PhonATe: Impact of Type-Written Phonological Features of African American Language on Generative Language Modeling Tasks
Authors: Nicholas Deas
Jessica A Grieser
Xinmeng Hou
Shana Kleiner
Tajh Martin
Sreya Nandanampati
Desmond U. Patton
Kathleen McKeown
Venue: COLM (2024)
Keywords: data augmentation, african american language, bias and fairness, language generation
TL;DR: Fine-tuning and ablation experiments with synthetic type-written phonological features of AAL (e.g., "goin") reveal that these features are a vital consideration to understanding perceived model biases.

Abstract: Current Large Language Models perform poorly on African American Language (AAL) texts in tasks like toxicity detection and sentiment analysis. AAL is underrepresented in both pre-training data and existing benchmarks for these tasks, hindering thorough evaluation and understanding of these biases. We introduce a novel approach to synthetically introduce type-written phonological features of AAL into text, a class of AAL features that has been overlooked in prior work. Our goal is to better understand how these features affect generative language models' performance on three tasks: toxicity detection, sentiment analysis, and masked span prediction. We find that fine-tuning with synthetic type-written phonological features lowers perceived biases on downstream tasks and our ablations reveal which features have particularly large negative impacts on model performance. Our results suggest that phonological features are vital to consider when designing bias mitigation techniques.

Published as a conference paper at COLM 2024
PHONATE: Impact of Type-Written Phonological Features of
African American Language on Generative Language Model- ing Tasks
Nicholas Deas
Jessi Grieser
Department of Computer Science Columbia University ndeasêcs.
Department of Linguistics
University of Michigan
columbia. ndeas@cs.coluubiaa
edu
jgrieser@umicht ch. edu
Xinmeng Hou
Shana Kleiner
Teachers College
School of Social Policy and Practice, Annenberg School for Communications University of Pennsylvania skleiner@upenn.co edu
Columbia University fh24500tc. wtw.ttt.cccommbai..... edu
Tajh Martin
Sreya Nandanampati Department of Computer Science Barnard College sn2875ebarnard. edu
Department of Computer Science Columbia University
m8clu3acolmbb.
Desmond Patton
Kathleen McKeown
School of Social Policy and Practice, Annenberg School for Communications University of Pennsylvania dupattonêupenn. edu
Department of Computer Science Columbia University kathyêcs, columbia.edu
Abstract
Warning: This paper contains content and language that may be considered offensive some readers. Current Large Language Models perform poorly on African American Language (AAL) texts in tasks like toxicity detection and sentiment analysis. AAL underrepresented in both pre-training data and existing benchmarks for these tasks, hindering thorough evaluation and understanding of these biases. We introduce a novel approach synthetically introduce type-written phonological features of AAL into text, a class AAL features that has been overlooked in prior work. Our goal is to better understand how these features affect generative language models' performance on three tasks: toxicity detection, sentiment analysis, and masked span prediction. We find that finetuning with synthetic type- written phonological features lowers perceived biases on downstream tasks and our ablations reveal which features have particularly large negative impacts on model performance. Our results suggest that phonological features are vital to consider when designing bias mitigation techniques
1 Introduction
Existing text resources used in the development and evaluation of Large Language Mod- els (LLMs) lack sufficient representation of linguistic diversity (Dodge et al., 2021). This presents major obstacle to reliably evaluating and mitigating language model biases against minoritized language varieties like African American Language (AAL), the dialects
Our code is available athttts://githutt http://hhhth...cmm/cccc/////aaa/ ht

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of English primarily associated with the African American community in the United States (Grieser 2022). AAL, in particular, has minimal representation in both pre-training corpora for developing LLMs and finetuning data for tasks such as toxicity detection and sentiment analysis (e.g,, see Xia et al. 2020). In contrast, text resources overwhelmingly reflect White Mainstream English (WME), the variety of English primarily used by White Americans and reinforced as the norm of English (Baker-Bell Bell, 2020 Reliable evaluation is vital to measur- ing and mitigating potential linguistic blases that can pose allocational (e.g., accessibility of LLMs) and representational (e.g,, disproportionately labeling AAL as toxic) harms to historically marginalized language communities
To account for this representation disparity, recent works have proposed data augmentation techniques through morphosyntactic and lexical transformations (Tan et al., 2020 Ziems
et al. 2022 2023). Other work has also traced biased predictions on tasks like toxicity detection to lexical features (Zhou et al. 2021). While these approaches have contributed to understanding of language model biases, they do not fully capture one particular class of these lexical features, which we refer to as type-written phonological features.
Type-written phonological features are spelling variants found in written text that reflect phonetic variation, such as writing goin in place of going. Past work has found that such features primarily associated with texting, social media, and other digital contexts (Hillewaert) 2015 Cunningham 2014 Ali et al. 2022). Furthermore, Eisenstein (2013) finds significant correlations on Twitter between the use of type-written phonological features commonly associated with AAL and county-level demographic data.
To improve understanding of models' behavior with AAL text found online and inform bias mitigation approaches, we focus on type-written phonological features of AAL and how these features impact model predictions. To do so, we introduce Phonological Augmentations for Text (PHONATE), employing models to convert between graphemes (characters in normal text) and phonemes (phonetic symbols) as well as a set of 10 rule- based phoneme transformations derived from sociolinguistic literature SerratturrGrrenn,, 2009 Bailey & Thomas 2021 Thomas 2007: Pollock et al. 1998). We then use this approach to augment existing datasets for tasks with type-written phonological features of AAL and study these features' effect on generative language models' behavior in a controlled manner.
Task
Input Text
Prediction
done with finals, aka done with school, HOT JAM tonight,...
Neutral
Sentiment
done wif finals, aka done wif school, HOT JAM tonight....
Negative
I'm afraid you don't know what you't 're ta talking about! I'm fraid you dont no what you re talkin about!
Non-Toxic
Toxicity
Toxic
Table 1: Example toxicity and sentiment predictions of Flan-T5 on WME text and text with
synthetic type-written phonological features. Synthetic type-written phonological features (bolded) of AAL using PHON lead the model to predict a non-toxic text as toxic and neutral text as expressing negative sentiment.
We emphasize that we develop this data augmentation approach strictly to evaluate and analyze model behavior and do not advocate for developing models by training on synthetically generated AAL text (ethical implications are discussed further in section 6). We summarize our contributions follows:
1.
We introduce a novel approach, PHONATE, to generate synthetic typewritten phono- logical features for use in evaluating language model performance on dialectal variation.
We acknowledge the different terms used to refer to these varieties and follow those used by
Baker- Bell (2020) and Alim & SSitherman (0122).
*We follow Blodgett et al. 2020 in identifying harms. Allocational harms refer to the unfair distribution of resources among social groups, while representational harms refer to the inaccurate
representation or unfair treatment of social groups. The harms we identify follow those of Deas et al.
2023),, particularly in applications with high social impact.
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2. We show that finetuning text with PHONATE transformations reduces measured AAL biases across most models and tasks, suggesting that type-written phonological features are vital consideration for bias mitigation approaches.
3. We find that particular phonological features can drastically alter model predictions on toxicity and sentiment tasks.
2 PHONATE Approach
While some orthographic variants may be predictable given a specific phonological feature, such as the use of g- g-dropping in spelling going as goin, many other features have more com- plex effects on orthography, such as the reduced diphthong in spelling my as mah. Impacts orthography may be complicated further by the lack of one-to-one correspondence between phonemes and graphemes in English (Fry) 2004 Thorndike & Lorge 1944 Hanna
et al. 1966). For example, the voicing distinction between the interdental fricatives (fft) in thng (/#m/) and the (/ (/ăa/) have different corresponding stops (/d/ and /t/ respectively) may be realized in spelling da and ting, capturing this distinction despite originally represented with the same grapheme.
The PHONATE augmentation process involves transcribing each text into phoneme se- quence, applying mented phoneme sequence back into new grapheme sequence. To better control the frequency of each feature, we incorporate hyperparameters designating the probability that each feature applied.
wenllinguustiisiffomoen phonological rules, and transcribing the aug-
Phoneme/Grapheme Models. To transcribe sentences between graphemes and phonemes, we employ an existing multilingual saapheme-to-phoneee
(G2P) model and finetune custom phoneme-to-ge toree-tto-g-aeeeeeee (P2G) model on AAL data. The multilingual G2P model released by Zhu et al. (2022) transcribes text into IPA phonetic symbols. The model is based on T5 (Xue et al. 2022), which processes and generates individual bytes rather than relying on a tokenizer, making it more suitable for character-level tasks. Compared to pronunciation dictionary, this approach can also predict phoneme sequences for previously unseen words such as those that may appear on social media but are unlikely to be included in existing dictionaries.
To ensure that the P2G model used in PHONATE is able to generate words with type-written phonological features of AAL, finetune custom model. The training data includes both existing English pronunciation data originally used in training the G2P mode 4 as well as all words in the TwitterAAB corpus (Blodgett et al. 2018) with corresponding phoneme sequences predicted by the G2P model. Further details on finetuning the P2G model are included in Appendix
Phonological Rules. We use set of 10 phoneme transformation rules derived from phonological features studied in sociolinguistics literature (Green 2009 Bailey Thomas 2021: Thomas 2007: PPllock et al. 1998). Each phonological teature is encoded using phonemic regex patterns, deleting phonemes or replacing phonemes with the appropriate alternative based descriptions in literature.
For example, g-dropping is the orthographic feature corresponding to the pronunciation of the velar nasal (e.g,, the -ng in runming) as an alveolar nasal (e.g., the -n in runnin) while monophthongizztion describes the simplification of diphthongs, which are two adjacent vowel sounds (e-g., the in my) to a single vowel sound (e-g., the -a in ma). Based on these features, the corresponding regex-based transformations identify "1""s at word bound- aries and replace them with "n"s to simulate g-dropping as well as remove the second vowel from sequences of two vowels simulate monopithooggizatin. In addition to g- dropping and monophthongizztion, we create transformation rules for r-lessness, I-deletion,
/https://gitt t/tt::b. htmm/open-dict-attaiiiin
Fach feature may be represented by multiple transformations depending on context, such as fl-substitutions which depend on the location in the word as well as the original voicing. 6While the phonetic feature is not referred to in this way, we use g-dropping to refer to the orthographic realization.
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th- substitutions, consonant cluster reduction, morpheme-final devoicing, str-backing, and drross-dropping.. These are among the most common phonological features of AAL as well as among the most common to be realized as type-written phonological features. Descriptions of each phonological feature studied and the accompanying orthographic transformations are included in Appendix ala Appendix B respectively.
Filtering. Because the transformations operate on phoneme sequences, the transformations may inadvertently alter the meaning of the sentence such as converting past tense verbs to present tense. For example, passed in the original text may undergo the transformation for Monophthongization and become pass, To avoid these transformations, we use the spaCy part-of-speech (POS) tagger to label all original and augmented texts, and revert any transformations that result in distinct recognized term or term with different predicted part of speech tag-
3 Methods
3.1 Tasks
Models are evaluated on three tasks: toxicity detection, sentiment analysis, and masked span prediction. Biases against AAL have been identified for both toxicity (e.g., Sap et al. 2019) and sentiment (e.g,, Resende et al. 2024) classification tasks, but no studies to the authors' knowledge have examined these tasks in the context of AAL in generative language models. Additionally, prior work evaluating biases in generative language models have been limited to primarily intrinsic evaluations (Deas et al. 2023 Groenwold et al., 2020), while our work also includes extrinsic evaluations of model perlormance. We evaluate models on a masked span prediction (MSP) task following these prior works on generative models as well.
3.2
Data
Task
Dataset
Dialect
Size
Avg. Length
DWMW17 (Davidson et al. 2017
Toxicity
183,675
62.48
Jigsaw
WME
Sentiment
TweetEval Sentiment (Barbieri al,
2020
45,614
19.35
MSP
BookCorpus (Zhu et al. 2015
20,000
11.20
Evaluation
TwitterAAE subset
Groenwold et al.
2020)
AAL
2,019
20.51
Table 2: Summary statistics for datasets used throughout experiments. Average length is measured in tokens using spaCy.
To finetune models, we use existing datasets primarily composed of WME and filter out potential AAL texts to form baselines with no additional exposure AAL and remove
potentially biased annotations of AAL in the original datasets (Sap et al. 2022 ? augment these datasets using PHONATE to introduce synthetic type-written phonological features and compare against other data augmentations. To evaluate downstream perfor- mance, we naturally occurring AAL to measure impacts on model predictions. All datasets used are summarized in Table
We then
Toxicity Detection. For toxicity detection experiments, DWMW17 Davidson et al. 2017
and Jigsaw are used for finetuning all model variants. The datasets include samples of Tweets and Wikipedia comments respectively labelled for toxicity. We reduce the combined dataset two labels, conserving "non-toxic" labels and collapsing all other "toxic" (toxic, severely toxic, obscene, threat, insult, identity hate, offensive, hate speech, or abusive).
FFllowwng (Xia al. 2020), we use the classifier introduced in Blodgett al. 2016 to remove all texts for which the predicted probability of representing AAL. is higher than U.S. hwwp///wwwww/coc
data

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Sentiment Analysis. For the sentiment analysis experiments, use the sentiment analysis subset of the TweefEEal corpus (Barbieri et al. 2020) to finetune models. The corpus includes Tweets labeled for a variety of tasks including emotion detection, stance detection, and sentiment analysis. The sentiment analysis subset includes labels for each Tweet as either negative, neutral, positive sentiment.
Masked Span Prediction. Finally, masked span prediction experiments use the BookCorpus dataset (Zhu et al., 2015) for finetuning. The BookCorpus includes short sentences from thousands of books by authors that were unpublished at the time.
Evaluation. To evaluate finetuned models on genuine AAL text on social media, we corpus of Tweets reflecting AAL: the TwitterAAE corpus Blodgett et al. (2018). Prior work has found that an extremely small percentage of randomly sampled Tweets are toxic (Founta et al. 2018). Based on this and following Xia et al. (2020), we also assume that all texts in
IwitterAAE are non-toxic. In particular, we use the subset of TwitterAAE texts and the associated WME counterparts introduced in (Groenwold et al. 2020).
3.3 Metrics
Augmentation Quality. To evaluate the quality of the rule-based phonological transforma- tions, we conduct human evaluation of augmented texts. We create the evaluation data by first sampling 60 WME texts from the data introduced in inoeroewwlld et al. (2020) and then apply PHONATE to the WME counterparts to generate pairs of WME and correspond- ing texts with synthetic type-written phonological features of AAL. As baselines, we also collect judgments for the original AAL counterparts in the dataset to compare the quality of PHONATE transformations to naturally occurring AAL texts. We ask annotators to rate each text two dimensions using 5-point Likert scales. Naturalness asks how likely the text would be written by a human on social media; and following Deas et al. (2023), Meaning Preservation asks how well the augmented text conserves the meaning of the original text. Each annotator is asked to rate 50 texts such that 40 texts are shared between them. Rat- ings for these shared texts averaged. Both annotators are self-identified AAL speakers studying linguistics.
Toxicity. To evaluate the impact of different data variants on toxicity bias measures, we examine the False Positive Rate (FPR) on the AAL test set. Using the assumption that a small fraction of the TwitterAAE (Blodgett et al. 2018) dataset toxic, we consider any text predicted to be toxic as a false positive (Founta et al.) 2018 Xia et al. 2020). In the experiments, FPR is calculated as FPR # Toxic Predictions Texts
and we consider lower FPR percentages to indicate less biased model toxicity predictions.
Sentiment Detection. To evaluate how each data augmentation technique affects perceived sentiment biases, we again examine how often models trained on each dataset variant label given text with negative sentiment. Unlike the toxicity task, sentiment expressed on social media is often mixed preventing the assumption that most texts express positive sentiment. Instead, we extract the pairs of AAL and WME text for which the model labels the WME text as positive sentiment and calculate the percentage of AAL texts in the subset that are labeled as negative sentiment. Within the set of positively labeled WME texts, the rate of
We consider lower percentages to illustrate less biased predictions as it suggests that models are less likely to label positive sentiment text as negative due to the presence of AAL features.
negative predictions is calculated # Posifive WME Predictions
Negatise AAL Predictions
Masked Span Prediction. Finally, we evaluate models in generation setting where models are tasked with predicting a word or phrase masked from the input as in Deas et al. 2002)). Performance on this task is measured with perplexity of the generated span to reflect how likely the original span is from the model's perspective, and top-k entropy to reflect the model's overall confidence in the predicted spans. Lower perplexities are considered to reflect less biased model behavior as lower scores suggest that the model gives higher probability to AAL texts.
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3.4 Models
We evaluate the effect of type-written phonological features on four generative language models, expanding on prior work identifying AAL biases in generative language models (Deas al. 2023; Groenwold et al. 2020). T5 (large) is an encoder-decode
model with 770 millionn parameters and is pre-trained on the C4 dataset as well as several generation tasks such as summarization (Raffel et al. 2020). With the same architecture, Flan-T5 (large) has 780 million parameters and is instruction-tuned on larger set of tasks as well as chain-of- thought data (Chung et al. 2022). To represent larger, open-source models, we evaluate Mistral-7B, 7.3 Billion parameter decoder-only model (Jiang et al. 2023). Because Mistral is a decoder-only model and is not pretrained on the same masked span prediction task as T5 and Flan-T5, we restrict its evaluation to the toxicity and sentiment tasks. Finally, we evaluate the robustness of ChatGPT, a commercially available, chat-based model trained on human preferences. Model checkpoints and prompts are included in in/pppendix D and details on finetuning and generation hyperparameters are included in Appendix E
3.5 Experimental Setup
First, we evaluate the effect of finetuning on synthetic data. Measuring the impact of syn- thetic finetuning on real AAL data performance can suggest whether better representation of type-written phonological features or knowledge of these features can aid in debias- ing language models on these tasks. To establish baseline, models are finetuned on the original WME data with no augmentations (Base) to reflect models with no additional exposure to AAL. To represent the impact of morphosyntactic features alone, set of models are finetuned on the data augmented using VALUE (Ziems et al. 2022). In this case, all features are applied to the text except the lexical features in order to isolate the impact of PHONATE on model predictions. Finally, a set of models are finetuned on data with morphosyntactic and random phonological augmentations to isolate the effect of systematic augmentations inspired by phonological features of AAL against random perturbation Finetuning hyperparameters for T5, Flan-T5, and Mistral are included in Appendix F.
We compare all baseline data variants to data augmented with both VALUE and PHONATE to reflect the added benefit of phonological features in finetuning data. Across all tasks, phonological transformations are performed where applicable with probability 20%. We stress that this does not accurately reflect the frequency or use of type-written phonological rrequencyoo
features online as our intent is not necessarily to produce realistic AAL text, but to study and evaluate models' behavior with these features. Examples of paired original texts and PHON ATOOAATT-uugmettd texts are included in Table 1
In a second set of experiments, we leverage PHONATE to investigate and better isolate the impact of each feature on model predictions. 5,000 texts with non-toxic labels for toxicity task and 5,000 texts with positive labels for the sentiment task are randomly sampled from the original dataset to analyze T5, Flan-T5, and Mistral. We examine 3 variants of the sample for each transformation: the original, VALUE-augmented,, and PHoNATE-augmented variants. Unlike in the finetuning experiments, each phonological feature is applied individually to the sample in order to isolate the effect of each feature 10 We examine WME-finetuned models predictions on each variant measuring incorrect predictions. Due to the large number of texts for each feature, ChatGPT is evaluated on smaller sample of 500 candidate texts.
VVowels are only replaced with other vowels, and consonants only with other consonants. For fair comparison, we restrict random augmentations such that the same counts of insertions, substitutions, and deletions align with the transformations applied using PHONATE for each text.
Samples are subset to only the texts where is possible to synthetically introduce each feature.

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4
Results
4.1 PHONATE Quality
Table 4|shows the average ratings for the original AAL texts in (Groenwold et al. 2020) and
WME texts augmented with РHONATE. Most PHONATE-aagmmeted texts (61.9% are rated 3 or higher for naturalness, suggesting that majority of transformations reasonably reflect natural type-written phonological features. The Naturalness score for PHONATE falls below that of the human-written texts, but this is expected for synthetic augmentations, particularly as the judged PHONATE texts include type-written phonological features alone with no other features. Additionally, PHONATE receives high rating for Meaning Preservation, suggesting that observed changes to model performance in the presence of PHONATE features likely due to the orthographic changes rather than incidental meaning changes.
Original WME Text
PHONATE-Augmenter Text
I Isee Thanks for clearing that up. Talk understand. the only one left. I'm going to check out Centiare too. Looks like it will be way better than WP.
see Thanks for clearin dat up. Talk understan. deh only one lef. I'ma check out Centiare too. Looks like it will be wa better den WP.
Table 3: Example texts with PHONATE transformations (bolded) on all applicable terms.
Texts
Natural
Meaning Avg. Score
Avg. Score
% 2
% 1623
Human PHONATE
4.24
95.2%
4.52
100%
3.01
61.9%
4.69
100%
Table 4: Average ratings and percentage of ratings 2 3 for PHONATE transformations and original human-written AAL counterparts from the (Grennwold et al. 2020) dataset.
4.2
Synthetic Phonological Features in Finetuning
Toxicity Detection
Sentiment Classification
60
25
Neg Sentinent %
Toxic FPR %
16
10
0
T5
Flan-T5
Mistral
T5
Flan-Ty Model
Mistral
Model
Original
Random
VALUE
+PHONATE
Figure 1: Toxicity False Positive Rates (left) and rate of negative sentiment predictions (right) on the AwitterAAE (Blodgett et al. 2018) corpus for different finetuning variants. Lower FPR and Negative Sentiment %8 indicate less biased model predictions.
Toxicity. Figure presents the FPRs for models finetuned on each dataset variant. First, T5 and Flan-T5 finetuned on random augmentations (Random) increase the FPR over the WME- finetuned baselines (Original), suggesting that random augmentations do not consistently improve model understanding or robustness to phonological features. Morphosyntactic augmentations (VALUE) also slightly raise the FPR for T5 and Mistral, but lower the FPR for Flan-T5, suggesting that morphosyntactic features alone do not consistently aid in toxicity detection tasks. Both morphosyntactic and phonological features (+PHONATE))
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however, lower the FPR for all models, and lower the FPR the most for T5 and Flan- T5. This supports our hypothesis that phonological features are significant in toxicity detection models, as the use of systematic phonological features rather than random or morphosyntactic augmentations improves the FPR most.
Sentiment Detection. Erreettion.Fiuureee also includes the rate of negative sentiment labels AAL texts where the WME counterparts are labeled with positive sentiment across finetuning dataset variants. As with the toxicity task, random augmentation (Random) raises the rate of negative sentiment labels for T5 and Flan-T55 compared to the WME-finetuned baseline (Original), while morphosyntactic augmentations (VALUE) alone slightly raise the rate for T5 and slightly lower the rate for Flan-T5 and Mistral. Also similar, finetuning with the combination morphosynlactic and phonological augmentations (+PHONATH) results in the lowest rate of negative sentiment predictions across models, suggesting again that representation of phonological features significant for sentiment analysis models predicting AAL text as well.
Masked Span Perplexitty
Top-5 Entropy
20
Log Perplexity
15
Entropy
10
1.0
0.5
T5
Flan-T5
0.0
Flan- T5
Model
Model
Original
Random
VALUE
[PHoNATe
Figure 2: Log Perplexity and Top-5 Entropy scores on the TwitterAAE (Blodgett et al., 2018 corpus for 15 and Flan-T5 finetuning variants. Lower log perplexities indicate the model designates higher probability to masked AAL spans, while lower entropies indicate the model places higher probability on its top predictions.
Masked Span Prediction. Unlike the toxicity and sentiment tasks, the results for the masked span prediction task present less consistent trends, shown in Figure 2 (full results included in Appendix G). In this case, for T5, the WME dataset (Original) and morphosyntax-augmemn dataset (VALUE) result in the lowest perplexity scores. At the same time, the same variants of Flan-T5 yield the highest perplexity scores and the lowest entropy scores, suggesting that they confidently predict incorrect spans the AAL data. Despite not having the lowest perplexity for 15, the combination of morphosyntactic and phonological augmentations (+PHONÅTE) does yield the lowest perplexity for Flan-T5, consistent with the toxicity and sentiment tasks. Additionally, the addition of phonological transformations still exhibits lower perplexity than random augmentations (Random) in both cases.
4.3 Model Robustness to Phonological Features
presents the FPRs when phonological features are applied in isolation alongside the average FPRs on the original and VALUE-augmenteed data (FPRs on original and VALUE-augmeetted data within each subset are shown in Appendix H). Overall, while certain features lead to minor changes in performance, others result notably higher FPRs. In particular, consonant cluster reduction and final devoicing cause large increases in FPR for T5, Flan-15, and Mistral, as well as monophthongization for T5 and Flan-T5. Interestingly, the most and least impactful features are highly similar between T5 and Flan-T5, possibly because instruction finetuning includes little or no AAL to impact performance. The most impactful features for ChatGPT are largely different from the other models, with predictions primarily impacted by features such as th-substitutions, r-lessness, and lelssnees. This may signal differences in the distribution of AAL features among models' pre-training corpora.
Toxicity. Figure 3
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Original Averae
VALUE Average
PHоNATE
T5
Flan-T5
Type-written Phonologienl Feature
Consonant Roduction
Consonant Reduction
Uh-subet itsuubstitttons
Final Devoicing
Final Devoicing
Monophthongizztiol
Monophthongizaie
th-su dt-substittutions
Haplology g-dropping r- lesshess
s-dropping
Haplology
Stress- Dropping
str- backiig
r-lessness
Stresss- Dropping I-lessness
str-backing
I-lcssness
Mistral
CharGPT
Consonant Reduction
Stress- Stress-Droppinn Dropping 8 g-dropping l-eessnsss
str-backing
Haplology
Stress-
Dropping
Haplology r-Jossncss
Final Devoicing
Jessness
th substitutions Consonant Reduction
T-lessmess
g-dropping
str- backing
Moonoptthongizztion
Monophtbongization
th-substitutions
Final Devoicing
12
16
20
24
28
10
50
80
TV
80
90
100
Toxicity FPR %
Figure 3: Rate of toxic predictions of WME-finetuned models on PHONATE-augmenter non-toxic texts by type-written phonological feature. Average FPRs on original and VALUE- augmented texts are indicated by dashed lines. Type-written phonological features (y-axes) are ordered by largest impact, and higher FPRs indicate greater impact on model predictions.
Sentiment. A similar analysis for the sentiment task is shown in Figure 4 presenting the rate of Neutral or Negative sentiment labels for each phonological transformation alongside original and VALUE-augmented data variants. In contrast to the toxicity task, all phonological transformations cause notable increases in the rate of negative or neutral labels for T5 and Flan-T5, Again, the order of the most impactful features is largely similar between the two models, though the order largely differs from that of Mistral, ChatGPT, and the results the toxicity analysis.
Between both the toxicity and sentiment robustness results, consonant cluster reduction, final devoicing, and nonophthongization appear to be responsible for some of the largest increases in the rate of toxic or neutral/ /nggttive sentiment predictions for T5, Flan-T5, and Mistral. ChatGPT, however, appears to be more consistently impacted by stress dropping and th-substitutions in both tasks.
Related Works
AAL Bias in NLP Tasks. Models and datasets across variety of tasks have been shown to be biased or less performant with AAL texts. One of the most studied settings for AAL
biases is toxicity detection (Sap al. 2019 2022: Chuang et al. 2021 Xia al. 2020). Particularly related to the impact of phonological features and lexical variants, Zhou et al.
(2021) examines how lexical items and dialect markers of AAL may trigger toxicity systems to falsely label a text as toxic. In addition to toxicity detection, Resende et al. (2024) also identifies how sentiment analysis systems are disproportionatety likely to label African American English expressions with negative sentiment. Beyond these tasks, performance disparities have also been uncovered in Automatic Speech Recognition systems (Martin &
Tang, 2020 Koenecke et al., 2020; Mengesha et al., 2021), , language identification systems Bloddett et 2016) Blodgett & O'Connor 2017), and models for dependency- POS-tagging (Blodgett et al.) 2018 Jørgensen et al. 2016). Biases in generative language models, however, have received little attention and been constrained to intrinsic evaluations
and
2018)orgennsen
(Deas et al. 2023: Groenwold et al. 2020). In contrast, include extrinsic evaluations

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Original Avenae
VALUE Average
PHONATE
Flan-T5
Monophchongization
Monophrhongization
Haplology
Haplology
Type-writtnn Phonological Feature
Stress- Dropping
str- backing
str-backing
Consonant Final
Reduction
Consonant Reduction
Devoicing
il-subetjlutions
r-lossnsss
T- lessness
Stress-
Stress-Dropping
Dropping
Final Devoicing
1-lessness
I-lessness
th-substitutiions
g-dropping
g-dropping
Misrnl
ChatGPT
Monophthongizatin
Final Devoicing
Haplology
Stress-Droo Drooss-Drooping
str-backing
Haplology
Stress- Dropping
th-substitutions
th-substittutonns
Monophthongizatie
Final Devoicing
Consonant Reduction
Consonant
Reduction r- lessness
r-lessness
g-dropping
I-Icssness
str- baccbackig
g-dropping
I-lessness
10
20
25
30
10
15
20
Neg/Neutral Neutral %
Figure 4: Rate of negative or neutral predictions of WME-finetuned models on РHONATE- augmented, positive sentiment texts by type-written phonological feature. Average nega- tive/neutral rates original and VALUE-augmented texts are indicated by dashed lines. Type-written phonological features (y-axes) are ordered by largest impact, and higher %s indicate greater impact model predictions.
of models on toxicity detection and sentiment analysis tasks while also giving focus to type-written phonological features.
Linguistic Data Augmentation. Several prior works have used data augmentation for both evaluating and mitigating societal biases in language models. Maudslay et al. (2019) augments data with counterfactuals and name substitutions to mitigate gender biases, while Qian al. (2022) uses neural perturbations
augment data along multiple demographic axes. Similar approaches have been applied to linguistic biases well, using augmentations to approximate linguistic features of a language variety or dialect. Morpheus (Tan et al., 2020) augments word inflections ad adversarial during training to make models more robust to 12 English and AAL speakers. Alternatively, to better reflect the targeted language variety, Ziems et al. (2022) and Ziems et al. (2023) use surface-level transformations to synthetically introduce morphosyntactic and lexical features of variety of dialects. In contrast to these approaches, study linguistic biases against AAL focusing on synthetically introducing type-written phonological features without relying on limited dictionaries.
Conclusion
We introduce novel approach, PHONATE, to augment WME texts with synthetic type- written phonological features of AAL. We use PHONATE conduct two sets of experiments evaluating the significance of type-written phonological features on generative language models in three tasks. First, we finetune models for toxicity, sentiment, and masked span prediction using datasets with different sets of augmentations. We find that finetuning with both morphosyntactic and phonological features typically results the best or least bi- ased performance on toxicity, sentiment, and masked span prediction tasks, outperforming finetuning with morphosyntactic features alone and random phonological augmentations. Additionally, evaluate how often morphosyntactic and individual phonological fea- tures alter model predictions on the toxicity and sentiment tasks, finding that particular phonological features have large impacts on model predictions.
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Limitations
We recognize several limitations accompanying the results of our experiments. First, the augmentations applied by PHONATE are not exhaustive and do not perfectly capture the natural use of type-written phonological features. The aim in using the PHONATE approach is to improve the recall of type-written phonological features by avoiding reliance on finite list of features, and we combat potential inaccuracies by filtering transformations with POS-tagging- Additionally, both PHONATE and dictionary approaches to incorporating type-written phonological features are limited in that they do not consider larger context in applying transformations (e.g,, some features depend on the preceding or following word). As there are few studies of the use of type-written phonological features, we leave improved augmentations and study of the differences between spoken and written phonological features to future work.
Additionally, we limit our evaluation to generative models on three tasks-toxicity, sentiment, and masked span prediction-whie may not reflect the same patterns as with other tasks. At the same time, in the masked span prediction experiments, we are unable to reliably mask only AAL features when evaluating models, leading the scores to also reflect performance on spans without features. We examine these tasks based prior studies that have uncovered biases in both encoder-only and generative language models as well as to include both extrinsic and intrinsic evaluation of the included models. We leave further analysis, evaluation of classification model architectures, and evaluation on other tasks to future work.
Ethics Statement
We recognize that in developing an approach to simulate features of African American Language, it could be used to finetune model to generate AAL and mimic AAL speakers online. To avoid this in our own experiments, we refrain from finetuning any model to generate AAL using the synthetic augmentations: in the toxicity and sentiment experiments, only the inputs are modified, and in the masked span prediction task, we avoid masking augmented tokens. Additionally, we emphasize that finetuning on data augmented to introduce synthetic features of AAL is not intended as approach to mitigate, and our primary aim is to show that phonological features in online texts have a notable impact on model performance. We are committed to openness and transparency in our research the impact of AAL features on LLM performance and our ultimate aim is the development of core mitigation strategies that can address problems in current practice. Given the historical linguistic subordination of AAL that derives from prejudices against it speakers, we feel it recision-makinn processes, and thus, we recruit
imperative center AAL speakers AAE-speakers to validate our approach. The research must come out of Black communities in order to develop best practices around the use of AAL data and the development of applications with social impact that must be made available to all communities.
Acknowledgements
This work was supported in part by grant IIS-2106666 from the National Science Foundation, National Science Foundation Graduate Research Fellowship DGF-2036197, the Columbia University Provost Diversity Fellowship, and the Columbia School of Engineering and Applied Sciences Presidential Fellowship. Any opinion, findings, and conclusions or recom- mendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
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A Phonological Features of AAL
Beyond morphosyntactic and lexical features, AAL also exhibits phonological and phonetic features that are realized as variation in the pronunciation of consonants and vowels in particular contexts (Thomas) 2007). Frequent features of AAL phonology considered in this work are detailed below, compiled from linguistic studies of AAL phonology (Green 2009
Bailey & Thomas 2 Thomas 2007 Pollock et al. 1998).
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r-lessness, non-rhoticity, or r-deletion describes cases where the syllable and often word-final /x/ may not be pronounced or vocalized as /w/. In non-rhotic dialects, a word such as teacher (/titfa/ may be pronounced as teacha (/titfar/), or store (/star) as sto (/stww/)
I-deletion similarly involves deletion of /1/ sounds, in word-final positions or preceding labial consonants. The /1/ may be deleted entirely, or vocalized /o/, /w/, or /a/. For example, cool (/kul/) may be pronounced as coo (/ku/).
g-dropping occurs when the word-final -ing (-/m/) /) may be pronounced as -in (/m/) in non-monosyllabic words. For example, running (/rani) /) may be pronounced as rummin (/ranin/)
th-fronting alters the pronunciation of /0/ (voiceless in bath) and /5/ (voiced in that), with differing realizations depending on the position in the word and voicing. /0/ may be pronounced as /t/ in word-initial positions or /f/ in word-final positions while / may be pronounced as /d/ in word-initial positions or /v/ in word-final positions. For example, bath (/bat/ may be pronounced as buf (/baf/) /) while that (/ //bat/) may be pronounced dat (/dat/).
Consonant cluster reduction simplifies clusters of two or more adjacent consonants in word- final positions. In most cases, the final consonant sound is deleted, such as how first (/farst/) may be pronounced firs (/fars/).
Final devoicing replaces morpheme-finge final voiced consonants with their voiceless counterparts. For example, kids (/kids/) may be pronounced as kits (/kits/).
feature unique to AAL among North American dialects is str-backing, where /str/ in word-initial positions may be pronounced /skr/- For example, street (/strit/) may be pronounced skreet (/skrit/).
Beyond consonantal features, vowels may be altered through monopithongization or diph- thong simplification, where diphthongs, two successive vowel sounds, are simplified monophthong, single vowel sound. For example, my (/mai/) may be pronounced mah (/ma/).
Finally, stress-dropping describes how word-initial, unstressed syllables may be deleted. A common example is realized when about (/albout/) is pronounced 'bout (/bout/).
B Rule-Based Phonological Transformations
Table shows each feature with a description of the associated regex transformation. We employ this approach rather than end-to-end approach to ensure adherence to the features as identified by sociolinguistics studies. This approach also better ensures that the meaning of the original text is conserved, given that prior work finds that style transfer approaches often fail to conserve meaning (Ziems et al. 2022).
C Phoneme-to-Grapheme Model finetuning
While we use an existing Grapheme-to- Popphoemeto- model for transcribing text into phoneme sequences, PHONATE requires a Phoneme-to-Grapheme model capable of producing terms reflecting type-written phonological features of AAL. To do this, we create a dataset consist- ing of 1) the CMU English Pronouncing Dictionary with added syllable markers and 2) predicted pronunciations of AAL text on social media from Blodgett al. (0018)
We use the multilingual Crapheme-to-Phonene model pronunciations of all terms the TwitterAAB corpus. We filter out terms containing 3 or more sequential repeated characters (e.g,, yesssss) or non-Latin characters before generating the paired AAL terms and predicted pronunciations.
Zhu modelZZu
2022 to generate predicted
11 http://www..peeeec...eeeeeeeeeee cs. https:://itthb. http:///oeeenddctttataaaa//--apa
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Feature
Description
Phonemes/Pattermn
Transformation
diphthongs (sequences of
Monophthong- ization
diphthongs (i.e. /au/ or /ar/)
First vowel phoneme in pair
vowel sounds) are simplified to single monophthongs
/r/ phonemes at the ends
/rhotic) before consonant or after
r-lessness/ r- deletion
words or after consonants at
//
beginnings of words can be dropped
word-initial consonants
[str] sounds pronounced
str-backing
/st(rhotic)/
/sk(rhotic)/
[skr]
[th] sound pronounced as
/d/, /t/ or /v/, /f/
th-substitutions
/t/, /d/, /fv depending
/0/ /8/
on voicing and position
[]] sound deleted before
/11/ before labial consonant
I-deletion
labial consonant
labial consonants
Substitute voiced final
/(devoiced consonant)/
Final devoicing
consonant with voiceless
/(voiced consonant)/
counterpart
Delete repeated vowel syllable
repeated vowel or syllable
single instance of vowel or syllable
Haplology
Remove last consonant in word-fina cluster of consonants
Consonant
/cconsonat))1.) (consonant)/
/any) (consonant)(1.)/
Cluster Reduction
[igg] word endings replaced with [in]
g-dropping
/w/
/m/
Remove unstressed syllable beginning of word
Stress-dropping
(3
/'(any)/
Table 5: List of phonological rules derived from sociolinguistic literature on African Ameri- can Language phonological features
With this data, we finetune byT5-small model Xue et al. (2022) to produce the original term given the phoneme sequence reflecting pronunciation. The model is finetuned for single epoch with learning rate 3e-4, linear learning rate schedule, and weight decay 0.01.
D Model Checkpoints and Prompts
For 15, Flan-T5, and Mistral, we use the google-t5/t--arge, google/flan-t5, -55 and mistralai/Mistral 7B-v0.1 checkpoints respectively made available through Huggingface. For querying Chat- GPT, we use the gpt-3.5-turbo-1100 checkpoint for reproducibility and a temperature of 1 in generations.
All prompts are drawn from (Si et al. 2023) and (Zhang et al. 2024). Mistral prompts are appended with end token ("(/s)")
Prompts used for each task and model are shown in Table 6
E Generation Hyperparameters
For toxicity and sentiment tasks, models are restricted to generating and 3 tokens as expected outputs are ("Yes", "No") and ("Negative", "Neutral", "Positive") respectively. For the masked span prediction task, models can generate up to 64 tokens. All generation is performed with greedy decoding.
For reproducibility, we query ChatGPT with temperature 0 and seed 1. Only user messages provided through the API with system message.
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Task
Models
Prompt
"Categorize the comment the basis of toxicity. the comment is toxic (harmful, malicious, derogatory, threat, insult, identity attack, etc.) output Yes, otherwise output No. [INPUT TEXT]"
T5, Flan-T5, Mistral
Toxicity
"Categorize the comment on the basis of toxicity. the comment is toxic (harmful, malicious, derogatory, threat, insult, identity attack, etc.) output Yes, otherwise output No.
ChatGPT
Sentence: [INPUT TEXT] Label:"
15, Flan-15, Mistral
"[INPUT TEXT Sentiment:
Sentiment
"Please perform Sentiment Classification task. Given the sentence, assign a sentiment label from I'Negative', 'Neutral', 'Positive']. Return label only without any other text. Sentence: IINPUT TEXT Label:"
ChatGPT
Table 6: Prompts used for each model in Toxicity and Sentiment task finetuning and evaluation.
Finetuning Hyperparameters
Across tasks, models are finetuned with identical hyperparameters, which are detailed below:
T5 and Flan-T5 finetuned with learning rate le-3, linear learning rate schedule, weight decay 0.01, and a batch size of 32.
Mistral is finetuned using LoRa (Hu et al. 2021) with flash attention. For LoRa, we use LoRa rank 64, LoRa alpha 16, LoRa dropout 0.05. Additionally, we use maximum learning rate of 1e-4, minimum learning rate 2c-5, learning rate gamma 0.8, a cosine annealing learning rate schedule, weight decay ,01, and batch size of 32, with 10 warmup steps and 50 cycle steps.
G Full Finetuning Results
Raw scores for the Toxicity and Sentiment tasks shown in Table 7a and entropy values for the masked span prediction task are shown in
while perplexity Table 7b
Model
Training Variant
T5
Flan
Training
Toxic FPR %
Neg Sentiment %
Perp
Ent
Perp
Ent
Variant
T5
Flan
Mistral
T5
Flan
Mistral
Base
3.6e7 1.82
1.9e5 1.65
Base
34.8
34.0
66.8
11.2
11.3
27.4
Random
7.488
1.63
7.8e4 2.09
Random
35.7
35.4
64.1
13.4
11.3
26.3
VALUE
3.6e7 2.05
9.1e4 1.92
VALUE
35.2
33.4
67.3
11.2
13.4
23.9
+PHONATE
7.7e7
1.98
4.9e4 2.04
PHONATE
32.7
31.6
65.0
10.1
9,9
22.2
(b) Raw perplexity and 1op-5-entron scores on the TwitterAAF (Blodgett al.] 2018) corpus for T5 and Flan- 15 finetuning variants.
(a) False Positive Rates (FPR) and rate of negative sentiment predictions on the TwitterAAE (Blodgett al. 2018) corpus for different finetuning variants.
Table Raw synthetic finetuning results for the Toxicity and Sentiment tasks (a) and Masked Span Prediction task (b).
18

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Published as a conference paper at COLM 2024
H Full Robustness Results
Figure 5 and Figure present the robustness results including the raw scores on the original texts for each subset. As the percentages for each feature are calculated based on the subset of texts where a given feature may be applied, there is slight variance in the scores on the original and VALUE-augmented texts.
and VALUE-augmente
Original
VALUE
PHONATE
T5
Flan- T5
Toxicity FPR %
3
19
Cousonant Reduction
th-sabstitutie itttins
Final Devoicing
Monop Monophthongian iithhngizttion
Haplology
g-dropping
r-lessneso
str bbbkiing
Stress- Drpping I-lessnesg
Consonant Reduction
Final Devoicing
e-dropping
Haplology
Stress Dropping
r- l-Jesuees
str- brebbokingg L.lesaness
Monophthongizan
Mistral
ChatGPT
100
28
90
Toxicit FPR %
24
80
20
70
60
16
50
12
40
8
30
20
4
10
Cousonant Reduction
str-backing bhbckiing
Stress- Droppiug
Haplology
Final Devoicing
Hesssess
Monopht womphhhhonttann toongiztiion
r-lessness
drropping
Ah-substitune itutionss
Streess Stress- Dropping
g-dropping
Lessmess
Haplology
th-substituti stubbstttuion lons
Consomant Reduction
i-Jessness
Monop ptt hongiooizttinn ingizattion Final Devoiciug
str- beckacking
Type-written Phonological Feature
Figure 5: Rate of toxic predictions by type-written phonological feature of the WME- finetuned 15, Flan-15, and Mistral models as well as ChatGPT on PHONATE-augmented non-toxic samples from the DWMW17 and Jigsaw datasets. FPR %s are shown as raw percentages for each data subset. Features (y-axis) are sorted by largest FPR on PHONATE: augmented data.
19

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Published as a conference paper at COLM 2024
Original
VALUE
PHONATE
T5
Flan- 15
Nogative/Noo /Neutral Sentiment
40
40
35
35
30
30
25
25
20
20
15
15
10
5
Monophthongizn conephthongizatin
Strees Dropping
Haplology
Consonant Reduction
str- backing
th-aubstitation itttion
Final Devoleing
lesessnes
Hessmess
g-dropping
Monophthongizen iittioon
Consonant Reduction
Haplologs loogy stx backing
Final Devolcing
Strees- Dropping Llessness
r-lessnsso
th Ah-subatitation g-dropping
Negative/ Neaive///taalll Sentiment %
Mistral
ChalGPT
40
40
35
35
30
30
25
25
20
15
15
10
5
Hap Haplology
str- babkiinn
Stress Dropping
dh-subetitug inutstitutions
Finel Devoicing
Consonaut Reduction
r- Aeesnnss
Ielessessss
g-dropping
Final Devoicing Sirees- Dropping
Haplology
Consonaut Reduction
r- --lesuesss
g-dropping str- backiig I- I-lessncis
Monophthougisate
Type-written written Phonological Feature
Figure 6: Rate of negative or neutral sentiment predictions by type-written phonological feature of the WME-finetuned T5, Flan-T5, and Mistral models as well ChatGPT on PhoNATE-augmented non-toxic samples from the TweetEval sentiment dataset. Nega- %s are shown as raw percentages for each data subset. Features (y-axis) are sorted by largest Negative/Ner Sentiment % on PHON aaggmnemed data.
20


Reviews:
[{"review_id": "S661sfMQG7", "reviewer": "Reviewer_7yF2", "summary": "The study introduces Phonological Augmentations for Text (PhonATe), a method that converts between graphemes and phonemes, as well as rule-based phoneme transformations derived from sociolinguistic literature. By augmenting datasets with AAL phonological features, the researchers aim to understand how these features influence model predictions in tasks such as toxicity detection and sentiment analysis. The study also discusses the potential implications of incorporating AAL features into language models for bias mitigation and highlights the importance of developing more inclusive and accurate models for diverse linguistic contexts.", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your review.\n\n__Reason to Reject 1:__ _While the paper states that fine-tuning on data augmented with synthetic AAL features is not intended as an approach to mitigate biases, there may be ambiguity in how the fine-tuning process was conducted and its implications on model behavior. Clearer explanations or guidelines on the fine-tuning methodology and its impact on model performance are necessary for transparency and reproducibility._\n\nAll model checkpoints, prompts, generation hyperparameters, and fine-tuning hyperparameters are described in Appendices D-F (we direct readers to the Appendix in section 3.4). Thus, we include sufficient details for reproducing our experiments, and plan to release our code. If there are particular details the reviewer feels are missing, we will include them in the revision.\n\n__Reason to Reject 2:__ _The paper emphasizes the importance of centering AAL speakers in decision-making processes . However, there is a lack of clarity on how the research engages with or involves the AAL community in the development and evaluation of the PhonATe approach. Involving relevant communities in the research process can provide valuable insights and ensure that the study is culturally sensitive and beneficial to those it aims to represent._\n\nThe development of PhonATe is drawn from sociolinguistic studies of AAL conducted with AAL-speakers. Some of the authors of this work are from the AAL community, including sociolinguistics researchers. Additionally, we note in the paper that the PhonATe evaluation is conducted by 2 AAL-speakers (“Augmentation Quality” on pg.4-5). We will further elaborate on how the AAL community was involved in this work in the Ethics statement.\n\n__Reason to Reject 3:__ _... Considering the sensitive nature of the tasks evaluated in the study (toxicity detection, sentiment analysis), there should be a thorough discussion on how potential biases in annotation or data collection were addressed to ensure the reliability and fairness of the results._\n\nWe do not collect any data and instead rely on existing fine-tuning corpora. To mitigate the impacts of annotators beliefs and avoid relying on potentially biased AAL annotations, we use the demographic-alignment classifier introduced by Blodgett et al (2016) to filter out all texts  from the fine-tuning datasets predicted to reflect AAL given that they may have biased annotations (Paragraph 1 of Data section). We will elaborate further on this in the revision.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The paper introduces a novel approach, PhonATe, to synthetically introduce type-written phonological features of African American Language (AAL) into text, addressing a gap in prior research . This innovative method has the potential to enhance understanding of model behavior with AAL text and inform bias mitigation strategies in language models.\n\nThe paper demonstrates a commitment to openness, transparency, and ethical research practices in exploring the impact of AAL features on language model performance.\n\nBy focusing on type-written phonological features of AAL and their impact on model predictions, the study provides valuable insights that can inform the development of more inclusive and accurate language models.", "reasons_to_reject": "While the paper states that fine-tuning on data augmented with synthetic AAL features is not intended as an approach to mitigate biases , there may be ambiguity in how the fine-tuning process was conducted and its implications on model behavior. Clearer explanations or guidelines on the fine-tuning methodology and its impact on model performance are necessary for transparency and reproducibility.\n\nThe paper emphasizes the importance of centering AAL speakers in decision-making processes . However, there is a lack of clarity on how the research engages with or involves the AAL community in the development and evaluation of the PhonATe approach. Involving relevant communities in the research process can provide valuable insights and ensure that the study is culturally sensitive and beneficial to those it aims to represent.\n\nPrevious research has highlighted how annotator beliefs and identities can bias toxic language detection tasks . Considering the sensitive nature of the tasks evaluated in the study (toxicity detection, sentiment analysis), there should be a thorough discussion on how potential biases in annotation or data collection were addressed to ensure the reliability and fairness of the results.", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "6bssW2uvfk", "reviewer": "Reviewer_JF8K", "summary": "The authors study if introducing synthetic data mimicking African-American English dialects into model training can narrow the gap in performance on AAE evaluation sets. They first develop a framework to transform text into AAE based on typed phonological features of AAE derived from sociolinguistics literature. They then present results showing that finetuning on this synthetic language lowers false positive rates in toxicity detection with AAE and leads to fewer model predictions of negative sentiment on AAE text. They also conduct a masked span prediction experiment where they find mixed results in terms of perplexity after synthetic AAE finetuning, though it can lower perplexity in some cases.", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your review.\n\n__Reason to Reject 1:__ _The writing is unclear in many places and should be revised before publication…_\n\nThank you for pointing out places that could clarified. Each annotator is asked to rate 50 augmented and natural texts such that 40 of each overlap. For these 40, the ratings are averaged. Concerning data subsets, we sample 5,000 texts with non-toxic labels from the toxicity datasets as well as 5,000 texts with positive labels from the sentiment dataset. We will update the descriptions to clarify.\n\n__Reason to Reject 2:__ _The sentiment detection eval seems very odd, given that there is no ground-truth for the sentiment…_\n\nWe directly measure the percentage of cases where the sentiment prediction does change and thus, we measure bias via this process. Prior work on other biases in sentiment detection similarly use differences in sentiment predictions (e.g., Goldfarb-Tarrant et al., 2023). We will more explicitly state why we consider this behavior biased and would be open to suggestions on improving the experiment.\n\n__Reason to Reject 3:__ _The paper never really motivates the use of synthetic AAE instead of finetuning on a larger corpus of real AAE_\n\nWe note in the beginning of the Introduction that AAL has minimal representation in common datasets. While the TwitterAAE corpus is large, our aim was to better isolate the impacts of specific phonological features (e.g. the robustness experiments). We will more thoroughly motivate using synthetic data in our controlled experiments in the revision.\n\n__Question 1:__ _What was the error rate in the phonological transformations?_\n\nWith multiple ways an author may spell a term reflecting phonological features, there are not necessarily correct or incorrect augmentations in all cases. Instead, we evaluate how natural the augmentations appear to AAL-speakers (percentages shown in Table 4).\n\n__Question 2:__ _Why do you think finetuning works for lowering perplexity with Flan-T5 and not T5?_\n\nAs we cannot reliably mask only AAL features, model losses rise for some spans without features. For T5 in particular, a small set of samples have a large rise, but the additional pre-training of Flan-T5 may mitigate these effects. We will expand our Results and Limitations sections to discuss this behavior and the limitation of random masking.\n\n__Question 3:__ _Figures 2/3 are hard to decipher_\n\nThank you for bringing this to our attention. We will make the figures clearer in the revision.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- I found this to be an interesting paper, which tackles a relatively known problem in NLP (lack of robustness to AAE) with an approach that is novel within this area (synthetically introducing phonological features). This is a important area of AI ethics, with recent work finding widespread LLM discrimination against AAE speakers, even leading to a higher likelihood of AAE speakers being sentenced to death by LLM judges [1]. \n\n- The authors do an excellent job in both the main body of the paper, limitations and ethics statement to address potential concerns around mimicking actual AAE speakers. I thought the ethics statement in particular was very thoughtful, calling for representation of affected communities in LLM application development. \n\n- They analyze performance across multiple tasks and models. While I found there to be flaws in evaluation (see reasons to reject), I did think that their breakdown of effects on task performance from each of the phonological features was thorough, and potentially useful for informing future research on safe/non-discriminatory deployment of LLMs. \n\n- I appreciated that they conduct a human evaluation to verify the quality of synthetic data with AAE speakers, which absolutely should be done in a study like this \n\n[1] Hofmann, Valentin, Pratyusha Kalluri, Dan Jurafsky and Sharese King. “Dialect prejudice predicts AI decisions about people's character, employability, and criminality.” ArXiv abs/2403.00742 (2024).", "reasons_to_reject": "- The writing is unclear in many places and should be revised before publication. For example, in the section on augmentation quality it states “both annotators are self-identified AAL speakers,” but it isn't stated explicitly that there are two annotators judging each example and how the final rating is derived. The description of the data subsets used on pg 6 (\"5000 non-toxic texts and 5000 texts for the sentiment dataset\") is also confusing. \n\n- The sentiment detection eval seems very odd, given that there is no ground-truth for the sentiment. Presumably if an example is labeled positive in WME, it shouldn't become negative just from a transformation into AAE, however this experiment could be conducted in a cleaner way to make it clear the outcome is a result of bias. \n\n-   The paper never really motivates the use of synthetic AAE instead of finetuning on a larger corpus of real AAE", "questions_to_authors": "1. What was the error rate in the phonological transformations?\n\n2. Why do you think finetuning works for lowering perplexity with Flan-T5 and not T5?\n\n3. Figures 2/3 are hard to decipher", "extra_scores": {}}, {"review_id": "aH1HwdebiF", "reviewer": "Reviewer_XUsW", "summary": "The paper presents an approach to enhance the performance of LLMs on texts with African American Language (AAL) features particularly focusing on type-written phonological features. They introduce an approach of generating synthetic typewritten phonological features that generates text with phonological transformations from a standard text. The paper investigates how injecting this synthetic AAL text into training datasets impacts the performance of LLMs on tasks such as toxicity detection, sentiment analysis, and masked span prediction.", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your review.\n\n__Reason to Reject 1:__ _Perhaps the authors should have explained better why these phonological features make their way to text, no where in the paper transcription is mentioned, so I assume (from the cited papers) most of the time people write these phonologically transformed text on social media._\n\nThe reviewer is correct that most of the time these type-written phonological features are written by individuals and primarily occur on social media (paragraph 3 of Introduction). Our work focuses on the use of these features in written posts and does not use speech transcription. To our knowledge, there is little sociolinguistics research into why the spoken phonological features may affect orthography (paragraph 1 of Limitations), and we believe studying these reasons is outside of the scope of this work. We can, however, make more explicit that this research gap exists and emphasize that these features are primarily written by users on social media.\n\n__Reason to Reject 2:__ _I understand that the authors have resource constraints but the two main LLMs used in the experiments are quite old and even Mistral is considered a small model. One cannot generalize these results and assume larger language models would behave similarly. Apart from that, I really think this paper fits better in a conference like *CL as the linguistic content is quite rich but the LLM part is quite limited._\n\nThank you for your suggestion. We are happy to evaluate a larger LLM in our robustness experiments. Additionally, we note that the scope of COLM is not just restricted to LLMs but language modeling more broadly, and we believe that the ethics and linguistics focus of our work falls within the Societal implications and LMs for everyone topics listed in the Call for Papers.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The paper is very well written and the task is clearly explained. The idea of extending LLM data with synthetic ones is very interesting and I think this is an approach that could be extended to other related areas.The ablation study clearly shows what contributes to the improvements.", "reasons_to_reject": "Perhaps the authors should have explained better why these phonological features make their way to text, no where in the paper transcription is mentioned, so I assume (from the cited papers) most of the time people write these phonologically transformed text on social media.\nI understand that the authors have resource constraints but the two main LLMs used in the experiments are quite old and even Mistral is considered a small model. One cannot generalize these results and assume larger language models would behave similarly.  \nApart from that, I really think this paper fits better in a conference like *CL as the linguistic content is quite rich but the LLM part is quite limited.", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This work aims to improve known issues of biases based on African American Language text in areas such as sentiment and toxicity (they also include masked span prediction) via synthetic data augmentation that transforms SAE text into AAL text. 

The reviewers bring up a number of concerns around presentation and clarity, that I agree with -- the graphs could be easier to parse, arguably some of the tables should be graphs, e.g. Fig 1 is a table despite being called a figure and in all cases it should be more clearly indicated what the metrics are and whether higher or lower is better.
There are insufficient methodological details regarding fine tuning, which harms transparency and replicability, please add those at least to the appendix.

However, these are quite minor changes (though important) and can easily be done before publication in another revision, which I recommend that the authors do. none of these complaints are about soundness of scientific rigor.

Given that the work is sound, I believe there is real value in this approach to the research community, which has struggled with what to do about dialects beyond SAE for some time and has really done very little about it. Synthetic data augmentation is a commonly used approach in LLM post training, and this is an interesting linguistically motivated one that is impelementable and works quite well at addressing a real problem. 

The only remaining concerns about publication from the reviews are wanting them to have used models larger than Mistral and Chat GPT, which is frankly a ridiculous complaint, everyone does experiments on smaller models and they are of sufficient size to generalise. And a few concerns about validity of using sentiment with no ground truth labels (which the authors address in a rebuttal) and the engagement with the AAL community, which they also address. So I am discounting these concerns as solved.
Decision: Accept