High-quality argumentative information in low resources approaches improve counter-narrative generation (2023)

Abstract: It has been shown that high quality fine-tuning boosts the performance of language models, even if the size of the fine-tuning is small. In this work we show how highly targeted fine-tuning improves the task of hate speech counter-narrative generation in user-generated text, even for very small sizes of training (1722 counter-narratives for English and 355 for Spanish). Providing a small subset of examples focusing on single argumentative strategies, together with the argumentative analysis relevant to that strategy, yields counter-narratives that are as satisfactory as providing the whole set of counter-narratives. We also show that a good base model is required for the fine-tuning to have a positive impact. Indeed, for Spanish, the counter-narratives obtained without fine-tuning are mostly unacceptable, and, while fine-tuning improves their overall quality, the performance still remains quite unsatisfactory.

## introduction large language models (llms) have shown impressive capabilities to generate acceptable texts in a number of scenarios. this has allowed to tackle tasks that were considered too difficult until very recently. counter-narrative generation in particular is a very challenging task because it requires reasoning, argumentation and world knowledge, in addition to the ability to produce text that is grammatically correct and pragmatically felicitous. often, counter-narratives resort to pieces of information that are only indirectly implied in the text. counter-narrative generation is arising as a valuable application to mitigate some forms of violence in social media. indeed, automatically generated counter-narratives have been proposed as a primary input to facilitate the task of ngo specialists to counter hate speech @xcite . the predominant strategy adopted so far to counter hate speech in social media is to recognize, block and delete these messages and/or the users that generated it. this strategy has two main disadvantages. the first one is that blocking and deleting may prevent a hate message from spreading, but does not counter its consequences on those who were already reached by it. the second one is that it can generate overblocking or censorship. an alternative to blocking that has been gaining attention in the last years, is to "oppose hate content with counter-narratives (i.e. informed textual responses)" @xcite 1 . in this way, the consequences of errors in the hate classification are minimized, overblocking is avoided, and it helps to spread a message against hate that can reach people that are not necessarily convinced, or even not involved in the conversation. however, the huge volume of online hate messages makes the manual generation of counternarratives an impossible task. in this scenario, automating the generation of counter-narratives is an appealing avenue, but the task poses a great challenge due to the complex linguistic and communicative patterns involved in argumentation. traditional machine learning approaches have typically fallen short to address argumentation mining and generation with satisfactory results. however, large language models (llms) seem capable of generating satisfactory text for many tasks. even so, for counter-narrative generation, specific training seems to be required to obtain nuanced, effective counter-narratives. but it is unclear what specific training for this task is, and which information would have a positive effect on this task. our hypothesis is that additional information about some argumentative aspects of text will have a positive impact in the quality of the generated counter-narratives. in this paper, we show that some kinds of argumentative information of hate speech do enhance the proportion of satisfactory counter-narratives that a llm generates. we have focused on the domain of hate speech in social media, and we show that a small dataset of counter-narratives boosts performance, but even smaller subsets have visible impacts in improving performance, if they are enriched with adequate argumentative analysis. in experiments for english and spanish, we show that even with very few examples, as is the case for spanish, highly focused examples and relevant argumentative information do have a positive impact. the rest of the paper is organized as follows. in the next section, we review relevant work related to automated counter-narrative generation and argumentative analysis of hate speech. then in section 3 we describe our dataset of counter-narratives, with which we carry out the comparison of scenarios described in section 4, where we also describe extensively our approach to the evaluation of generated counter-narratives, based on human judgements, and the prompts used to obtain the counter-narratives. results analyzed in section 6 show how fine-tuned llms and argumentative information provide better results, which we illustrate with some examples. ## related work automated counter-narrative generation has been recently tackled by leveraging the rapid advances in neural natural language generation. as with most natural language generation tasks in recent years, the basic machine learning approach has been to train or fine-tune a generative neural network with examples specific to the target task. the conan dataset @xcite is, to our knowledge, the first dataset of hate speech counter-narratives. it has 4078 hate speech -counter narrative original pairs manually written by ngo operators, translated to three languages: english, french and italian. data was augmented using automatic paraphrasing and translations between languages to obtain 15024 final pairs of hate speech -counter-narrative. unfortunately, this dataset is not representative of social media. similar approaches were carried out by @xcite and @xcite . qian et al. (2019)'s dataset consists of reddit and gab conversations where mechanical turkers identified hate speech and wrote responses. ziems et al. (2020) did not produce new text, but labeled covid-19 related tweets as hate, counter-speech or neutral based on their hatefulness towards asians. in follow-up work to the seminal @xcite applied llms to assist experts in creating the corpus, with gpt-2 generating a set of counter-narratives for a given hate speech and experts editing and filtering them. @xcite iteratively refined a llm where the automatically generated counter-narratives were filtered and post-edited by experts and then fed them to the llm as further training examples to fine-tune it, in a number of iterations. @xcite apply this same approach to obtain a machine-generated dataset of dialogues between haters and experts in hate countering. as a further enhancement in the llm-based methodology, @xcite enhanced the llm assistance with a knowledge-based retrieval architecture to enrich counter-narrative generation. @xcite use llms for generation with a prompting approach, instead of finetuning them with manually created or curated examples. they also propose a methodology to evaluate the generated output, based on human evaluation of some samples. this same approach is applied by @xcite to create a dataset of counter-narratives for spanish. both these approaches are targeted to user-generated text, closely related to social media. however, none of the aforementioned datasets or approaches to counter-narrative generation includes or integrates any additional annotated information apart from the hate message, possibly its context, and its response. that is why we consider an alternative approach that aims to reach generalization not by the sheer number of examples, but by providing a richer analysis of such examples that guide the model in finding adequate generalizations. we believe that information about the argumentative structure of hate speech may be used as constraints to improve automatic counternarrative generation. chung et al. (2021a ) address an argumentative aspect of hate speech countering. they classify counter-narratives by type, using a llm, and showing that knowledge about the type of counternarratives can be successfully transferred across languages, but they do not use this information to generate counter-narratives. to our knowledge, asohmo @xcite is the only corpus where tweets of hate speech have been annotated with argumentative information, and coneas is the only dataset of counter-narratives based on the argumentative structure of hate speech. we describe both in the following section. ## experimental settings we designed a series of experiments to assess the impact of high-quality examples and argumentative information in the automatic generation of counternarratives via prompting llms. we wanted to compare the following configurations: base or xl version of llm to assess the impact of the size of the base llm, we compared the base (250m parameters) and xl (3b parameters) versions of flan-t5 @xcite . given our limited computational resources, only these two llms were compared. finetuned vs fewshot to assess the adequacy of different training strategies, we compared a fine-tuning approach and a fewshot approach. hate tweet: user must deport all illegal migrants india already reeling under constant threat of muslim radicals curb population justification: india already reeling under constant threat of muslim radicals curb population (fact) conclusion: must deport all illegal migrants (policy) collective: illegal migrants property: muslim radicals ## evaluation method for generated counter-narratives evaluation of counter-narratives is not straightforward. so far, no automatic technique has been found satisfactory for this specific purpose. as cogently argued by @xcite , superficial n-gram-based metrics like bleu @xcite or rouge @xcite fail to capture important aspects of generated texts, because they rely strongly on words or n-grams overlap with manually generated examples and they are inadequate in scenarios where there can be many possible good outputs of the model, with significant differences between themselves, such as our case. specifically for argumentative texts, hinton and wagemans (2023) show that llms produce low quality arguments that are nevertheless wellformed utterances. a specific, more nuanced evaluation seems to be necessary to assess relevant features of generated counter-narratives. despite the limitations of n-gram-overlap metrics, they are still widely used to evaluate counterspeech @xcite . but, faced with the shortcomings of these metrics, some authors have conducted manual evaluations for automatically generated counter-narratives, in line with the methods of the natural language generation community @xcite . manual evaluations typically distinguish different aspects of the adequacy of a given text as a counter-narrative for another. @xcite evaluate three aspects of the adequacy of counter-narratives: suitableness (if the counter-narrative was suited as a response to the original hate message), informativeness (how specific or generic is the response) and intra-coherence (internal coherence of the counternarrative regardless of the message it is responding to). @xcite , on the other hand, assess these three other aspects: offensive-ness, stance (towards the original tweet) and informativeness (same as @xcite ). based on these previous works, we manually evaluate the adequacy of counter-narratives in four different aspects (further detail is provided in appendix d): each aspect constitutes a likert-scale with three possible values, ranging from 1 to 3, where lower values represent the least desirable categories and vice versa. informativeness is only evaluated if stance was assigned the higher value possible. otherwise, it is considered as having a 0 value. ## analysis of results results of the manual evaluation of different strategies for counter-narrative generation for english and spanish can be seen in in the first place, we can see overall that the results for spanish are much worse than for english, specially when it comes to using the llm without fine-tuning, only with a few-shot approach. using a larger model (xl) has a slightly beneficial effect, but in any case the proportions of good and excellent counternarratives obtained by a fewshot approach for spanish are almost always null. for english, we can see that the reverse holds: the smaller model (base) obtains better results for the fewshot approach than the bigger model. providing argumentative information in this setting seems to negatively affect performance. given the poor performance of the xl model in general, and given our limited computational resources, fine-tuning was carried out only for the base model. as can be seen in the lower half of for english, in the left part of moreover, in qualitative inspection we have observed that counter-narratives produced by providing argumentative information tend to be more informative, as can be seen in figure 3 . however, our current evaluation method is not fine-grained enough to reflect these subtle differences in quality. future developments of the evaluation method will aim to capture such differences. when we fine-tune the model only with counternarratives of a given type (lower quarter of for counter-narratives of type b and c, however, improvements are not as good. for counternarratives of type b, the small number of examples may be the reason for such a small improvement in performance. for counter-narratives of type c (negation of justification), with almost as many examples as in type a narratives, the smaller ratio of improvement may be attributed to more complex argumentative structures. in any case, providing the relevant argumentative information, collective for type b and premises for type c, always produces an improvement. in fact, even when finetuning with all types of counternarratives, providing information about the collective and premises yields a improvement higher than providing all available argumentative information, but not as good as not providing any argumentative information at all. for spanish, the performance of the model without specific training is much worse than for english, even with fine-tuning, producing almost no good and excellent counter-narratives. however, when the model is fine-tuned with counter-narratives, even if the number of training examples is very small (257 in total), the improvement in the performance of the model is notable, even much higher than for the case of english. indeed, while english goes from a score of 5.70 for the base model with few shot to a score of 7.15 for fine-tuned, spanish goes from 2.1 to 6.6, obtaining a much bigger improvement and coming much closer to the performance of the model for english. the improvement is much more notable when argumentative information is provided, even reaching the score for the best approaches for english (7.4) when all argumentative information is provided. however, the proportion of good narratives remains low, and excellent is nonexistent. tweet with argumentative information: tweet without argumentative information: street interview whit italians "send all migrants back to where they came from they block streets to pray " -free speech time -:link: @user | justification: street interview whit italians "send all migrants back to where they came from they block streets to pray " (fact) | conclusion: "send all migrants back to where they came from they block streets to pray " (policy) | pivot: migrants -they -they street interview whit italians "send all migrants back to where they came from they block streets to pray " -free speech time -:link: @user counter-narrative: counter-narrative: i don't think it's a good idea to send all migrants back to where they came from. i don't think it's the right thing to do. when fine-tuned with types of counter-narratives separately, improvements are smaller, probably due to the very small number of training examples. in any case, the evaluation we carried out is small, mostly indicative, and these tendencies will be further explored in future work. ## conclusions and future work we have presented an approach to generate counternarratives against hate speech in social media by prompting large language models with information about some argumentative aspects of the original hate speech. we have shown that finetuning a smaller model with a small corpus of high-quality examples of pairs hate speech -counter-narrative yields a bigger improvement in performance than using larger language models. we have also shown that some kinds of argumentative information do have a positive impact in generating more specific, more informative counternarratives. in particular, we have found that the types of counter-narrative that negate the relation between the justification and the conclusion and that negate the justification have an improvement in performance if argumentative information about the justification and the conclusion is provided. moreover, we have also found that argumentative information makes a positive impact in scenarios with very few tweets, as shown by our experiments for spanish. although the quality of the counter-narratives generated for spanish is lower than for english, it is encouraging that argumentative information has a much higher positive impact than for a scenario with bigger models and more training examples, and we will continue to annotate examples for spanish to improve the generation of counter-narratives. we will also explore other aspects of the quality of counter-narratives, with a more insightful, more extensive human evaluation. we will also explore the interaction between argumentative information and other aspects, like lexic, level of formality, and culture.