Voices in a Crowd: Searching for clusters of unique perspectives (2024)

Abstract: Language models have been shown to reproduce underlying biases existing in their training data, which is the majority perspective by default. Proposed solutions aim to capture minority perspectives by either modelling annotator disagreements or grouping annotators based on shared metadata, both of which face significant challenges. We propose a framework that trains models without encoding annotator metadata, extracts latent embeddings informed by annotator behaviour, and creates clusters of similar opinions, that we refer to as voices. Resulting clusters are validated post-hoc via internal and external quantitative metrics, as well a qualitative analysis to identify the type of voice that each cluster represents. Our results demonstrate the strong generalisation capability of our framework, indicated by resulting clusters being adequately robust, while also capturing minority perspectives based on different demographic factors throughout two distinct datasets.

## introduction supervised training is rooted in the presupposition that every example in a dataset has a single ground truth, also known as the gold label @xcite . however, disagreement among dataset annotators challenges the notion that a single, per-example, ground truth exists @xcite . while disagreement can be indicative of task difficulty or semantic ambiguity @xcite @xcite , it can also indicate the existence of both stable and conflicting inter-annotator perspectives @xcite . nevertheless, capturing minority perspectives present in the data, which we parallel to voices † equal contribution 1 all code is made available at https://github.com/ ni-vi/cluster . in a crowd, has proven challenging. two main approaches attempt to move beyond gold labels: i) disagreement-based which leverage annotator disagreement to provide distributional per-item prediction labels @xcite , and ii) metadata-based, which encode annotator metadata to boost the signal from voices with the same metadata labels @xcite @xcite (i and ii respectively in figure 1 ). however, both approaches come with strong vulnerabilities. disagreement-based approaches collapse multiple minority voices into a singular, peritem, minority-majority distribution @xcite , essentially limiting the number of expressed voices to the number of predicted labels (i.e., two voices in a binary prediction task). on the other hand, while metadata-based approaches allow for multiple minority voices to be expressed (albeit limited by metadata collected), they are based on the erroneous assumption that most members that share metadata labels (e.g., gendered females) will also exhibit similar patterns of behaviour @xcite . we introduce a framework that addresses both issues (figure 1iii ): it forms multiple clusters of distinct voices solely based on annotator behaviours exhibited during the annotation process in an unsupervised manner. our pipeline trains models to predict each annotation made by each annotator for a given text input. the final hidden states form what we refer to as behavioural embeddings, representing how a given annotator will behave when shown that text sample. these are then clustered via unsupervised methods, with each cluster formed being a potential voice-a group perspective of annotators with similar annotating behaviours. we apply our framework to two datasets related to political bias that have been found to contain multiple heterogeneous and conflicting perspectives @xcite @xcite . to identify the group whose voice each cluster belongs figure 1 : different approaches for handling annotations: i) disagreement-based create per-example distributional labels which fail to account for dataset-level effects; ii) metadata-based train models on annotations linked with annotator metadata, which often groups disagreeing annotators who share metadata labels; iii) the "voices in a crowd" approach dynamically creates clusters based on annotation patterns and finally verifies each cluster as a voice based on post-hoc matched metadata labels. to, we match each data point with annotator metadata post-hoc while we also conduct an in-depth qualitative analysis of the clusters themselves. the resulting clusters show high internal label consistency of either i) dataset majority labels (e.g., left-leaning in a left-leaning majority dataset), ii) dataset minority labels (e.g., right-leaning in a left-leaning majority dataset), but most importantly their intersection resulting in iii) inter-minority labels (e.g., right-leaning and highly educated, in a left-leaning, non-highly educated majority dataset). we are the first to dynamically identify voices of minority opinions within larger majority/minority groups, highlighting the significance of providing an intersectional understanding of annotators that goes beyond current grouping methodologies. to be more specific, disagreement-based solutions essentially limit the number of possible expressed voices into the number of predicted labels; the upper bound of possible voices expressed in a binary task is always two, no matter how diverse the dataset. unfortunately, this type of aggregation leads to the erasure of what we define as interminority voices: stable opinions held by minority groups that are in conflict with each other as well as the majority, across examples. while there are some disagreement-based approaches that attempt a more nuanced expression of varied voices present in the data @xcite @xcite , there is still a distinct lack of a holistic framework-such as ours-that both clusters and explains the type of voice expressed. ## experimental setup our framework comprises a supervised and an unsupervised component. the former produces latent embeddings informed by both text and annotating behaviour that the latter uses to cluster into voices. being the first such approach, we compared performance across a variety of transformer-based architectures, clustering, and dimensionality reduction techniques to identify optimal combinations. the supervised component explores several modelling choices (section 4) fine-tuned on each dataset to predict each annotator's individual annotation for a given example without providing any annotator metadata that could bias the model @xcite . the unsupervised component then performs dimensionality reduction on the behavioural embeddings-the final hidden states from the supervised component-and finally cre-ates clusters via several unsupervised algorithms (section 5). clusters are evaluated through internal (i.e., intra-cluster similarity) and external metrics (i.e., consistency of demographic labels in a given cluster), and via qualitative analysis of the bestperforming combination of components. ## supervised component each of the following modelling architectures was trained through a different combination of inputs (visual representation in appendix b): given a text sample in a dataset, @xmath0, we predict the individual annotation of each annotator p θ (y|x) where @xmath1, . . . , y k ) and k is the total number of unique annotators within the dataset. unpooled cross attention uses a pretrained t5 encoder @xcite where the encoded text and the embedded annotator unique identifiers are fed through a decoder to predict each annotator's annotation as a sequence. annotator embeddings are directly informed by the text via a crossattention layer aiming to capture the influence of the text in the annotators' behaviours. pooled cross attention follows @xcite , which showed strong performance in predicting annotator disagreement in the 2023 learning with disagreements shared task (lewidi; @xcite . this model is similar in structure to unpooled cross attention since it also uses a t5 encoder as the backbone. however, the dimension for each encoded text token is downsampled, as previous research has indicated possible benefits in the salience of encoded features @xcite @xcite . finally, decoder outputs are pooled @xcite to predict an aggregated annotation for each batch. encoder-encoder treats text and annotators as separate modalities, inspired by multimodal approaches @xcite @xcite . the encoded text (using t5) and embedded annotator ids are concatenated and fed through a bidirectional encoder to predict the annotation of each annotator, allowing for interaction between text and annotator embeddings. classifier model simply concatenates the text with the unique annotator identifier, before passing to an encoder (bert; @xcite for mbic) to predict each annotation label independently. the independence between annotators limits interaction between annotators during training. pretrained decoder is a decoder-only gpt-2 model @xcite prompted with the concatenated text and annotator identifiers in the form "<@xmath2... <ann @xmath3. pretrained encoder-decoder similarly to unpooled cross attention. it uses a pretrained t5 encoder-decoder where unique annotator identifiers are embedded through the decoder-instead of a decoder trained from scratch-to predict each annotator's annotation autoregressively. since the decoder is unidirectional, it forces causal attention across annotators in their canonical order. metrics we compute f1 score to measure the accuracy of predictions, and average pairwise cosine similarity (apcs) between hidden states of predicted annotations to illustrate how dense the latent states are by the end of training; we show that lower scores generally correlate with better clustering performance (see section 5.1). results clustering algorithms we used three clustering techniques: k-means @xcite , gaussian mixture models (gmm; @xcite , and hdbscan @xcite . each of these techniques have been used to cluster features when paired with either pca (asyaky and mandala, 2021; @xcite , or umap @xcite . metrics we use two internal validation metrics to assess average similarity scores between clusters, namely silhouette @xcite and davies-bouldin index @xcite . silhouette assesses intra-cluster separation and is bound between -1 and 1, with 1 being the best possible score, with a threshold of 0.5 for moderate clusters @xcite . the davies-bouldin index measures intra-cluster dissimilarity, with 0 indicating the lowest possible score @xcite . we use purity to assess the external validity of clusters. purity measures the internal consistency of assigned labels within a cluster and evaluates whether a cluster is prototypical (i.e., representative) across provided labels within a dataset @xcite . in our case, we report both average purity and the percentage of prototypical clusters per method. we define a cluster as prototypical if its metadata label purity (i.e., political leaning and education level) is significantly different from the original dataset metadata label distribution with a threshold @xmath4. these metrics allow us to automatically assess whether a cluster emerging from annotator behaviours during training is linked to any of the annotator labels (e.g., a cluster with high right-leaning metadata label purity) and thus is indicative of a distinct voice. ## conclusion we propose a novel framework to identify underlying minority perspectives in data. we compared six distinct model architectures trained on a classification task, without providing any annotator metadata to avoid biasing their training. subsequently, final hidden states were passed through various methods of dimensionality reduction (umap and pca), with the resulting embeddings used to create clusters through various unsupervised algorithms (k-means, gmm, and hdbscan). the resulting clusters were adequately separated according to internal and external validation metrics. further qualitative analysis of clusters produced by our best-performing model showcased the ability of our framework to capture perspectives as shown by three distinct types: clusters representative of a minority, a majority, and clusters that captured multiple minority labels, i.e., a minority within a minority.