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analysis.R
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library(tidyverse)
library(bootstrap)
library(lme4)
source("schuster_linzen_2022/analysis/helpers.R")
fill_cols = c(
"#f9e076", # llama2 7B
"#ca6702", # llama2 13B
"#ae2012", # llama2 70B
"#a4fba6", # llama3 8B
"#0f9200", # llama3 70B
"#add8e6", # babbage
"#2986cc", # davinci
"#000000"
)
col_scale = scale_fill_manual(values=fill_cols)
s_comp <- c("p(sg|neg_pos)>p(sg|neg_neg)",
"p(sg|pos_neg)>p(sg|neg_neg)",
"p(sg|pos_neg)>p(sg|pos_pos)",
"p(sg|neg_pos)>p(sg|pos_pos)" )
s_distance_comp <- c("p(sg|neg_pos)>p(sg|pos_neg)")
p_comp <- c("p(pl|pos_pos)>p(pl|pos_neg)",
"p(pl|pos_pos)>p(pl|neg_pos)",
"p(pl|pos_pos)>p(pl|neg_neg)")
sp_comp <- c("p(sg|pos_neg)>p(pl|pos_neg)",
"p(sg|neg_pos)>p(pl|neg_pos)",
"p(pl|pos_pos)>p(sg|pos_pos)")
sp_comp_all <- c("p(sg|pos_neg)>p(pl|pos_neg)",
"p(sg|pos_neg)>p(pl|neg_pos)",
"p(sg|pos_neg)>p(pl|neg_neg)",
"p(sg|neg_pos)>p(pl|neg_pos)",
"p(sg|neg_pos)>p(pl|pos_neg)",
"p(sg|neg_pos)>p(pl|neg_neg)",
"p(pl|pos_pos)>p(sg|pos_pos)",
"p(pl|pos_pos)>p(sg|neg_neg)")
model_levels_original <- c("llama2-7B", "llama2-13B", "llama2-70B", "llama3-8B", "llama3-70B", "babbage-002", "davinci-002", "human")
model_levels <- c("Llama 2 7B", "Llama 2 13B", "Llama 2 70B", "Llama 3 8B", "Llama 3 70B", "babbage-002", "davinci-002", "Human")
base_vs_diff_level <- c("Implicit", "Explicit Novelty")
base_vs_two_level <- c("Implicit", "Explicit Plurality")
base_vs_diff_type <- c("p(sg|pos_neg)>p(sg|pos_pos)",
"p(sg|neg_pos)>p(sg|pos_pos)")
base_vs_two_type <- c("p(sg|pos_neg)>p(sg|pos_pos)",
"p(sg|pos_neg)>p(sg|two_neg)",
"p(sg|neg_pos)>p(sg|pos_pos)",
"p(sg|neg_pos)>p(sg|neg_two)")
########################### DEFINE FUNCTIONS ##########################
analyze_model_res_an_only <- function(result_path, model_name, version) {
d <- read_csv(result_path)
d$model <- model_name
d$version <- version
d <- subset(d, sent_type=="affirmative_negation") # only looking at A_N sentences
d <- d %>%
select(id, sent_type, type, correct, model, version) %>%
mutate(type = case_when(type=="neg_pos_s>pos_neg_s" ~ "p(sg|neg_pos)>p(sg|pos_neg)",
type=="pos_neg_s>pos_pos_s" ~ "p(sg|pos_neg)>p(sg|pos_pos)",
type=="neg_pos_s>pos_pos_s" ~ "p(sg|neg_pos)>p(sg|pos_pos)",
type=="neg_pos_s>neg_neg_s" ~ "p(sg|neg_pos)>p(sg|neg_neg)",
type=="pos_neg_s>neg_neg_s" ~ "p(sg|pos_neg)>p(sg|neg_neg)",
type=="pos_pos_p>pos_neg_p" ~ "p(pl|pos_pos)>p(pl|pos_neg)",
type=="pos_pos_p>neg_pos_p" ~ "p(pl|pos_pos)>p(pl|neg_pos)",
type=="pos_pos_p>neg_neg_p" ~ "p(pl|pos_pos)>p(pl|neg_neg)",
type=="pos_neg_s>pos_neg_p" ~ "p(sg|pos_neg)>p(pl|pos_neg)",
type=="pos_neg_s>neg_pos_p" ~ "p(sg|pos_neg)>p(pl|neg_pos)",
type=="pos_neg_s>neg_neg_p" ~ "p(sg|pos_neg)>p(pl|neg_neg)",
type=="neg_pos_s>neg_pos_p" ~ "p(sg|neg_pos)>p(pl|neg_pos)",
type=="neg_pos_s>pos_neg_p" ~ "p(sg|neg_pos)>p(pl|pos_neg)",
type=="neg_pos_s>neg_neg_p" ~ "p(sg|neg_pos)>p(pl|neg_neg)",
type=="pos_pos_p>pos_pos_s" ~ "p(pl|pos_pos)>p(sg|pos_pos)",
type=="pos_pos_p>neg_neg_s" ~ "p(pl|pos_pos)>p(sg|neg_neg)",
type=="neg_pos_s>pos_neg_s" ~ "p(sg|neg_pos)>p(sg|pos_neg)",
type=="neg_pos_p>pos_neg_p" ~ "p(pl|neg_pos)>p(pl|pos_neg)"
))
d <- d %>% mutate(model = case_when(model=="davinci-002" ~ "davinci-002",
model=="babbage-002" ~ "babbage-002",
model=="llama2-7B" ~ "Llama 2 7B",
model=="llama2-13B" ~ "Llama 2 13B",
model=="llama2-70B" ~ "Llama 2 70B",
model=="llama3-8B" ~ "Llama 3 8B",
model=="llama3-70B" ~ "Llama 3 70B",
model=="human" ~ "Human"))
return(d)
}
analyze_model_res <- function(result_path, model_name, version) {
d <- read_csv(result_path)
d$model <- model_name
d$version <- version
d <- d %>%
select(id, sent_type, type, correct, model, version) %>%
mutate(sent_type = case_when(sent_type == "affirmative_negation" ~ "A_N",
sent_type == "know_doubt" ~ "K_D",
sent_type == "managed_failed" ~ "M_F")) %>%
mutate(type = case_when(type=="neg_pos_s>pos_neg_s" ~ "p(sg|neg_pos)>p(sg|pos_neg)",
type=="pos_neg_s>pos_pos_s" ~ "p(sg|pos_neg)>p(sg|pos_pos)",
type=="neg_pos_s>pos_pos_s" ~ "p(sg|neg_pos)>p(sg|pos_pos)",
type=="neg_pos_s>neg_neg_s" ~ "p(sg|neg_pos)>p(sg|neg_neg)",
type=="pos_neg_s>neg_neg_s" ~ "p(sg|pos_neg)>p(sg|neg_neg)",
type=="pos_pos_p>pos_neg_p" ~ "p(pl|pos_pos)>p(pl|pos_neg)",
type=="pos_pos_p>neg_pos_p" ~ "p(pl|pos_pos)>p(pl|neg_pos)",
type=="pos_pos_p>neg_neg_p" ~ "p(pl|pos_pos)>p(pl|neg_neg)",
type=="pos_neg_s>pos_neg_p" ~ "p(sg|pos_neg)>p(pl|pos_neg)",
type=="pos_neg_s>neg_pos_p" ~ "p(sg|pos_neg)>p(pl|neg_pos)",
type=="pos_neg_s>neg_neg_p" ~ "p(sg|pos_neg)>p(pl|neg_neg)",
type=="neg_pos_s>neg_pos_p" ~ "p(sg|neg_pos)>p(pl|neg_pos)",
type=="neg_pos_s>pos_neg_p" ~ "p(sg|neg_pos)>p(pl|pos_neg)",
type=="neg_pos_s>neg_neg_p" ~ "p(sg|neg_pos)>p(pl|neg_neg)",
type=="pos_pos_p>pos_pos_s" ~ "p(pl|pos_pos)>p(sg|pos_pos)",
type=="pos_pos_p>neg_neg_s" ~ "p(pl|pos_pos)>p(sg|neg_neg)",
type=="neg_pos_s>pos_neg_s" ~ "p(sg|neg_pos)>p(sg|pos_neg)",
type=="neg_pos_p>pos_neg_p" ~ "p(pl|neg_pos)>p(pl|pos_neg)",
type=="pos_neg_s>two_neg_s" ~ "p(sg|pos_neg)>p(sg|two_neg)",
type=="pos_neg_s>neg_two_s" ~ "p(sg|pos_neg)>p(sg|neg_two)",
type=="neg_pos_s>two_neg_s" ~ "p(sg|neg_pos)>p(sg|two_neg)",
type=="neg_pos_s>neg_two_s" ~ "p(sg|neg_pos)>p(sg|neg_two)",
type=="two_neg_p>pos_neg_p" ~ "p(pl|two_neg)>p(pl|pos_neg)",
type=="two_neg_p>neg_pos_p" ~ "p(pl|two_neg)>p(pl|neg_pos)",
type=="neg_two_p>pos_neg_p" ~ "p(pl|neg_two)>p(pl|pos_neg)",
type=="neg_two_p>neg_pos_p" ~ "p(pl|neg_two)>p(pl|neg_pos)",
type=="two_neg_p>two_neg_s" ~ "p(pl|two_neg)>p(sg|two_neg)",
type=="two_neg_p>neg_two_s" ~ "p(pl|two_neg)>p(sg|neg_two)",
type=="neg_two_p>two_neg_s" ~ "p(pl|neg_two)>p(sg|two_neg)",
type=="neg_two_p>neg_two_s" ~ "p(pl|neg_two)>p(sg|neg_two)",
type=="two_neg_p>pos_pos_s" ~ "p(pl|two_neg)>p(sg|pos_pos)",
type=="neg_two_p>pos_pos_s" ~ "p(pl|neg_two)>p(sg|pos_pos)",
type=="pos_pos_p>two_neg_s" ~ "p(pl|pos_pos)>p(sg|two_neg)",
type=="pos_pos_p>neg_two_s" ~ "p(pl|pos_pos)>p(sg|neg_two)"
))
d <- d %>% mutate(model = case_when(model=="davinci-002" ~ "davinci-002",
model=="babbage-002" ~ "babbage-002",
model=="llama2-7B" ~ "Llama 2 7B",
model=="llama2-13B" ~ "Llama 2 13B",
model=="llama2-70B" ~ "Llama 2 70B",
model=="llama3-8B" ~ "Llama 3 8B",
model=="llama3-70B" ~ "Llama 3 70B",
model=="human" ~ "Human"))
return(d)
}
plot_comparisons_an <- function(df, comp_levels, model_levels, ncol, nrow_legend) {
df <- df %>% subset(type %in% comp_levels) %>%
mutate(type=factor(type,
levels=comp_levels)) %>%
mutate(model = factor(model,
levels = model_levels))
plot <- df %>% group_by(model, type) %>%
summarize(accuracy=mean(correct), ci_low = ci.low(correct), ci_high = ci.high(correct)) %>%
ggplot(aes(x=model, fill=model, y=accuracy)) +
geom_bar(stat="identity", alpha=0.8, position=position_dodge()) +
ylim(0,1) +
xlab("Comparison Type") +
ylab("% expected") +
facet_wrap(~type, ncol=ncol) +
geom_hline(yintercept = 0.5, color="#666666", lty=2) +
geom_errorbar(aes(ymin=accuracy - ci_low, ymax=accuracy + ci_high),
width = 0.4,
col="#666666",
position=position_dodge(width=1)) +
theme(legend.position="bottom", axis.title.x = element_blank(),
axis.text.x = element_blank(),
legend.text = element_text(size=10),
axis.ticks.x = element_blank(),
legend.key.size = unit(0.3, 'cm')) + guides(fill = guide_legend(nrow = nrow_legend, byrow=TRUE)) + col_scale
return (plot)
}
plot_singular_comparisons_across_version_an <- function(df) {
df_singular <- df %>% subset(type %in% c("p(sg|pos_neg)>p(sg|pos_pos)",
"p(sg|neg_pos)>p(sg|pos_pos)")) %>%
mutate(type = factor(type,
levels = c("p(sg|pos_neg)>p(sg|pos_pos)",
"p(sg|neg_pos)>p(sg|pos_pos)"))) %>%
mutate(model = factor(model,
levels = model_levels)) %>%
mutate(version = factor(version,
levels = base_vs_diff_level))
plot <- df_singular %>% group_by(model, type, version) %>%
summarize(accuracy=mean(correct), ci_low = ci.low(correct), ci_high = ci.high(correct)) %>%
ggplot(aes(x=model, fill=model, y=accuracy)) +
geom_bar(stat="identity", alpha=0.8, position=position_dodge()) +
ylim(0,1) +
xlab("Sentence Type") +
ylab("% expected") +
facet_wrap(~type+version, ncol = 4) +
geom_hline(yintercept = 0.5, color="#666666", lty=2) +
geom_errorbar(aes(ymin=accuracy - ci_low, ymax=accuracy + ci_high),
width = 0.4,
col="#666666",
position=position_dodge(width=1)) +
theme(legend.position="bottom",
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
legend.text = element_text(size=10),
legend.key.size = unit(0.3, 'cm')) + guides(fill = guide_legend(nrow = 2, byrow=TRUE)) +
col_scale
return (plot)
}
########################### LOAD DATA ##########################
base.davinci.an <- analyze_model_res_an_only("results/base/base_davinci-002_accuracy_ref.csv","davinci-002", "Implicit")
base.babbage.an <- analyze_model_res_an_only("results/base/base_babbage-002_accuracy_ref.csv","babbage-002", "Implicit")
base.llama2.7.an <- analyze_model_res_an_only("results/base/base_llama2_7B_accuracy_ref.csv", "llama2-7B", "Implicit")
base.llama2.13.an <- analyze_model_res_an_only("results/base/base_llama2_13B_accuracy_ref.csv", "llama2-13B", "Implicit")
base.llama2.70.an <- analyze_model_res_an_only("results/base/base_llama2_70B_accuracy_ref.csv", "llama2-70B", "Implicit")
base.llama3.8.an <- analyze_model_res_an_only("results/base/base_llama3_8B_accuracy_ref.csv", "llama3-8B", "Implicit")
base.llama3.70.an <- analyze_model_res_an_only("results/base/base_llama3_70B_accuracy_ref.csv", "llama3-70B", "Implicit")
human.an <- analyze_model_res_an_only("human/human_accuracy.csv", "human", "Implicit")
diff.davinci.an <- analyze_model_res_an_only("results/diff/diff_davinci-002_accuracy_ref.csv","davinci-002", "Explicit Novelty")
diff.babbage.an <- analyze_model_res_an_only("results/diff/diff_babbage-002_accuracy_ref.csv","babbage-002", "Explicit Novelty")
diff.llama2.7.an <- analyze_model_res_an_only("results/diff/diff_llama2_7B_accuracy_ref.csv", "llama2-7B", "Explicit Novelty")
diff.llama2.13.an <- analyze_model_res_an_only("results/diff/diff_llama2_13B_accuracy_ref.csv", "llama2-13B", "Explicit Novelty")
diff.llama2.70.an <- analyze_model_res_an_only("results/diff/diff_llama2_70B_accuracy_ref.csv", "llama2-70B", "Explicit Novelty")
diff.llama3.8.an <- analyze_model_res_an_only("results/diff/diff_llama3_8B_accuracy_ref.csv", "llama3-8B", "Explicit Novelty")
diff.llama3.70.an <- analyze_model_res_an_only("results/diff/diff_llama3_70B_accuracy_ref.csv", "llama3-70B", "Explicit Novelty")
model.bases.an <- rbind(
base.davinci.an,
base.babbage.an,
base.llama2.7.an,
base.llama2.13.an,
base.llama2.70.an,
base.llama3.8.an,
base.llama3.70.an
)
model.diffs.an <- rbind(
diff.davinci.an,
diff.babbage.an,
diff.llama2.7.an,
diff.llama2.13.an,
diff.llama2.70.an,
diff.llama3.8.an,
diff.llama3.70.an
)
model.an <- rbind(model.bases.an, model.diffs.an) # all model results for base and diff
baselines.an <- rbind(model.bases.an, human.an)
base.singular.an <- plot_comparisons_an(baselines.an, s_comp, model_levels, 4, 2)
base.plural.an <- plot_comparisons_an(baselines.an, p_comp, model_levels, 3, 2)
base.sp.an <- plot_comparisons_an(baselines.an, sp_comp, model_levels, 3, 2)
base.singular.distance.an <- plot_comparisons_an(baselines.an, s_distance_comp, model_levels, 1, 2)
base.vs.diff.singular.an <- plot_singular_comparisons_across_version_an(model.an)
# remove comment to save to dir
# ggsave("plots/exp1_singular.pdf", base.singular.an, width=8, height = 2, dpi=300)
# ggsave("plots/exp1_plural.pdf", base.plural.an, width=8, height = 2, dpi=300)
# ggsave("plots/exp1_sp.pdf", base.sp.an, width=8, height = 2, dpi=300)
# ggsave("plots/exp1_singular_distance.pdf", base.singular.distance.an, width=6, height = 3, dpi=300)
# ggsave("plots/exp2_singular_vs_exp1.pdf", base.vs.diff.singular.an, width=8, height = 2, dpi=300)
############################ SIGNIFICANCE TESTING ############################
model.an.subset <- subset(model.an,
type=="p(sg|pos_neg)>p(sg|pos_pos)"|type=="p(sg|neg_pos)>p(sg|pos_pos)") %>%
mutate(version=factor(version,
levels=base_vs_diff_level))
library(lme4)
glm.model<- glmer(correct ~ version+type + (1|id), data = model.an.subset, family = "binomial")
summary(glm.model)
################################## APPENDIX ##################################
#================ PLOTS FOR ALL THREE SENTTYPE ===============
plot_comparisons <- function(df, comp_levels, model_levels, ncol, nrow_legend) {
df <- df %>% subset(type %in% comp_levels) %>%
mutate(type=factor(type,
levels=comp_levels)) %>%
mutate(model = factor(model,
levels = model_levels))
plot <- df %>%
group_by(model, type, sent_type) %>%
summarize(accuracy=mean(correct), ci_low = ci.low(correct), ci_high = ci.high(correct)) %>%
ggplot(aes(x=sent_type, fill=model, y=accuracy)) +
geom_bar(stat="identity", alpha=0.8, position=position_dodge()) +
ylim(0,1) +
xlab("Comparison Type") +
ylab("% expected") +
facet_wrap(~type, ncol=ncol) +
geom_hline(yintercept = 0.5, color="#666666", lty=2) +
geom_errorbar(aes(ymin=accuracy - ci_low, ymax=accuracy + ci_high),
width = 0.4,
col="#666666",
position=position_dodge(width=0.9)) +
theme(legend.position="bottom", axis.title.x = element_blank(),
# axis.text.x = element_blank(),
legend.text = element_text(size=10),
# axis.ticks.x = element_blank(),
legend.key.size = unit(0.3, 'cm')) + guides(fill = guide_legend(nrow = nrow_legend, byrow=TRUE)) + col_scale
return (plot)
}
plot_singular_comparisons_across_version <- function(df, version_level, types_to_compare) {
df_singular <- df %>% subset(type %in% types_to_compare) %>%
mutate(type = factor(type,
levels = types_to_compare)) %>%
mutate(model = factor(model,
levels = model_levels)) %>%
mutate(version = factor(version,
levels = version_level))
plot <- df_singular %>% group_by(model, sent_type, type, version) %>%
summarize(accuracy=mean(correct), ci_low = ci.low(correct), ci_high = ci.high(correct)) %>%
ggplot(aes(x=sent_type, fill=model, y=accuracy)) +
geom_bar(stat="identity", alpha=0.8, position=position_dodge()) +
ylim(0,1) +
xlab("Sentence Type") +
ylab("% expected") +
facet_wrap(~type+version, ncol = 4) +
geom_hline(yintercept = 0.5, color="#666666", lty=2) +
geom_errorbar(aes(ymin=accuracy - ci_low, ymax=accuracy + ci_high),
width = 0.4,
col="#666666",
position=position_dodge(width=0.9)) +
theme(legend.position="bottom",
axis.title.x = element_blank(),
# axis.text.x = element_blank(),
# axis.ticks.x = element_blank(),
legend.text = element_text(size=10),
legend.key.size = unit(0.3, 'cm')) + guides(fill = guide_legend(nrow = 2, byrow=TRUE)) +
col_scale
return (plot)
}
base.babbage <- analyze_model_res("results/base/base_babbage-002_accuracy_ref.csv", "babbage-002", "Implicit")
base.davinci <- analyze_model_res("results/base/base_davinci-002_accuracy_ref.csv","davinci-002", "Implicit")
base.llama2.7 <- analyze_model_res("results/base/base_llama2_7B_accuracy_ref.csv", "llama2-7B", "Implicit")
base.llama2.13 <- analyze_model_res("results/base/base_llama2_13B_accuracy_ref.csv", "llama2-13B", "Implicit")
base.llama2.70 <- analyze_model_res("results/base/base_llama2_70B_accuracy_ref.csv", "llama2-70B", "Implicit")
base.llama3.8 <- analyze_model_res("results/base/base_llama3_8B_accuracy_ref.csv", "llama3-8B", "Implicit")
base.llama3.70 <- analyze_model_res("results/base/base_llama3_70B_accuracy_ref.csv", "llama3-70B", "Implicit")
human <- analyze_model_res("human/human_accuracy.csv", "human", "Implicit")
diff.babbage <- analyze_model_res("results/diff/diff_babbage-002_accuracy_ref.csv", "babbage-002", "Explicit Novelty")
diff.davinci <- analyze_model_res("results/diff/diff_davinci-002_accuracy_ref.csv","davinci-002", "Explicit Novelty")
diff.llama2.7 <- analyze_model_res("results/diff/diff_llama2_7B_accuracy_ref.csv", "llama2-7B", "Explicit Novelty")
diff.llama2.13 <- analyze_model_res("results/diff/diff_llama2_13B_accuracy_ref.csv", "llama2-13B", "Explicit Novelty")
diff.llama2.70 <- analyze_model_res("results/diff/diff_llama2_70B_accuracy_ref.csv", "llama2-70B", "Explicit Novelty")
diff.llama3.8 <- analyze_model_res("results/diff/diff_llama3_8B_accuracy_ref.csv", "llama3-8B", "Explicit Novelty")
diff.llama3.70 <- analyze_model_res("results/diff/diff_llama3_70B_accuracy_ref.csv", "llama3-70B", "Explicit Novelty")
model.bases <- rbind(
base.babbage,
base.davinci,
base.llama2.7,
base.llama2.13,
base.llama2.70,
base.llama3.8,
base.llama3.70
)
model.diffs <- rbind(
diff.babbage,
diff.davinci,
diff.llama2.7,
diff.llama2.13,
diff.llama2.70,
diff.llama3.8,
diff.llama3.70
)
model.base.vs.diff <- rbind(model.bases, model.diffs)
baselines <- rbind(model.bases, human)
base.singular <- plot_comparisons(baselines, s_comp, model_levels, 4, 1)
base.plural <- plot_comparisons(baselines, p_comp, model_levels, 3, 1)
base.sp <- plot_comparisons(baselines, sp_comp_all, model_levels, 3, 1)
base.singular.distance <- plot_comparisons(baselines, s_distance_comp, model_levels, 1, 3)
diff.singular <- plot_comparisons(model.diffs, s_comp, model_levels, 4, 1)
diff.plural <- plot_comparisons(model.diffs, p_comp, model_levels, 3, 1)
diff.sp <- plot_comparisons(model.diffs, sp_comp, model_levels, 3, 1)
diff.singular.distance <- plot_comparisons(model.diffs, s_distance_comp, model_levels, 1, 3)
base.vs.diff.singular <- plot_singular_comparisons_across_version(model.base.vs.diff, base_vs_diff_level, base_vs_diff_type)
# # save plots
# ggsave("plots/exp1_singular_alltype.pdf", base.singular, width=10, height = 3, dpi=300)
# ggsave("plots/exp1_plural_alltype.pdf", base.plural, width=10, height = 3, dpi=300)
# ggsave("plots/exp1_sp_alltype.pdf", base.sp, width=10, height = 5, dpi=300)
# ggsave("plots/exp1_singular_distance_alltype.pdf", base.singular.distance, width=4.5, height = 3, dpi=300)
# #
# ggsave("plots/exp2_singular_alltype.pdf", diff.singular, width=10, height = 3, dpi=300)
# ggsave("plots/exp2_plural_alltype.pdf", diff.plural, width=10, height = 3, dpi=300)
# ggsave("plots/exp2_sp_alltype.pdf", diff.sp, width=10, height = 3, dpi=300)
# ggsave("plots/exp2_singular_distance_alltype.pdf", diff.singular.distance, width=4.5, height = 3, dpi=300)
#
# ggsave("plots/exp2_singular_vs_exp1_alltype.pdf", base.vs.diff.singular, width=10, height=3, dpi=300)
# significance testing
model.base.vs.diff.subset <- subset(model.base.vs.diff,
type=="p(sg|pos_neg)>p(sg|pos_pos)"|type=="p(sg|neg_pos)>p(sg|pos_pos)") %>%
mutate(version=factor(version,
levels=base_vs_diff_level))
glm.model.alltype.base.vs.diff <- glmer(correct ~ version+type + (1|id), data = model.base.vs.diff.subset, family = "binomial")
summary(glm.model.alltype.base.vs.diff)
#================ VERSION: TWO ================
two.babbage <- analyze_model_res("results/two/two_babbage-002_accuracy_ref.csv", "babbage-002", "Implicit")
two.davinci <- analyze_model_res("results/two/two_davinci-002_accuracy_ref.csv","davinci-002", "Implicit")
two.llama2.7 <- analyze_model_res("results/two/two_llama2_7B_accuracy_ref.csv", "llama2-7B", "Implicit")
two.llama2.13 <- analyze_model_res("results/two/two_llama2_13B_accuracy_ref.csv", "llama2-13B", "Implicit")
two.llama2.70 <- analyze_model_res("results/two/two_llama2_70B_accuracy_ref.csv", "llama2-70B", "Implicit")
two.llama3.8 <- analyze_model_res("results/two/two_llama3_8B_accuracy_ref.csv", "llama3-8B", "Implicit")
two.llama3.70 <- analyze_model_res("results/two/two_llama3_70B_accuracy_ref.csv", "llama3-70B", "Implicit")
model.twos <- rbind(
two.babbage,
two.davinci,
two.llama2.7,
two.llama2.13,
two.llama2.70,
two.llama3.8,
two.llama3.70
)
# fix model version
model.twos$version <- ifelse(grepl("two", model.twos$type), "Explicit Plurality", "Implicit")
base.vs.two.singular <- plot_singular_comparisons_across_version(model.twos, base_vs_two_level, base_vs_two_type)
# ggsave("plots/exp3_singular_vs_exp1_alltype.pdf", base.vs.two.singular, width=10, height=3, dpi=300)
#================ METRIC: NONREF ==============
nonref.base.babbage <- analyze_model_res("results/base/base_babbage-002_accuracy_nonref.csv", "babbage-002", "Implicit")
nonref.base.davinci <- analyze_model_res("results/base/base_davinci-002_accuracy_nonref.csv","davinci-002", "Implicit")
nonref.base.llama2.7 <- analyze_model_res("results/base/base_llama2_7B_accuracy_nonref.csv", "llama2-7B", "Implicit")
nonref.base.llama2.13 <- analyze_model_res("results/base/base_llama2_13B_accuracy_nonref.csv", "llama2-13B", "Implicit")
nonref.base.llama2.70 <- analyze_model_res("results/base/base_llama2_70B_accuracy_nonref.csv", "llama2-70B", "Implicit")
nonref.base.llama3.8 <- analyze_model_res("results/base/base_llama3_8B_accuracy_nonref.csv", "llama3-8B", "Implicit")
nonref.base.llama3.70 <- analyze_model_res("results/base/base_llama3_70B_accuracy_nonref.csv", "llama3-70B", "Implicit")
nonref.bases <- rbind(nonref.base.babbage,
nonref.base.davinci,
nonref.base.llama2.7,
nonref.base.llama2.13,
nonref.base.llama2.70,
nonref.base.llama3.8,
nonref.base.llama3.70)
nonref.base.singular <- plot_comparisons(nonref.bases, s_comp, model_levels, 4, 1)
nonref.base.plural <- plot_comparisons(nonref.bases, p_comp, model_levels, 3, 1)
nonref.base.sp <- plot_comparisons(nonref.bases, sp_comp, model_levels, 3, 1)
# ggsave("plots/nonref_exp1_singular_alltype.pdf", nonref.base.singular, width=10, height = 3, dpi=300)
# ggsave("plots/nonref_exp1_plural_alltype.pdf", nonref.base.plural, width=10, height = 3, dpi=300)
# ggsave("plots/nonref_exp1_sp_alltype.pdf", nonref.base.sp, width=10, height = 3, dpi=300)