CohereLabsCommunity/multilingual_safety_survey2025 huggingface dataset:
Using In-Context Learning to Improve Dialogue Safety
Findings of the Association for Computational Linguistics: EACL 2023, 2023
Topics: general safety, LLM alignment

While large neural-based conversational models have become increasingly proficient dialogue agents, recent work has highlighted safety issues with these systems. For example, these systems can be goaded into generating toxic content, often perpetuating social biases or stereotypes. We investigate a retrieval-based approach for reducing bias and toxicity in responses from chatbots. It uses in-context learning to steer a model towards safer generations. Concretely, to generate a response to an unsafe dialogue context, we retrieve demonstrations of safe responses to similar dialogue contexts. We find our method performs competitively with existing approaches to dialogue safety without requiring training. We also show, using automatic and human evaluation, that reductions in toxicity obtained using our approach are not at the cost engagingness or coherency. Finally, we note our method can be used in compliment to existing dialogue safety approaches, such as RLHF.
BioDEX: Large-Scale Biomedical Adverse Drug Event Extraction for Real-World Pharmacovigilance
Findings of the Association for Computational Linguistics: EACL 2023, 2023
Topics: others

Timely and accurate extraction of Adverse Drug Events (ADE) from biomedical literature is paramount for public safety, but involves slow and costly manual labor. We set out to improve drug safety monitoring (pharmacovigilance, PV) through the use of Natural Language Processing (NLP). We introduce BioDEX, a large-scale resource for Biomedical adverse Drug Event eXtraction, rooted in the historical output of drug safety reporting in the U.S. BioDEX consists of 65k abstracts and 19k full-text biomedical papers with 256k associated document-level safety reports created by medical experts. The core features of these reports include the reported weight, age, and biological sex of a patient, a set of drugs taken by the patient, the drug dosages, the reactions experienced, and whether the reaction was life threatening. In this work, we consider the task of predicting the core information of the report given its originating paper. We estimate human performance to be 72.0% F1, whereas our best model achieves 59.1% F1 (62.3 validation), indicating significant headroom. We also begin to explore ways in which these models could help professional PV reviewers. Our code and data are available at https://github.com/KarelDO/BioDEX.
DialGuide: Aligning Dialogue Model Behavior with Developer Guidelines
Findings of the Association for Computational Linguistics: EACL 2023, 2023
Topics: general safety, LLM alignment

Dialogue models are able to generate coherent and fluent responses, but they can still be challenging to control and may produce non-engaging, unsafe results. This unpredictability diminishes user trust and can hinder the use of the models in the real world. To address this, we introduce DialGuide, a novel framework for controlling dialogue model behavior using natural language rules, or guidelines. These guidelines provide information about the context they are applicable to and what should be included in the response, allowing the models to generate responses that are more closely aligned with the developer’s expectations and intent. We evaluate DialGuide on three tasks in open-domain dialogue response generation: guideline selection, response generation, and response entailment verification. Our dataset contains 10,737 positive and 15,467 negative dialogue context-response-guideline triplets across two domains - chit-chat and safety. We provide baseline models for the tasks and benchmark their performance. We also demonstrate that DialGuide is effective in the dialogue safety domain, producing safe and engaging responses that follow developer guidelines.
Walking a Tightrope – Evaluating Large Language Models in High-Risk Domains
Proceedings of the 1st GenBench Workshop on (Benchmarking) Generalisation in NLP, 2023
Topics: hallucination, factuality

High-risk domains pose unique challenges that require language models to provide accurate and safe responses. Despite the great success of large language models (LLMs), such as ChatGPT and its variants, their performance in high-risk domains remains unclear. Our study delves into an in-depth analysis of the performance of instruction-tuned LLMs, focusing on factual accuracy and safety adherence. To comprehensively assess the capabilities of LLMs, we conduct experiments on six NLP datasets including question answering and summarization tasks within two high-risk domains: legal and medical. Further qualitative analysis highlights the existing limitations inherent in current LLMs when evaluating in high-risk domains. This underscores the essential nature of not only improving LLM capabilities but also prioritizing the refinement of domain-specific metrics, and embracing a more human-centric approach to enhance safety and factual reliability. Our findings advance the field toward the concerns of properly evaluating LLMs in high-risk domains, aiming to steer the adaptability of LLMs in fulfilling societal obligations and aligning with forthcoming regulations, such as the EU AI Act.
Through the Lens of Core Competency: Survey on Evaluation of Large Language Models
Proceedings of the 22nd Chinese National Conference on Computational Linguistics, 2023
Topics: others

“From pre-trained language model (PLM) to large language model (LLM), the field of naturallanguage processing (NLP) has witnessed steep performance gains and wide practical uses. Theevaluation of a research field guides its direction of improvement. However, LLMs are extremelyhard to thoroughly evaluate for two reasons. First of all, traditional NLP tasks become inade-quate due to the excellent performance of LLM. Secondly, existing evaluation tasks are difficultto keep up with the wide range of applications in real-world scenarios. To tackle these problems,existing works proposed various benchmarks to better evaluate LLMs. To clarify the numerousevaluation tasks in both academia and industry, we investigate multiple papers concerning LLMevaluations. We summarize 4 core competencies of LLM, including reasoning, knowledge, relia-bility, and safety. For every competency, we introduce its definition, corresponding benchmarks,and metrics. Under this competency architecture, similar tasks are combined to reflect corre-sponding ability, while new tasks can also be easily added into the system. Finally, we give oursuggestions on the future direction of LLM’s evaluation.”
Safety and Ethical Concerns of Large Language Models
Proceedings of the 22nd Chinese National Conference on Computational Linguistics, 2023
Topics: others

“Recent months have witnessed significant progress in the field of large language models (LLMs).Represented by ChatGPT and GPT-4, LLMs perform well in various natural language process-ing tasks and have been applied to many downstream applications to facilitate people’s lives. However, there still exist safety and ethical concerns. Specifically, LLMs suffer from social bias,robustness problems, and poisoning issues, all of which may induce LLMs to spew harmful con-tents. We propose this tutorial as a gentle introduction to the safety and ethical issues of LLMs.”
Catching Misdiagnosed Limb Fractures in the Emergency Department Using Cross-institution Transfer Learning
Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association, 2023
Topics: others

We investigated the development of a Machine Learning (ML)-based classifier to identify abnormalities in radiology reports from Emergency Departments (EDs) that can help automate the radiology report reconciliation process. Often, radiology reports become available to the ED only after the patient has been treated and discharged, following ED clinician interpretation of the X-ray. However, occasionally ED clinicians misdiagnose or fail to detect subtle abnormalities on X-rays, so they conduct a manual radiology report reconciliation process as a safety net. Previous studies addressed this problem of automated reconciliation using ML-based classification solutions that require data samples from the target institution that is heavily based on feature engineering, implying lower transferability between hospitals. In this paper, we investigated the benefits of using pre-trained BERT models for abnormality classification in a cross-institutional setting where data for fine-tuning was unavailable from the target institution. We also examined how the inclusion of synthetically generated radiology reports from ChatGPT affected the performance of the BERT models. Our findings suggest that BERT-like models outperform previously proposed ML-based methods in cross-institutional scenarios, and that adding ChatGPT-generated labelled radiology reports can improve the classifier’s performance by reducing the number of misdiagnosed discharged patients.
Calibrated Interpretation: Confidence Estimation in Semantic Parsing
Transactions of the Association for Computational Linguistics, Volume 11, 2023
Topics: others

Sequence generation models are increasingly being used to translate natural language into programs, i.e., to perform executable semantic parsing. The fact that semantic parsing aims to predict programs that can lead to executed actions in the real world motivates developing safe systems. This in turn makes measuring calibration—a central component to safety—particularly important. We investigate the calibration of popular generation models across four popular semantic parsing datasets, finding that it varies across models and datasets. We then analyze factors associated with calibration error and release new confidence-based challenge splits of two parsing datasets. To facilitate the inclusion of calibration in semantic parsing evaluations, we release a library for computing calibration metrics.1
Hallucinations in Large Multilingual Translation Models
Transactions of the Association for Computational Linguistics, Volume 11, 2023
Topics: hallucination, factuality
Languages covered: Afrikaans, Arabic, Asturian, Bengali, Czech, Welsh, German, Greek, Spanish, Persian, Finnish, French, Hebrew, Hindi, Croatian, Hungarian, Indonesian, Japanese, Korean, Lithuanian, Dutch, Occitan, Polish, Portuguese, Russian, Swedish, Swahili, Tagalog, Turkish, Vietnamese, Chinese

Hallucinated translations can severely undermine and raise safety issues when machine translation systems are deployed in the wild. Previous research on the topic focused on small bilingual models trained on high-resource languages, leaving a gap in our understanding of hallucinations in multilingual models across diverse translation scenarios. In this work, we fill this gap by conducting a comprehensive analysis—over 100 language pairs across various resource levels and going beyond English-centric directions—on both the M2M neural machine translation (NMT) models and GPT large language models (LLMs). Among several insights, we highlight that models struggle with hallucinations primarily in low-resource directions and when translating out of English, where, critically, they may reveal toxic patterns that can be traced back to the training data. We also find that LLMs produce qualitatively different hallucinations to those of NMT models. Finally, we show that hallucinations are hard to reverse by merely scaling models trained with the same data. However, employing more diverse models, trained on different data or with different procedures, as fallback systems can improve translation quality and virtually eliminate certain pathologies.
SafeConv: Explaining and Correcting Conversational Unsafe Behavior
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023
Topics: general safety, LLM alignment
Languages covered: Chinese

One of the main challenges open-domain end-to-end dialogue systems, or chatbots, face is the prevalence of unsafe behavior, such as toxic languages and harmful suggestions. However, existing dialogue datasets do not provide enough annotation to explain and correct such unsafe behavior. In this work, we construct a new dataset called SafeConv for the research of conversational safety: (1) Besides the utterance-level safety labels, SafeConv also provides unsafe spans in an utterance, information able to indicate which words contribute to the detected unsafe behavior; (2) SafeConv provides safe alternative responses to continue the conversation when unsafe behavior detected, guiding the conversation to a gentle trajectory. By virtue of the comprehensive annotation of SafeConv, we benchmark three powerful models for the mitigation of conversational unsafe behavior, including a checker to detect unsafe utterances, a tagger to extract unsafe spans, and a rewriter to convert an unsafe response to a safe version. Moreover, we explore the huge benefits brought by combining the models for explaining the emergence of unsafe behavior and detoxifying chatbots. Experiments show that the detected unsafe behavior could be well explained with unsafe spans and popular chatbots could be detoxified by a huge extent. The dataset is available at https://github.com/mianzhang/SafeConv.
Reward Gaming in Conditional Text Generation
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023
Topics: others

To align conditional text generation model outputs with desired behaviors, there has been an increasing focus on training the model using reinforcement learning (RL) with reward functions learned from human annotations. Under this framework, we identify three common cases where high rewards are incorrectly assigned to undesirable patterns: noise-induced spurious correlation, naturally occurring spurious correlation, and covariate shift. We show that even though learned metrics achieve high performance on the distribution of the data used to train the reward function, the undesirable patterns may be amplified during RL training of the text generation model. While there has been discussion about reward gaming in the RL or safety community, in this discussion piece, we would like to highlight reward gaming in the natural language generation (NLG) community using concrete conditional text generation examples and discuss potential fixes and areas for future work.
CHBias: Bias Evaluation and Mitigation of Chinese Conversational Language Models
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023
Topics: toxicity, bias

redWarning: This paper contains content that may be offensive or upsetting.Pretrained conversational agents have been exposed to safety issues, exhibiting a range of stereotypical human biases such as gender bias. However, there are still limited bias categories in current research, and most of them only focus on English. In this paper, we introduce a new Chinese dataset, CHBias, for bias evaluation and mitigation of Chinese conversational language models.Apart from those previous well-explored bias categories, CHBias includes under-explored bias categories, such as ageism and appearance biases, which received less attention. We evaluate two popular pretrained Chinese conversational models, CDial-GPT and EVA2.0, using CHBias. Furthermore, to mitigate different biases, we apply several debiasing methods to the Chinese pretrained models. Experimental results show that these Chinese pretrained models are potentially risky for generating texts that contain social biases, and debiasing methods using the proposed dataset can make response generation less biased while preserving the models’ conversational capabilities.
Using contradictions improves question answering systems
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023
Topics: others

This work examines the use of contradiction in natural language inference (NLI) for question answering (QA). Typically, NLI systems help answer questions by determining if a potential answer is entailed (supported) by some background context. But is it useful to also determine if an answer contradicts the context? We test this in two settings, multiple choice and extractive QA, and find that systems that incorporate contradiction can do slightly better than entailment-only systems on certain datasets. However, the best performances come from using contradiction, entailment, and QA model confidence scores together. This has implications for the deployment of QA systems in domains such as medicine and science where safety is an issue.
Measuring Attribution in Natural Language Generation Models
Computational Linguistics, Volume 49, Issue 1 - March 2023, 2023
Topics: hallucination, factuality

Large neural models have brought a new challenge to natural language generation (NLG): It has become imperative to ensure the safety and reliability of the output of models that generate freely. To this end, we present an evaluation framework, Attributable to Identified Sources (AIS), stipulating that NLG output pertaining to the external world is to be verified against an independent, provided source. We define AIS and a two-stage annotation pipeline for allowing annotators to evaluate model output according to annotation guidelines. We successfully validate this approach on generation datasets spanning three tasks (two conversational QA datasets, a summarization dataset, and a table-to-text dataset). We provide full annotation guidelines in the appendices and publicly release the annotated data at https://github.com/google-research-datasets/AIS.
Multi-party Goal Tracking with LLMs: Comparing Pre-training, Fine-tuning, and Prompt Engineering
Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue, 2023
Topics: others

This paper evaluates the extent to which current LLMs can capture task-oriented multi-party conversations (MPCs). We have recorded and transcribed 29 MPCs between patients, their companions, and a social robot in a hospital. We then annotated this corpus for multi-party goal-tracking and intent-slot recognition. People share goals, answer each other’s goals, and provide other people’s goals in MPCs - none of which occur in dyadic interactions. To understand user goals in MPCs, we compared three methods in zero-shot and few-shot settings: we fine-tuned T5, created pre-training tasks to train DialogLM using LED, and employed prompt engineering techniques with GPT-3.5-turbo, to determine which approach can complete this novel task with limited data. GPT-3.5-turbo significantly outperformed the others in a few-shot setting. The ‘reasoning’ style prompt, when given 7% of the corpus as example annotated conversations, was the best performing method. It correctly annotated 62.32% of the goal tracking MPCs, and 69.57% of the intent-slot recognition MPCs. A ‘story’ style prompt increased model hallucination, which could be detrimental if deployed in safety-critical settings. We conclude that multi-party conversations still challenge state-of-the-art LLMs.
Deep Learning Approaches to Detecting Safeguarding Concerns in Schoolchildren’s Online Conversations
Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, 2023
Topics: general safety, LLM alignment

For school teachers and Designated Safeguarding Leads (DSLs), computers and other school-owned communication devices are both indispensable and deeply worrisome. For their education, children require access to the Internet, as well as a standard institutional ICT infrastructure, including e-mail and other forms of online communication technology. Given the sheer volume of data being generated and shared on a daily basis within schools, most teachers and DSLs can no longer monitor the safety and wellbeing of their students without the use of specialist safeguarding software. In this paper, we experiment with the use of state-of-the-art neural network models on the modelling of a dataset of almost 9,000 anonymised child-generated chat messages on the Microsoft Teams platform. The data was manually classified into eight fine-grained classes of safeguarding concerns (or false alarms) that a monitoring program would be interested in, and these were further split into two binary classes: true positives (real safeguarding concerns) and false positives (false alarms). For the fine grained classification, our models achieved a macro F1 score of 73.56, while for the binary classification, we achieved a macro F1 score of 87.32. This first experiment into the use of Deep Learning for detecting safeguarding concerns represents an important step towards achieving high-accuracy and reliable monitoring information for busy teachers and safeguarding leads.
KITLM: Domain-Specific Knowledge InTegration into Language Models for Question Answering
Proceedings of the 20th International Conference on Natural Language Processing (ICON), 2023
Topics: others

Large language models (LLMs) have demon- strated remarkable performance in a wide range of natural language tasks. However, as these models continue to grow in size, they face sig- nificant challenges in terms of computational costs. Additionally, LLMs often lack efficient domain-specific understanding, which is par- ticularly crucial in specialized fields such as aviation and healthcare. To boost the domain- specific understanding, we propose, KITLM 1 , a novel knowledge base integration approach into language model through relevant informa- tion infusion. By integrating pertinent knowl- edge, not only the performance of the lan- guage model is greatly enhanced, but the model size requirement is also significantly reduced while achieving comparable performance. Our proposed knowledge-infused model surpasses the performance of both GPT-3.5-turbo and the state-of-the-art knowledge infusion method, SKILL, achieving over 1.5 times improvement in exact match scores on the MetaQA. KITLM showed a similar performance boost in the avi- ation domain with AeroQA. The drastic perfor- mance improvement of KITLM over the exist- ing methods can be attributed to the infusion of relevant knowledge while mitigating noise. In addition, we release two curated datasets to accelerate knowledge infusion research in specialized fields: a) AeroQA, a new bench- mark dataset designed for multi-hop question- answering within the aviation domain, and b) Aviation Corpus, a dataset constructed from unstructured text extracted from the National Transportation Safety Board reports. Our re- search contributes to advancing the field of domain-specific language understanding and showcases the potential of knowledge infusion techniques in improving the performance.