The climate crisis is a salient issue in online discussions, and hypocrisy accusations are a central rhetorical element in these debates. However, for large-scale text analysis, hypocrisy accusation detection is an understudied tool, most often defined as a smaller subtask of fallacious argument detection. In this paper, we define hypocrisy accusation detection as an independent task in NLP, and identify different relevant subtypes of hypocrisy accusations. Our Climate Hypocrisy Accusation Corpus (CHAC) consists of 420 Reddit climate debate comments, expert-annotated into two different types of hypocrisy accusations: personal versus political hypocrisy. We evaluate few-shot in-context learning with 6 shots and 3 instruction-tuned Large Language Models (LLMs) for detecting hypocrisy accusations in this dataset. Results indicate that the GPT-4o and Llama-3 models in particular show promise in detecting hypocrisy accusations (F1 reaching 0.68, while previous work shows F1 of 0.44). However, context matters for a complex semantic concept such as hypocrisy accusations, and we find models struggle especially at identifying political hypocrisy accusations compared to personal moral hypocrisy. Our study contributes new insights in hypocrisy detection and climate change discourse, and is a stepping stone for large-scale analysis of hypocrisy accusation in online climate debates.

Large language models (LLMs) have taken centre stage in debates on Artificial Intelligence. Yet there remains a gap in how to assess LLMs' conformity to important human values. In this paper, we investigate whether state-of-the-art LLMs, GPT-4 and Claude 2.1 (Gemini Pro and LLAMA 2 did not generate valid results) are moral hypocrites. We employ two research instruments based on the Moral Foundations Theory: (i) the Moral Foundations Questionnaire (MFQ), which investigates which values are considered morally relevant in abstract moral judgements; and (ii) the Moral Foundations Vignettes (MFVs), which evaluate moral cognition in concrete scenarios related to each moral foundation. We characterise conflicts in values between these different abstractions of moral evaluation as hypocrisy. We found that both models displayed reasonable consistency within each instrument compared to humans, but they displayed contradictory and hypocritical behaviour when we compared the abstract values present in the MFQ to the evaluation of concrete moral violations of the MFV.

I'm a guy "in real life," but I've always played female characters in video games. More and more people say this means I'm either secretly gay/trans or a total creep. Am I allowed to just prefer it? For timely guidance on encounters with technology, open a support ticket via email; or register and post a comment below. It sounds like you have a lot of people in your life, Player, who think they know you better than you know yourself.

Chain-of-Thought (CoT) prompting has boosted the multi-step reasoning capabilities of Large Language Models (LLMs) by generating a series of rationales before the final answer. We analyze the reasoning paths generated by CoT and find two issues in multi-step reasoning: (i) Generating rationales irrelevant to the question, (ii) Unable to compose subquestions or queries for generating/retrieving all the relevant information. To address them, we propose a graph-guided CoT prompting method, which guides the LLMs to reach the correct answer with graph representation/verification steps. Specifically, we first leverage LLMs to construct a "question/rationale graph" by using knowledge extraction prompting given the initial question and the rationales generated in the previous steps. Then, the graph verification step diagnoses the current rationale triplet by comparing it with the existing question/rationale graph to filter out irrelevant rationales and generate follow-up questions to obtain relevant information. Additionally, we generate CoT paths that exclude the extracted graph information to represent the context information missed from the graph extraction. Our graph-guided reasoning method shows superior performance compared to previous CoT prompting and the variants on multi-hop question answering benchmark datasets.

When I wrote recently that California's recall election process was terribly flawed and in need of serious reform, the angry messages came flowing in, calling me a hypocrite. The writers didn't believe for a second that I objected to the recall on principle -- they assumed that as a loyal Democrat, I was just shilling for Gov. Gavin Newsom. "You're in his pocket," said one dismissive tweet. Would I still be vehemently opposed to the recall if, instead of being used against a Democratic governor, it was targeting a Trump-supporting right-wing governor -- someone who, say, was unleashing the fossil fuel industry, hoping to do away with the minimum wage and fighting mask and vaccine mandates? Would I still feel the recall was a troubling, badly structured, overused, undemocratic tool that should be reformed or abolished?

So the other day, I was having a somewhat politically incorrect conversation with my fiancé. At one point, I slightly rebuked her by saying, "Honey, you know, Mark Zuckerburg could be listening to us as we speak!" Yeah, we've grown quite comfortable with Big Brother in our household. In line with that, I wanted to talk today a little bit about what's been called Surveillance Capitalism. Lately, I've been commenting a lot on artificial intelligence.

And I am talking Season 3. Or Amazon's hit, The Handmaid's Tale? Do you just binge and veg out or are you like me, and see how easily we could, and are, slipping into these worlds? After watching shows like this I often find myself reflecting back on George Orwell's 1984. It proves more eerily prophetic with each passing year. This Season, I fear, the writers of Westworld are almost scripting our future lives. You may not have caught it, but it is all in there.