LLMs and Finetuning: Benchmarking cross-domain performance for hate speech detection

• URL: http://arxiv.org/abs/2310.18964v2
• Date: Sat, 30 Mar 2024 15:01:08 GMT
• Title: LLMs and Finetuning: Benchmarking cross-domain performance for hate speech detection
• Authors: Ahmad Nasir, Aadish Sharma, Kokil Jaidka,
• Abstract summary: This study investigates the effectiveness and adaptability of pre-trained and fine-tuned Large Language Models (LLMs) in identifying hate speech. LLMs offer a huge advantage over the state-of-the-art even without pretraining. We conclude with a vision for the future of hate speech detection, emphasizing cross-domain generalizability.
• Score: 10.014248704653
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the evolving landscape of online communication, hate speech detection remains a formidable challenge, further compounded by the diversity of digital platforms. This study investigates the effectiveness and adaptability of pre-trained and fine-tuned Large Language Models (LLMs) in identifying hate speech, to address two central questions: (1) To what extent does the model performance depend on the fine-tuning and training parameters?, (2) To what extent do models generalize to cross-domain hate speech detection? and (3) What are the specific features of the datasets or models that influence the generalization potential? The experiment shows that LLMs offer a huge advantage over the state-of-the-art even without pretraining. To answer (1) we analyze 36 in-domain classifiers comprising LLaMA, Vicuna, and their variations in pre-trained and fine-tuned states across nine publicly available datasets that span a wide range of platforms and discussion forums. To answer (2), we assessed the performance of 288 out-of-domain classifiers for a given end-domain dataset. In answer to (3), ordinary least squares analyses suggest that the advantage of training with fine-grained hate speech labels is greater for smaller training datasets but washed away with the increase in dataset size. We conclude with a vision for the future of hate speech detection, emphasizing cross-domain generalizability and appropriate benchmarking practices.

Related papers

• Audio Large Language Models Can Be Descriptive Speech Quality Evaluators [46.765203628127345]
  We introduce the first natural language-based speech evaluation corpus, generated from authentic human ratings. This corpus offers detailed analysis across multiple dimensions and identifies causes of quality degradation. We propose an alignment approach with LLM distillation (ALLD) to guide the audio LLM in extracting relevant information from raw speech.
  arXiv  Detail & Related papers  (2025-01-27T22:47:51Z)
• Bridging Modalities: Enhancing Cross-Modality Hate Speech Detection with Few-Shot In-Context Learning [4.136573141724715]
  Hate speech on the internet poses a significant challenge to digital platform safety. Recent research has developed detection models tailored to specific modalities. This study conducts extensive experiments using few-shot in-context learning with large language models.
  arXiv  Detail & Related papers  (2024-10-08T01:27:12Z)
• Intent-conditioned and Non-toxic Counterspeech Generation using Multi-Task Instruction Tuning with RLAIF [14.2594830589926]
  Counterspeech, defined as a response to online hate speech, is increasingly used as a non-censorial solution. Our study introduces CoARL, a novel framework enhancing counterspeech generation by modeling the pragmatic implications underlying social biases in hateful statements. CoARL's first two phases involve sequential multi-instruction tuning, teaching the model to understand intents, reactions, and harms of offensive statements, and then learning task-specific low-rank adapter weights for generating intent-conditioned counterspeech.
  arXiv  Detail & Related papers  (2024-03-15T08:03:49Z)
• Probing Critical Learning Dynamics of PLMs for Hate Speech Detection [39.970726250810635]
  Despite widespread adoption, there is a lack of research into how various critical aspects of pretrained language models affect their performance in hate speech detection. We deep dive into comparing different pretrained models, evaluating their seed robustness, finetuning settings, and the impact of pretraining data collection time. Our analysis reveals early peaks for downstream tasks during pretraining, the limited benefit of employing a more recent pretraining corpus, and the significance of specific layers during finetuning.
  arXiv  Detail & Related papers  (2024-02-03T13:23:51Z)
• Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation via Decoupling Optimization [64.36097398869774]
  Semi-supervised learning (SSL) has been an active research topic for large-scale 3D scene understanding. The existing SSL-based methods suffer from severe training bias due to class imbalance and long-tail distributions of the point cloud data. We introduce a new decoupling optimization framework, which disentangles feature representation learning and classifier in an alternative optimization manner to shift the bias decision boundary effectively.
  arXiv  Detail & Related papers  (2024-01-13T04:16:40Z)
• Disco-Bench: A Discourse-Aware Evaluation Benchmark for Language Modelling [70.23876429382969]
  We propose a benchmark that can evaluate intra-sentence discourse properties across a diverse set of NLP tasks. Disco-Bench consists of 9 document-level testsets in the literature domain, which contain rich discourse phenomena. For linguistic analysis, we also design a diagnostic test suite that can examine whether the target models learn discourse knowledge.
  arXiv  Detail & Related papers  (2023-07-16T15:18:25Z)
• Hate Speech Detection via Dual Contrastive Learning [25.878271501274245]
  We propose a novel dual contrastive learning framework for hate speech detection. Our framework jointly optimize the self-supervised and the supervised contrastive learning loss for capturing span-level information. We conduct experiments on two publicly available English datasets, and experimental results show that the proposed model outperforms the state-of-the-art models.
  arXiv  Detail & Related papers  (2023-07-10T13:23:36Z)
• A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity [84.6421260559093]
  This study is the largest set of experiments to validate, quantify, and expose undocumented intuitions about text pretraining. Our findings indicate there does not exist a one-size-fits-all solution to filtering training data.
  arXiv  Detail & Related papers  (2023-05-22T15:57:53Z)
• Detect Hate Speech in Unseen Domains using Multi-Task Learning: A Case Study of Political Public Figures [7.52579126252489]
  We propose a new Multi-task Learning pipeline that utilizes MTL to train simultaneously across multiple hate speech datasets. We show strong results when examining generalization error in train-test splits and substantial improvements when predicting on previously unseen datasets. We also assemble a novel dataset, dubbed PubFigs, focusing on the problematic speech of American Public Political Figures.
  arXiv  Detail & Related papers  (2022-08-22T21:13:38Z)
• Improving Distortion Robustness of Self-supervised Speech Processing Tasks with Domain Adaptation [60.26511271597065]
  Speech distortions are a long-standing problem that degrades the performance of supervisely trained speech processing models. It is high time that we enhance the robustness of speech processing models to obtain good performance when encountering speech distortions.
  arXiv  Detail & Related papers  (2022-03-30T07:25:52Z)
• Deep Learning for Hate Speech Detection: A Comparative Study [54.42226495344908]
  We present here a large-scale empirical comparison of deep and shallow hate-speech detection methods. Our goal is to illuminate progress in the area, and identify strengths and weaknesses in the current state-of-the-art. In doing so we aim to provide guidance as to the use of hate-speech detection in practice, quantify the state-of-the-art, and identify future research directions.
  arXiv  Detail & Related papers  (2022-02-19T03:48:20Z)
• Addressing the Challenges of Cross-Lingual Hate Speech Detection [115.1352779982269]
  In this paper we focus on cross-lingual transfer learning to support hate speech detection in low-resource languages. We leverage cross-lingual word embeddings to train our neural network systems on the source language and apply it to the target language. We investigate the issue of label imbalance of hate speech datasets, since the high ratio of non-hate examples compared to hate examples often leads to low model performance.
  arXiv  Detail & Related papers  (2022-01-15T20:48:14Z)
• Towards Language Modelling in the Speech Domain Using Sub-word Linguistic Units [56.52704348773307]
  We propose a novel LSTM-based generative speech LM based on linguistic units including syllables and phonemes. With a limited dataset, orders of magnitude smaller than that required by contemporary generative models, our model closely approximates babbling speech. We show the effect of training with auxiliary text LMs, multitask learning objectives, and auxiliary articulatory features.
  arXiv  Detail & Related papers  (2021-10-31T22:48:30Z)
• Sub-word Level Lip Reading With Visual Attention [88.89348882036512]
  We focus on the unique challenges encountered in lip reading and propose tailored solutions. We obtain state-of-the-art results on the challenging LRS2 and LRS3 benchmarks when training on public datasets. Our best model achieves 22.6% word error rate on the LRS2 dataset, a performance unprecedented for lip reading models.
  arXiv  Detail & Related papers  (2021-10-14T17:59:57Z)
• Statistical Analysis of Perspective Scores on Hate Speech Detection [7.447951461558536]
  State-of-the-art hate speech classifiers are efficient only when tested on the data with the same feature distribution as training data. In such a diverse data distribution relying on low level features is the main cause of deficiency due to natural bias in data. We show that, different hate speech datasets are very similar when it comes to extract their Perspective Scores.
  arXiv  Detail & Related papers  (2021-06-22T17:17:35Z)
• Deep F-measure Maximization for End-to-End Speech Understanding [52.36496114728355]
  We propose a differentiable approximation to the F-measure and train the network with this objective using standard backpropagation. We perform experiments on two standard fairness datasets, Adult, Communities and Crime, and also on speech-to-intent detection on the ATIS dataset and speech-to-image concept classification on the Speech-COCO dataset. In all four of these tasks, F-measure results in improved micro-F1 scores, with absolute improvements of up to 8% absolute, as compared to models trained with the cross-entropy loss function.
  arXiv  Detail & Related papers  (2020-08-08T03:02:27Z)
• Phoneme Boundary Detection using Learnable Segmental Features [31.203969460341817]
  Phoneme boundary detection plays an essential first step for a variety of speech processing applications. We propose a neural architecture coupled with a parameterized structured loss function to learn segmental representations for the task of phoneme boundary detection.
  arXiv  Detail & Related papers  (2020-02-11T14:03:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.