tum-nlp/gpt-2-medium-target-aware-counterspeech-generation

Target-Aware Counter-Speech Generation

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The target-aware counter-speech generation model is an autoregressive generative language model fine-tuned on hate- and counter-speech pairs from the CONAN datasets for generating more contextually relevant counter-speech, based on the gpt2-medium model. The model utilizes special tokens that embedded target demographic information to guide the generation towards more relevant responses, avoiding off-topic and generic responses. The model is trained on 8 target demographics, including Migrants, People of Color (POC), LGBT+, Muslims, Women, Jews, Disabled, and Other.

Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> The model is intended for generating counter-speech responses for a given hate speech sequence, combined with special tokens for target-demographic embeddings.

Bias, Risks, and Limitations

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We observed negative effects such as content hallucination and toxic response generation. Though the intended use is to generate counter-speech for combating online hatred, the usage is to be monitored carefully with human post-editing or approval system, ensuring safe and inclusive online environment.

How to Get Started with the Model

Use the code below to get started with the model.

types = ["MIGRANTS", "POC", "LGBT+", "MUSLIMS", "WOMEN", "JEWS", "other", "DISABLED"] # A list of all available target-demographic tokens from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.frompretrained(tum-nlp/gpt-2-medium-target-aware-counterspeech-generation) tokenizer = AutoTokenizer.frompretrained(tum-nlp/gpt-2-medium-target-aware-counterspeech-generation) tokenizer.padding_side = "left"

prompt = "<|endoftext|> <other> Hate-speech: Human are not created equal, some are born lesser. Counter-speech: " input = tokenizer(prompt, returntensors="pt", padding=True) outputsequences = model.generate( inputids=inputs['inputids'].to(model.device), attentionmask=inputs['attentionmask'].to(model.device), padtokenid=tokenizer.eostokenid, maxlength=128, numbeams=3, norepeatngramsize=3, numreturnsequences=1, earlystopping=True ) result = tokenizer.decode(outputsequences, skipspecial_tokens=True)

Training Hyperparameters

trainingargs = TrainingArguments( numtrainepochs=20, learningrate=3.800568576836524e-05, weightdecay=0.050977894796868116, warmupratio=0.10816909354342182, optim="adamwtorch", lrschedulertype="cosine", evaluationstrategy="epoch", savestrategy="epoch", savetotallimit=3, loadbestmodelatend=True, autofindbatchsize=True, )

Evaluation

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Testing Data, Factors & Metrics

Testing Data

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The model's performance is tested on three test sets, from which two are subsets of the CONAN dataset and one is the sexist portion of the EDOS dataset

Metrics

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The model's performance is tested on a custom evaluation pipeline for counter-speech generation. The pipeline includes CoLA, Toxicity, Hatefulness, Offensiveness, Label and Context Similarity, Validity as Counter-Speech, Repetition Rate, target-demographic F1 and the Arithmetic Mean

Results

CONAN | Model Name | CoLA |TOX | Hate | OFF | L Sim | C Sim | VaCS | RR | F1 | AM | | ---------- | ---- | -- | ---- | --- | ----- | ----- | ---- | -- | -- | -- | | Human | 0.937 | 0.955 | 1.000 | 0.997 | - | 0.751 | 0.980 | 0.861 | 0.885 | 0.929 | | target-aware gpt2-medium | 0.958 | 0.946 | 1.000 | 0.996 | 0.706 | 0.784 | 0.946 | 0.419 | 0.880 | 0.848 |

CONAN SMALL | Model Name | CoLA |TOX | Hate | OFF | L Sim | C Sim | VaCS | RR | F1 | AM | | ---------- | ---- | -- | ---- | --- | ----- | ----- | ---- | -- | -- | -- | | Human | 0.963 | 0.956 | 1.000 | 1.000 | 1.000 | 0.768 | 0.988 | 0.995 | 0.868 | 0.949 | | target-aware gpt2-medium | 0.975 | 0.931 | 1.000 | 1.000 | 0.728 | 0.783 | 0.888 | 0.911 | 0.792 | 0.890 |

EDOS | Model Name | CoLA |TOX | Hate | OFF | C Sim | VaCS | RR | F1 | AM | | ---------- | ---- | -- | ---- | --- | ----- | ---- | -- | -- | -- | | target-aware gpt2-medium | 0.930 | 0.815 | 0.999 | 0.975 | 0.689 | 0.857 | 0.518 | 0.747 | 0.816|