Claim Check-Worthiness Detection: How Well do LLM s Grasp Annotation Guidelines? (2024)

Abstract: The rising threat of disinformation underscores the need to fully or partially automate the fact-checking process. Identifying text segments requiring fact-checking is known as claim detection (CD) and claim check-worthiness detection (CW), the latter incorporating complex domain-specific criteria of worthiness and often framed as a ranking task. Zero- and few-shot LLM prompting is an attractive option for both tasks, as it bypasses the need for labeled datasets and allows verbalized claim and worthiness criteria to be directly used for prompting. We evaluate the LLMs’ predictive accuracy on five CD/CW datasets from diverse domains, using corresponding annotation guidelines in prompts. We examine two key aspects: (1) how to best distill factuality and worthiness criteria into a prompt, and (2) how much context to provide for each claim. To this end, we experiment with different levels of prompt verbosity and varying amounts of contextual information given to the model. We additionally evaluate the top-performing models with ranking metrics, resembling prioritization done by fact-checkers. Our results show that optimal prompt verbosity varies, meta-data alone adds more performance boost than co-text, and confidence scores can be directly used to produce reliable check-worthiness rankings.

## introduction the global spread of information, coupled with mis-and disinformation, is increasing the demand for fact-checking @xcite , highlighting the need for automation. however, complete automation may not be ideal; for instance, politifact, which used chatgpt to verify previously fact-checked claims, faced issues like inconsistency, knowledge limitations, and misleading confidence @xcite . nevertheless, they see potential in language models for assisting factcheckers, particularly in identifying claims worth verifying. similarly, fullfact has highlighted the lack of effective claim selection tools as a major workflow challenge @xcite . to warrant fact-checking, a claim must be both factual (i.e., related to purported facts) and checkworthy (i.e., of interest to society). the nlp tasks of identifying factual and check-worthy claims are known as claim detection (cd) and claim checkworthiness detection (cw), respectively. the tasks make up the first component of the automatic factchecking pipeline. while both are typically defined as classification tasks, cw can also be framed as a ranking task, mimicking the prioritization process employed by fact-checking organizations. both cd and cw are challenging for several reasons. firstly, the underlying concepts of factual claims and check-worthiness resist straightforward definitions. to grasp factuality, @xcite presented a thorough categorization of factual claims, while @xcite provided a definition distinguishing opinions. regardless of these variations, factual could be deemed universal and self-explanatory, unlike check-worthiness, a term frequently used in previous research. defining check-worthiness is made more challenging by its subjective, context-dependent nature and temporal variability. assessing it usually requires choosing more specific criteria, such as relevance to the general public @xcite or policymakers, potential harm @xcite , or alignment with a particular topic @xcite ). another challenge is identifying the situational context (including previous discourse and speaker information) required to determine claim factuality and check-worthiness. for example, in cw annotation campaigns @xcite , annotators are typically presented with surrounding sentences to aid their assessment. the cd and cw tasks have been approached using both traditional supervised machine learning and fine-tuning pre-trained language models, both of which depend on labeled data. however, obtaining such datasets can be challenging as they need to align with specific languages, domains, and genres and meet desired factuality and worthiness criteria. moreover, dataset annotation is costly and requires redoing if criteria change. llms present a viable alternative to supervised methods owing to their strong zero-and few-shot performance @xcite . over time, fact-checking organizations have refined principles for claim prioritization, and zero-and few-shot prompting offers a seamless way to transfer this knowledge to the model. thus, an effective strategy might entail zero-and few-shot prompting with check-worthiness criteria from annotation guidelines. the challenge, however, is that llms often exhibit sensitivity to variations in prompts @xcite and unreliability @xcite . in this paper, we study the predictive and calibration accuracy of zero-and few-shot llm prompting for cd and cw. we experiment with five datasets, each with a different factuality or worthiness criterion outlined in the accompanying annotation guidelines. we investigate two key aspects: (1) how to best distill factuality and worthiness criteria from the annotation guidelines into the prompt and ( 2 ) what amount of context to provide for each claim. for (1), we experiment with varying the level of prompt verbosity, starting from brief zeroshot prompts to more detailed few-shot prompts that include examples. for (2), we expand the prompt with co-text and other components of the claim's situational context. furthermore, inspired by the fact-checker's prioritization process, we consider cw as a ranking task, using llm confidence scores as a proxy for determining priority. figure 1 depicts the workflow of our experiments. we show that prompting with worthiness criteria adopted from annotation guidelines can yield accuracy and ranking scores comparable to or surpassing existing cd/cw methods. although optimal prompt verbosity varies across datasets, certain in-domain trends can be observed across models. we also find that the impact of adding context is greater for lower verbosity levels, while meta-data is more beneficial than co-text. finally, we show that confidence scores can be directly used to produce reliable check-worthiness rankings. our contributions include analyzing llm performance in terms of (1) prompt detail, (2) provided context, and (3) variations across domains and worthiness criteria. ## related work developing a fully automated fact-checking system is appealing for both its applicability and the challenge it presents @xcite . however, @xcite question the purpose of such a system, pointing to its reliance on counter-evidence that may not be available for newly coined disinformation. this motivates a shift toward human-in-the-loop approaches and automating parts of the fact-checking pipeline. the cd and cw tasks constitute the first part of the fact-checking pipeline and are meant to select parts of the input for which fact-checking is possible (cd) or deemed necessary (cw). typically framed as classification tasks, the cd and cw tasks are handled using traditional supervised machine learning @xcite @xcite or fine-tuning pre-trained language models @xcite . methods of solving include rich sentence and context-level features @xcite , speaker, object, and claim span identification @xcite , or incorporating domain-specific knowledge by combining ontology and sentence embeddings @xcite . cw can also be framed as a ranking task @xcite , mimicking the prioritization of claims by fact-checking organizations. recently, the use of llms for cd and cw is starting to take on. @xcite and @xcite compare the performance of fine-tuned language models such as bert with llms using zero-and few-shot learning as well as fine-tuning. although zero-and few-shot approaches for llms underperform, the authors note their reliance on internal definitions of worthiness and limited prompt testing. as part of the fully automated fact-checking system relying only on llms, @xcite implement a cd module using a verbose few-shot prompt, yet they do not report performance metrics. finally, @xcite tackle cd by proposing a three-step prompting approach to examine model consistency. however, neither @xcite nor @xcite address the cw task. to our knowledge, there is no work on cw focused on describing specific worthiness criteria using verbose prompts. ## datasets our experiments utilize five datasets in english covering diverse topics and genres. examples from each dataset are presented in claimbuster (cb) @xcite ) is a widely used dataset of claims from usa presidential debates, featuring ternary labels (non-factual, unimportant factual, check-worthy factual) that distinguish between check-worthy and unimportant factual claims. this setup addresses both the cd and cw tasks. claims are deemed check-worthy if the general public would be interested in their veracity. however, no specific definition of factuality is provided -unimportant factual claims are defined as those lacking check-worthiness. clef checkthat!lab 2022 (clef) @xcite ) contains tweets about covid-19, with two parts: a set of tweets with claims and a subset with check-worthy claims, addressing both cd and cw tasks. check-worthiness is defined as the need for professional fact-checking, excluding jokes, trivial claims, or those deemed uninteresting. factual claims are defined as sentences that assert something is true and can be verified using factual information, such as statistical data, specific examples, or personal testimony. environmentalclaims (env) @xcite is compiled from environmental articles and reports. the dataset focuses on check-worthy environmental claims related to green-washing in marketing strategies. the authors defined specific criteria for an environmental claim that extend beyond the topic itself (e.g., highlighting the positive environmental impact of a product, not being too technical). the annotators were instructed to label only the explicit claims, discouraging the selection of claims with inter-sentence coreferences. newsclaims (news) @xcite comprises sentences from news articles on covid-19, with metadata available for positives (speaker, object, claim span). the annotators were asked to judge whether a claim falls into one of the four topic-specific categories, which essentially formed the worthiness criteria, even though checkworthiness was not explicitly mentioned in the guidelines. the dataset includes both check-worthy and non-check-worthy claims with inter-sentence coreferences (e.g., that's also false), which typically require inspecting the surrounding context to determine their check-worthiness (we estimate this applies to about 10% of claims in the test set). policlaim (poli) @xcite covers the same topic as claimbuster (politics, speeches of governors) but labels only verifiable claims, leaving out check-worthiness. the authors provided detailed guidelines on verifiable claims, emphasizing the need for specificity and differentiation from opinions lacking factual basis. to handle ambiguous cases, they employed a ternary (yes, no, maybe) annotation scheme. maybe indicates that a claim may contain factual information but does not fully meet all criteria. for claims labeled as maybe, annotators answered a follow-up yes-no question to determine whether the claim leans toward factual information or subjective opinion. as with news, inter-sentence coreference was considered; since the claims are extracted from political speeches, many of them include personal pronouns (i, we), which necessitates coreference resolution to identify the claimant or subject. we use these five datasets because they provide detailed annotation guidelines and cover various topics, genres, and worthiness criteria. ## experimental setup in our experiments, we use both closed-source and open-source llms. for closed source, we use ope-nai models gpt-turbo-3.5 and gpt-4-turbo. for open-source models, due to hardware constraints, we chose llama 3 8b instruct, which is the top performer in its parameter class. to ensure repro-ducibility and encourage deterministic behaviour, we prompt gpt models with the temperature setting of 0 along with a fixed seed parameter and use greedy sampling with @xmath0. we also experimented with other opensource models. mistral 7b instruct v0.2 was not compliant with the provided labels, instead giving open-ended answers, even for less verbose prompts. see appendix b for more detailed information on models. ## results we present the results for prompt verbosity levels in