Can LLM s Be Efficient Predictors of Conversational Derailment? (2025)

Abstract: Conversational derailment — when online discussions stray from their intended topics due to toxic or inappropriate remarks — is a common issue on online platforms. These derailments can have negative impacts on users and the online community. While previous work has focused on post hoc identification of toxic content, recent efforts emphasize proactive prediction of derailments before they occur, enabling early moderation. However, forecasting derailment is difficult due to the context-dependent emergence of toxicity and the need for timely alerts. We prompt pre-trained large language models (LLMs) to predict conversational derailment without task-specific fine-tuning. We compare a range of prompting strategies, including chain-of-thought reasoning (CoT) and few-shot exemplars, across small and large scale models, and evaluate their performance and inference-cost trade-offs on derailment benchmarks. Our experiments show that the best prompting configuration attains state-of-the-art performance, and forecasts derailments earlier than existing approaches. These results demonstrate that LLMs, even without fine-tuning, can serve as an effective tool for proactive conversational moderation.

## introduction the task of forecasting conversation derailment, defined as the proactive prediction of future antisocial behavior in otherwise civil online discussions, has received increasing attention in recent years. earlier research primarily focused on post hoc classification of toxic behavior @xcite ; however, recent advancements aim to detect early warning signs of derailment to enable timely interventions. this task is unusual and challenging because the predictive model must take into account previous and current conversational turns, not simply the dialog as a whole. also, conversations can "derail" in various ways, and different datasets reflect particular properties of this phenomenon. for example in the conversations gone awry (cga) dataset @xcite conversations are taskoriented dialogues between wikipedia editors; in contrast, the reddit changemyview (cmv) corpus @xcite covers a broader range of topics. in both datasets, a derailment indicates that the conversation devolved into personal attacks (i.e., became "toxic"). these represent the two main datasets commonly used for this task. in this paper, we conduct experiments using llms to predict the existence of a derailment in a conversation as early as possible, and address the following research questions: rq1: are llms able to predict conversational derailments? rq2: can llms predict derailments as early as current models? rq3: how do llms compare on performance and cost parameters against current models? our results on two datasets with four llms show that these models excel at the prediction task itself and are able to predict the derailment quite early. we use only the data present in these datasets, without additional features or fine-tuning. in addition, we analyze the costs and benefits of deploying such models in real-world scenarios where toxic conversations may need to be detected dynamically in real time. ## previous approaches a clear methodological trajectory emerges across previous studies. @xcite establish the foundational task and dataset, relying on static linguistic features and crowd-annotated personal attacks. @xcite move to dynamic prediction via hierarchical rnns (craft), explicitly modeling comment-to-comment interactions over time. @xcite further refine this by proposing dynamic training aligned with the inference-time setup, using bert-based sequential models and revealing trade-offs in noisy data. later models shift toward deeper architectural representations. @xcite adopt a hierarchical transformer design and introduce multitask learning with a novel regression head for time-to-derailment, enhancing interpretability and early warning capacity. @xcite go beyond sequence modeling by introducing a graphbased approach (fgcn), capturing multi-party dynamics through user relationships and community signals such as voting. this model demonstrates state-of-the-art performance, particularly in terms of forecast horizon, as does its knowledge-aware version in @xcite . some approaches use additional information either exploiting aspects of the model itself or adding additional elements to the corpus. sicilia and alikhani (2024) leverage uncertainty estimation in derailment forecasting, while nonaka and yoshida (2025) add contextual information to the data, including the corpus name with a short description in the prompt. all current research tends to emphasize the gradual, emergent nature of toxic exchanges and the necessity for models that operate incrementally as conversations unfold. each approach aims to forecast antisocial outcomes before they manifest explicitly, offering a window for intervention. forecast horizon, a metric indicating how early a derailment is predicted, is a key comparative axis. the baseline craft model achieves an average of three turns, while fgcn-t+ extends this to over four turns on cmv. the introduction of dynamic training improves early detection in some datasets (e.g., cga), but may suffer on noisier data as evidenced by performance drops in bert.sc+ compared to fgcn-t+. ## data and methodology each sample within each dataset is a single conversation containing a set of conversational turns or utterances to answer rq1, we developed prompts for each dataset in a zero-shot, few-shot, and cot-reasoning contexts, asking the model to predict whether a conversation will derail given the conversation so far at each utterance. we develop separate prompts with general instructions and dataset-specific instructions for derailment forecasting. the output is a boolean value, true if the conversation is or will be derailed by a personal attack, false otherwise. examples of the prompts are in the appendix d. we primarily run four models, @xcite , gpt-4o-mini (openai, 2024a), claude 3.5 sonnet @xcite and claude 3.5 haiku @xcite . for completeness, we also evaluate open-source llama 3.1 @xcite variants (8b, 70b, 405b); full results and cost analyses are provided in appendix c. our baselines include craft @xcite , bert.sc (kementchedjhieva and søgaard, 2021) and the hierarchical transformer @xcite , fcgn @xcite , and ka-fgcn-brt @xcite . for each of our models, we report results for the task of predicting the conversational outcome and also the mean forecast horizon associated with that prediction, as our approach to answering rq2. details of how we report model forecasting results are found in 4.1. finally, we address rq3 by analyzing the inference-cost trade-offs of our approach on these two benchmark datasets. ## results and discussion we evaluate performance using standard classification metrics: accuracy, precision, recall, and f1 score. f1 score will serve as our primary metric, as it balances precision and recall and is particularly important when dealing with the asymmetric costs of false positives and false negatives in conversational derailment forecasting. in addition to these standard metrics, we also report the mean forecast horizon (h), defined as the average number of conversational turns before derailment at which the model successfully issues a warning. a longer fore-cast horizon indicates earlier and more actionable derailment predictions, enabling more effective intervention, so larger values are better. all metrics are calculated following the same evaluation protocol used in prior works to ensure consistency and comparability. each model is evaluated on the heldout test sets of both the cga and cmv datasets. our results are presented in for the cga dataset, gpt-4o and claude 3.5 sonnet both outperform the current bestperforming models. for the cmv dataset, claude 3.5 sonnet beats the current best-performing model, with gpt-4o showing comparable results to the fgcn baseline. we explored a variety of prompting strategies for each model and report the configurations that achieved the best performance. for gpt-4o, optimal results were obtained using 200shot general instructions for cga and 100-shot task-specific instructions for cmv, both without cot. claude 3.5 sonnet performed best with zeroshot general instructions for cga and 10-shot general instructions with cot for cmv. gpt-4o-mini showed the highest effectiveness with few-shot prompting -200 shots for cga and 100 for cmv -using task-specific instructions without cot. for claude 3.5 haiku, zero-shot general instructions yielded the best results. evaluation details for opensource llama 3.1 variants (8b, 70b, 405b) are provided in appendix c; in brief, llama-3.1-70b is competitive but trails the strongest proprietary models while offering lower cost. the main trend we notice is that claude does better with fewer shots in general than gpt. in a recent study @xcite , zero-shot analysis indicated that gpt-4o excels in simple, short sa tasks across various datasets, while claude 3.5 sonnet outperforms it in more complex, sentimentwavering task. this may explain the higher performance of claude in the cmv dataset, where exchanges tend to be longer and more complex. in the few-shot results in @xcite , both models exhibit similar trends, with claude 3.5 sonnet achieving superior results on most datasets, but gpt-4o demonstrates greater improvement with more shots. we observe the same pattern here. there is ample evidence in the literature showing differences between the two datasets, in particular, longer sequences and more turns in cmv, which would tend to favor claude 3.5 sonnet. ## conclusion we have shown that off-the-shelf llms, when guided by carefully designed prompts and simple heuristics, can forecast conversational derailment as effectively as specialized models, and more effectively than baselines, while delivering earlier warnings. our comprehensive evaluation across cga and cmv benchmarks shows that generalpurpose prompts are robust, and that task-specific prompts offer marginal improvements in some settings, while a heuristic of withholding predictions on the first turn improves f1 by reducing false positives. larger models yield higher f1 and forecast horizons (compared to baselines) but at increased cost, underscoring practical trade-offs in real-world deployments. these findings suggest that generalpurpose llms, with no additional fine-tuning, can serve as a scalable, vendor-agnostic solution for proactive moderation. we restrict ourselves to zeroand few-shot prompting without fine-tuning, which could further boost performance at the cost of additional data and engineering effort.