# Self-Anchored Attention Model for Sample-Efficient Classification of Prosocial Text Chat

arXiv: https://arxiv.org/abs/2506.09259 · HTML: https://mlsanity.com/paper/2506.09259/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 71 references that are in the map.
- First posted on arXiv 10 Jun 2025.

## Builds on (most cited first, 17 of 17)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgetting](https://mlsanity.com/paper/2004.12651/) (2020)
- [CERT: Contrastive Self-supervised Learning for Language Understanding](https://mlsanity.com/paper/2005.12766/) (2020)
- [Efficient Few-Shot Learning Without Prompts](https://mlsanity.com/paper/2209.11055/) (2022)
- [Self-Detoxifying Language Models via Toxification Reversal](https://mlsanity.com/paper/2310.09573/) (2023)
- [Few-shot Instruction Prompts for Pretrained Language Models to Detect Social Biases](https://mlsanity.com/paper/2112.07868/) (2021)
- [ChatGPT Based Data Augmentation for Improved Parameter-Efficient Debiasing of LLMs](https://mlsanity.com/paper/2402.11764/) (2024)
- [BiasTestGPT: Using ChatGPT for Social Bias Testing of Language Models](https://mlsanity.com/paper/2302.07371/) (2023)
- [Can You Label Less by Using Out-of-Domain Data? Active & Transfer Learning with Few-shot Instructions](https://mlsanity.com/paper/2211.11798/) (2022)
- [Anchor-based Bilingual Word Embeddings for Low-Resource Languages](https://mlsanity.com/paper/2010.12627/) (2020)
- [LLM Confidence Evaluation Measures in Zero-Shot CSS Classification](https://mlsanity.com/paper/2410.13047/) (2024)
- [Augmenting and Tuning Knowledge Graph Embeddings](https://mlsanity.com/paper/1907.01068/) (2019)
- [One For All & All For One: Bypassing Hyperparameter Tuning with Model Averaging For Cross-Lingual Transfer](https://mlsanity.com/paper/2310.10532/) (2023)
- [QUEST: Queue Simulation for Content Moderation at Scale](https://mlsanity.com/paper/2103.16816/) (2021)

## Built on by (most cited first, 1 of 1)

- [Prosocial Behavior Detection in Player Game Chat: From Aligning Human-AI Definitions to Efficient Annotation at Scale](https://mlsanity.com/paper/2508.05938/) (2025)

## Abstract (arXiv)

Millions of players engage daily in competitive online games, communicating through in-game chat. Prior research has focused on detecting relatively small volumes of toxic content using various Natural Language Processing (NLP) techniques for the purpose of moderation. However, recent studies emphasize the importance of detecting prosocial communication, which can be as crucial as identifying toxic interactions. Recognizing prosocial behavior allows for its analysis, rewarding, and promotion. Unlike toxicity, there are limited datasets, models, and resources for identifying prosocial behaviors in game-chat text. In this work, we employed unsupervised discovery combined with game domain expert collaboration to identify and categorize prosocial player behaviors from game chat. We further propose a novel Self-Anchored Attention Model (SAAM) which gives 7.9% improvement compared to the best existing technique. The approach utilizes the entire training set as "anchors" to help improve model performance under the scarcity of training data. This approach led to the development of the first automated system for classifying prosocial behaviors in in-game chats, particularly given the low-resource settings where large-scale labeled data is not available. Our methodology was applied to one of the most popular online gaming titles - Call of Duty(R): Modern Warfare(R)II, showcasing its effectiveness. This research is novel in applying NLP techniques to discover and classify prosocial behaviors in player in-game chat communication. It can help shift the focus of moderation from solely penalizing toxicity to actively encouraging positive interactions on online platforms.