_count[feat1])-as.numeric(true_ref_count[feat1])),0,na.rm=TRUE) + } + + # create a table of counts for each true candidate feature + true_can_count=table(extract_elements_v2(features$true_can_features[[i]])) + # create a table of counts for each candidate feature according to the + # evaluator + match_can_count=table(extract_elements_v2(features$match_can_features[[i]])) + # initialize the reference multi-match hallucination counter + multi_halls_can=c() + # loop through each true reference feature + for (feat2 in extract_elements_v2(features$true_can_features[[i]])){ + # calculate the multi-match hallucinations for that feature by counting + # how many times it appears in the true candidate feature list, counting + # how many times it appears in the evaluators candidate feature list, and + # finding the difference between those two numbers. If the difference is + # negative, that is a negative hallucination, not a multi-match, so set + # those to 0 to count correctly + multi_halls_can[feat2]=max(sum(as.numeric( + match_can_count[feat1])-as.numeric(true_can_count[feat2])),0,na.rm=TRUE) + } + # the number of multi-match hallucinations for the trial is the sum of the + # multi-match hallucinations for each of its features + features$num_multi_halls[[i]]=sum(multi_halls_ref,multi_halls_can) + + } + # the above returned the multi-match hallucinations as a list, which is not + # ideal, so convert it to a number + features$num_multi_halls=as.numeric(features$num_multi_halls) + return(features) + + + +} + +``` + +```{r} + +hall_data <- id_hallucinations_v2(trial_df, matches_df) +``` + +```{r} + +# Summarize data to get the average number of hallucinations per model +hallucinations_by_model <- hall_data %>% + group_by(model) %>% + summarise(avg_positive_hallucinations = sum(num_pos_halls), + avg_negative_hallucinations = sum(num_neg_halls), + avg_multimatch_halls = sum(num_multi_halls)) + +# Reshape data to long format for combined bar plot +hallucinations_long <- hallucinations_by_model %>% + pivot_longer(cols = starts_with("avg_"), names_to = "hallucination_type", values_to = "count") %>% + mutate(hallucination_type = recode(hallucination_type, + "avg_positive_hallucinations" = "Positive", + "avg_negative_hallucinations" = "Negative", + "avg_multimatch_halls" = "Multimatch")) + +# Create combined bar chart +ggplot(hallucinations_long, aes(x = model, y = count, fill = hallucination_type)) + + geom_bar(stat = "identity", position = "dodge") + + labs(title = "Total Hallucinations by Model and Type for CTnPub", x = "Model", y = "Total Hallucinations") + + scale_fill_manual(values = c("steelblue", "tomato", "purple"), + name = "Hallucination Type") + + theme_minimal() + +``` +### Discussion of results + +Overall, almost all of the models followed a similar trend as shown by the bar chart. Each model except for llama3-zs had the least number of multimatch hallucinations, the most amount of positive hallucinations, the number of negative hallucinations was in between the two. All the models had roughly the same number of negative hallucinations, with llama3-zs having the least and gpt4o-ts having the most. For multimatch hallucinations, interestingly gpt4o-ts had the least, and llama3-zs had the most. And finally for the positive hallucinations, gpt4o-zs performed the worst, while llama3-zs performed the best. + +## Analysis: Question 2 + +How do F1 scores change look across different models and trial groups? + +### Data Preparation + +Here I read in the data after being reprocessed having the hallucinations removed, group it by trial group and average them by model + +```{r, result02_data} +metrics <- readRDS("../../StudentNotebooks/Assignment05/CT-Pub-Hallucination-Metrics.Rds") + +subgroup_data <- metrics %>% + select(TrialGroup, `Generation Model`, Precision, Recall, F1) %>% + mutate(TrialGroup = as.factor(TrialGroup), + model = as.factor(`Generation Model`)) + +grouped_data_avg <- subgroup_data %>% + group_by(TrialGroup) %>% + summarise(mean_f1_all_models = mean(F1, na.rm = TRUE), + sd_f1_all_models = sd(F1, na.rm = TRUE), + .groups = "drop") # Drop grouping after summarising + +``` + +### Analysis: Methods and Results + + +```{r} + + +# Step 2: Rank the diseases based on the overall mean F1 score +# Select the top 10 best-performing diseases +best_diseases <- grouped_data_avg %>% + arrange(desc(mean_f1_all_models)) %>% + slice(1:10) %>% + pull(TrialGroup) # Extract disease names + +# Select the bottom 10 worst-performing diseases +worst_diseases <- grouped_data_avg %>% + arrange(mean_f1_all_models) %>% + slice(1:10) %>% + pull(TrialGroup) # Extract disease names + +# Step 3: Filter the original dataset for top 10 and bottom 10 diseases +best_data <- subgroup_data %>% filter(TrialGroup %in% best_diseases) +worst_data <- subgroup_data %>% filter(TrialGroup %in% worst_diseases) + +# Step 4: Recalculate the F1 scores for each model within the top and bottom 10 diseases +plot_f1_scores <- function(data, title) { + grouped_data_avg_subset <- data %>% + group_by(TrialGroup) %>% + summarise(mean_f1_all_models = mean(F1, na.rm = TRUE), + sd_f1_all_models = sd(F1, na.rm = TRUE), + .groups = "drop") + + data_combined <- data %>% + group_by(TrialGroup, `Generation Model`) %>% + summarise(mean_f1 = mean(F1, na.rm = TRUE), .groups = "drop") %>% + left_join(grouped_data_avg_subset, by = "TrialGroup") + + # Step 5: Plot with error bars representing the mean F1 score ± SD across all models + ggplot(data_combined, aes(x = TrialGroup, y = mean_f1, group = `Generation Model`, color = `Generation Model`)) + + geom_point(size = 3) + # Points for each model-specific score + geom_line(aes(group = `Generation Model`)) + # Lines connecting model scores within each disease group + + # Add error bars for mean F1 ± SD + geom_errorbar(aes(ymin = mean_f1_all_models - sd_f1_all_models, + ymax = mean_f1_all_models + sd_f1_all_models), + width = 0.2, color = "black") + + + + + # Highlight the mean F1 score with a tick mark or point + geom_point(aes(y = mean_f1_all_models), shape = 3, size = 4, color = "black") + + + labs(title = title, x = "Disease Type", y = "Mean F1 Score") + + theme_minimal() + + theme(legend.position = "bottom", axis.text.x = element_text(angle = 45, hjust = 1)) +} + +# Step 6: Plot the top 10 best-performing diseases +plot_best <- plot_f1_scores(best_data, "F1 Scores by Disease Type and Model with Average Error Bars") + + +plot_best +``` +### Discussion of results + +Here we see that our actual plot looks very similar to what it looked like with the hallucinations included(included in my last notebook), but in the this case, each of the averages seem to be higher. + + + + + + + + + +## Analysis: Question 3 + +### Question being asked + +How are the different types of hallucinations affected by trial and model? + +### Data Preparation +Here I combine the results hall_data frame that I generated earlier from corey's code, and the metrics data frame that I imported with the trial groups for each trial. I combine them making sure to align model and trial. + +```{r} +# Create a mapping table for model names +model_mapping <- data.frame( + hall_data_model_name = c("gpt4-omni-ts", "gpt4-omni-zs", "llama3-70b-in-ts", "llama3-70b-in-zs"), # Names in hall_data + metrics_model_name = c("gpt4o_ts_gen_hal", "gpt4o_zs_gen_hal", "llama3_70b_it_ts_gen_hal", "llama3_70b_it_zs_gen_hal") # Corresponding names in metrics +) + +# Join hall_data with model_mapping to standardize model names +hall_data_standardized <- hall_data %>% + left_join(model_mapping, by = c("model" = "hall_data_model_name")) %>% + mutate(model_name = metrics_model_name) %>% # Replace model_name with standardized name + select(-metrics_model_name) # Drop the temporary column + +# Perform a full join on trial_id and standardized model_name +combined_data <- full_join( + hall_data_standardized, metrics, + by = c("trial_id" = "NCTId", "model_name" = "Generation Model") +) + +# Step 1: Summarize total hallucinations by trial group and model +hallucinations_by_trial_group <- combined_data %>% + group_by(TrialGroup, model_name) %>% + summarise( + total_positive_hallucinations = sum(num_pos_halls, na.rm = TRUE), + total_negative_hallucinations = sum(num_neg_halls, na.rm = TRUE), + total_multimatch_hallucinations = sum(num_multi_halls, na.rm = TRUE) + ) + +# Step 2: Reshape the data for visualization +hallucinations_long <- hallucinations_by_trial_group %>% + pivot_longer( + cols = starts_with("total_"), + names_to = "hallucination_type", + values_to = "count" + ) %>% + mutate(hallucination_type = recode(hallucination_type, + "total_positive_hallucinations" = "Positive", + "total_negative_hallucinations" = "Negative", + "total_multimatch_hallucinations" = "Multimatch")) + +``` + +### Analysis methods used + +```{r} +ggplot(hallucinations_long, aes(x = TrialGroup, y = count, color = model_name, group = model_name)) + + geom_line(size = 1) + + geom_point(size = 2) + + facet_wrap(~ hallucination_type, scales = "free_y") + + labs(title = "Total Hallucinations by Trial Group and Model", + x = "Trial Group (Disease)", + y = "Total Hallucination Counts", + color = "Model") + + theme_minimal() + + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + +``` + +Here we see a visualization of what this looks like. However, to draw any conclusions, below I am running a kruskal-wallis test by both model and trialgroup for each type of hallucination. + +```{r} + + +# Create a list to store the results +results <- list() + +# Define hallucination types +hallucination_types <- c("Positive", "Negative", "Multimatch") + +# Step 1: Loop through each hallucination type and perform one-way ANOVA and Kruskal-Wallis tests +for (type in hallucination_types) { + # Filter data for the current hallucination type + hallucination_data <- hallucinations_long %>% + filter(hallucination_type == type) + + # Perform Kruskal-Wallis Test for TrialGroup + kruskal_trialgroup <- kruskal.test(count ~ TrialGroup, data = hallucination_data) + p_value_kruskal_trialgroup <- kruskal_trialgroup$p.value + + # Perform Kruskal-Wallis Test for model_name + kruskal_model <- kruskal.test(count ~ model_name, data = hallucination_data) + p_value_kruskal_model <- kruskal_model$p.value + + # Add Kruskal-Wallis results to list + results <- append(results, list( + list(Hallucination_Type = type, Test = "Kruskal-Wallis", Group = "TrialGroup", p_value = p_value_kruskal_trialgroup), + list(Hallucination_Type = type, Test = "Kruskal-Wallis", Group = "Model", p_value = p_value_kruskal_model) + )) +} + +# Convert the list of results to a DataFrame +results_df <- do.call(rbind, lapply(results, as.data.frame)) + +# View the results DataFrame +print(results_df) + +``` + + +### Discussion of results + +Here, our results are very interesting. Positive hallucinations seems to be correlated based on model and not trial group, while negative hallucinations seem to be correlated by trial group and not model. Multimatch hallucinations on the other hand are not correlated by either. I believe this answer actually makes a lot of sense. When a model hallucinates a feature, as we saw earlier with the bar chart, some models are better than others. However for negative hallucinations this finding is very interesting as the correlation is based on the trial group itself. This could mean that each model seems to struggle with the same types of trials, and just removing features, maybe when that trial group has a large number of features to sort through. + +## Summary and next steps + +Next, I will be looking into the correlations between hallucination types themselves, and work on what we had discussed in class. + diff --git a/StudentNotebooks/Assignment05/Balajy_assignment5.pdf b/StudentNotebooks/Assignment05/Balajy_assignment5.pdf new file mode 100644 index 0000000000000000000000000000000000000000..36a40d14ab2016a7af0e062697a86a743c2104a2 GIT binary patch literal 296270 zcmb5VV~{4@()Zi8ZB659+dXaDwr$(SwC(O|+P0>9+O}=moHKjJ`#y0Z_8sT!r#@6v zRAsGIA1Z&jGFSd76h+19nHkvOC}tK0SKyeLNtj3+jIH7L_~002&Fn2)ElHSJ**Qu6 zeZVn_TiLprIg>Do+Zwr=iJF->n3}=y^TWBgI-42U!FjG-YT7xhbD(_X83;X$o01rd zCZd%Z_5Wy+%QnK9BNt?L4I!0@tzK%;RhG{Bd?WNqv16CQFVUM#2N4q8x{ddkCQOi! zFqI~|v!!|)dET+Lxc+e*Duvf5`lHbWM>-2$XpXIz4q91EA5uZw3GX65R+wn8&xp>^ 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