An LLM -Enabled Knowledge Elicitation and Retrieval Framework for Zero-Shot Cross-Lingual Stance Identification (2024)

Abstract: Stance detection aims to identify the attitudes toward specific targets from text, which is an important research area in text mining and social media analytics. Existing research is mainly conducted in monolingual setting on English datasets. To tackle the data scarcity problem in low-resource languages, cross-lingual stance detection (CLSD) transfers the knowledge from high-resource (source) language to low-resource (target) language. The CLSD task is the most challenging in zero-shot setting when no training data is available in target language, and transferring stance-relevant knowledge learned from high-resource language to bridge the language gap is the key for improving the performance of zero-shot CLSD. In this paper, we leverage the capability of large language model (LLM) for stance knowledge acquisition, and propose KEAR, a knowledge elicitation and retrieval framework. The knowledge elicitation module in KEAR first derives different types of stance knowledge from LLM’s reasoning process. Then, the knowledge retrieval module in KEAR matches the target language input to the most relevant stance knowledge for enhancing text representations. Experiments on multilingual datasets show the effectiveness of KEAR compared with competitive baselines as well as the CLSD approaches trained with labeled data in target language.

## introduction stance detection aims to determine the attitudes (e.g., in favor of, against or neutral) toward predefined targets (e.g., entities, controversial topics or events) from a given text. it has attracted significant research attention and can facilitate critical applications such as market analysis, veracity checking and public opinion mining @xcite . recently, a variety of monolingual methods were proposed for different settings, including in-domain methods @xcite , cross-target methods @xcite and zeroshot methods @xcite @xcite . the majority of them are conducted on english datasets, whereas in other low-resource languages, it lacks sufficient data for training quality stance detection models. to alleviate the data scarcity issue, cross-lingual stance detection (clsd) transfers the knowledge learned from high-resource (source) language to low-resource (target) one. recent approaches usually heavily rely on labeled or unlabeled data in target language @xcite . in extreme data-scarce practical applications, clsd is the most challenging in zeroshot setting. zero-shot cross-lingual stance detection @xcite aims to identify the stance toward certain targets when no training data is available (i.e., even without unlabeled training data) in target language. the state-of-the-art approach @xcite proposes to pretrain language models with sentiment-based data and transfer the learned knowledge to target languages with prompt-based fine-tuning. in this paper, we focus on the challenging task of zero-shot clsd. since there is no training data for target language, due to the unique linguistic and cultural nuances in target language, the disparity among languages cannot be overcome via training on source language data alone. in such situation, incorporating language-agnostic external knowledge for zero-shot clsd can serve as a feasible scheme to enhance cross-lingual representations and bridge the language gap. although existing monolingual methods have incorporated external knowledge including sentiment lexicons @xcite , knowledge graph @xcite and wikipedia @xcite @xcite , they lack the proper consideration on the acquisition of stance-relevant knowl- ## our proposed method the overall structure of the proposed method kear is shown in figure 2 . knowledge elicitation module first derives different types of stance knowledge from the llm-generated reasoning process. the acquired knowledge is then validated by knowledge verification module and transferred to the source language through knowledge retrieval module hierarchically. finally, the retrieved stance knowledge is concatenated with the input and then fed into the classifier module. ## related work monolingual stance detection in-domain stance detection aims to identify the stance toward pre-defined targets @xcite @xcite . zero-shot stance detection predicts stance on unseen targets without training data. the mainstream work adopts transfer learning @xcite @xcite and mines implicit associations across targets @xcite @xcite . as for external knowledge, some work uses sentiment lexicon to guide knowledge transfer between targets @xcite @xcite . @xcite ; @xcite introduce commonsense knowledge from conceptnet to improve the generalization ability. besides, @xcite incorporate target-related knowledge from wikipedia to further enhance stance detection. further, @xcite utilizes llm to filter the knowledge retrieved from wikipedia for stance detection augment. however, their work fails to consider the stance-relevant inferential knowledge which is im-portant for stance identification. cross-lingual stance detection compared with english, data resources in most other languages are rather limited @xcite @xcite . to address this problem, cross-lingual stance detection (clsd) transfers knowledge from high-resource source language to low-resource target language. @xcite proposes contrastive language adaptation to align representations in source and target languages. based on this, @xcite further develops a targetlevel contrastive learning method for fine-grained alignment. however, both methods rely on annotated data in target language. @xcite utilizes unlabeled data in target language via dual knowledge distillation to bridge the language gap. to tackle the situation of extremely scarce data resources, zero-shot clsd @xcite identifies the stance toward targets with no training data available in target language. @xcite pre-train multilingual plm with sentiment-based corpus and transfer the knowledge to target languages via prompt-based tuning. ## conclusion we propose an llm-enabled knowledge elicitation and retrieval framework for zero-shot crosslingual stance detection, which explicitly elicits stance knowledge critical for stance detection from llm. the knowledge elicitation module acquires stance knowledge with target structure and lexicon based knowledge partition, and the verification module further verifies the knowledge with collaborative agents. the knowledge retrieval process matches the target language data with the most relevant stance knowledge to bridge the language gap. experimental results on the multilingual stance datasets verify the effectiveness of our method.