Recently, there has been considerable attention towards leveraging large language models (LLMs) to enhance decision-making processes. However, aligning the natural language text instructions generated by LLMs with the vectorized operations required for execution presents a significant challenge, often necessitating task-specific details. To circumvent the need for such task-specific granularity, inspired by preference-based policy learning approaches, we investigate the utilization of multimodal LLMs to provide automated preference feedback solely from image inputs to guide decision-making. In this study, we train a multimodal LLM, termed CriticGPT, capable of understanding trajectory videos in robot manipulation tasks, serving as a critic to offer analysis and preference feedback. Subsequently, we validate the effectiveness of preference labels generated by CriticGPT from a reward modeling perspective. Experimental evaluation of the algorithm's preference accuracy demonstrates its effective generalization ability to new tasks. Furthermore, performance on Meta-World tasks reveals that CriticGPT's reward model efficiently guides policy learning, surpassing rewards based on state-of-the-art pre-trained representation models.

Astronomers have found the brightest object in the universe – a'hellish' black hole that consumes a star a day. Described as'the most hellish place in the universe', the black hole is 12 billion light years away and has a mass roughly 17 billion times that of our solar system's sun. Due to their immense gravitational pull, black holes grow in mass by capturing nearby material, whether it's stars, planets and even other black holes. The matter being pulled in toward this black hole, known as J0529-4351, forms a whopping disc that measures seven light-years in diameter. All galaxies have a supermassive black hole at their cores.

Former Acting Defense Secretary Chris Miller joined'Fox & Friends' to discuss the latest on the escalation in the Middle East as the U.S. continues to strike Iranian proxies. Despite a month of U.S.-led airstrikes, Yemen's Iran-backed Houthi rebels remain capable of launching significant attacks -- just this week, they seriously damaged a ship in a crucial strait and apparently downed an American drone worth tens of millions of dollars. The continued assaults by the Houthis on shipping through the crucial Red Sea corridor -- the Bab el-Mandeb Strait -- against the backdrop of Israel's war on Hamas in the Gaza Strip underscore the challenges in trying to stop the guerrilla-style attacks that have seen them hold onto Yemen's capital and much of the war-ravaged country's north since 2014. Meanwhile, the campaign has boosted the rebels' standing in the Arab world, despite their own human rights abuses in a yearslong stalemated war with several of America's allies in the region. And the longer their attacks go on, analysts warn the greater the risk that disruptions to international shipping will begin to weigh down on the global economy.

Parents might have once cautioned against speaking to strangers on the internet. But dating apps are now one of the most common ways for couples around the world to meet. Matching with strangers still has its drawbacks, including the risk of being'catfished'. But help is at hand, as Tinder has launched new verification tools in the UK and US that let users scan their passport or driving licence to confirm their identity. 'Giving users more confidence that their matches are authentic is one of the most valuable things we can do for our users,' said Rory Kozoll, SVP Product Integrity at Tinder.

Writing formulas on spreadsheets, such as Microsoft Excel and Google Sheets, is a widespread practice among users performing data analysis. However, crafting formulas on spreadsheets remains a tedious and error-prone task for many end-users, particularly when dealing with complex operations. To alleviate the burden associated with writing spreadsheet formulas, this paper introduces a novel benchmark task called NL2Formula, with the aim to generate executable formulas that are grounded on a spreadsheet table, given a Natural Language (NL) query as input. To accomplish this, we construct a comprehensive dataset consisting of 70,799 paired NL queries and corresponding spreadsheet formulas, covering 21,670 tables and 37 types of formula functions. We realize the NL2Formula task by providing a sequence-to-sequence baseline implementation called fCoder. Experimental results validate the effectiveness of fCoder, demonstrating its superior performance compared to the baseline models. Furthermore, we also compare fCoder with an initial GPT-3.5 model (i.e., text-davinci-003). Lastly, through in-depth error analysis, we identify potential challenges in the NL2Formula task and advocate for further investigation.

With the rapid development of big data and AI technology, programming is in high demand and has become an essential skill for students. Meanwhile, researchers also focus on boosting the online judging system's guidance ability to reduce students' dropout rates. Previous studies mainly targeted at enhancing learner engagement on online platforms by providing personalized recommendations. However, two significant challenges still need to be addressed in programming: C1) how to recognize complex programming behaviors; C2) how to capture intrinsic learning patterns that align with the actual learning process. To fill these gaps, in this paper, we propose a novel model called Programming Exercise Recommender with Learning Style (PERS), which simulates learners' intricate programming behaviors. Specifically, since programming is an iterative and trial-and-error process, we first introduce a positional encoding and a differentiating module to capture the changes of consecutive code submissions (which addresses C1). To better profile programming behaviors, we extend the Felder-Silverman learning style model, a classical pedagogical theory, to perceive intrinsic programming patterns. Based on this, we align three latent vectors to record and update programming ability, processing style, and understanding style, respectively (which addresses C2). We perform extensive experiments on two real-world datasets to verify the rationality of modeling programming learning styles and the effectiveness of PERS for personalized programming guidance.

Large language models (LLMs) have demonstrated impressive abilities in generating unstructured natural language according to instructions. However, their performance can be inconsistent when tasked with producing text that adheres to specific structured formats, which is crucial in applications like named entity recognition (NER) or relation extraction (RE). To address this issue, this paper introduces an efficient method, G&O, to enhance their structured text generation capabilities. It breaks the generation into a two-step pipeline: initially, LLMs generate answers in natural language as intermediate responses. Subsequently, LLMs are asked to organize the output into the desired structure, using the intermediate responses as context. G&O effectively separates the generation of content from the structuring process, reducing the pressure of completing two orthogonal tasks simultaneously. Tested on zero-shot NER and RE, the results indicate a significant improvement in LLM performance with minimal additional efforts. This straightforward and adaptable prompting technique can also be combined with other strategies, like self-consistency, to further elevate LLM capabilities in various structured text generation tasks.

Received wisdom in linguistic typology holds that if the structure of a language becomes more complex in one dimension, it will simplify in another, building on the assumption that all languages are equally complex (Joseph and Newmeyer, 2012). We study this claim on a micro-level, using a tightly-controlled sample of Dutch dialects (across 366 collection sites) and Min dialects (across 60 sites), which enables a more fair comparison across varieties. Even at the dialect level, we find empirical evidence for a tradeoff between word length and a computational measure of phonotactic complexity from a LSTM-based phone-level language model-a result previously documented only at the language level. A generalized additive model (GAM) shows that dialects with low phonotactic complexity concentrate around the capital regions, which we hypothesize to correspond to prior hypotheses that language varieties of greater or more diverse populations show reduced phonotactic complexity. We also experiment with incorporating the auxiliary task of predicting syllable constituency, but do not find an increase in the negative correlation observed.

The dependence of Natural Language Processing (NLP) intelligent software on Large Language Models (LLMs) is increasingly prominent, underscoring the necessity for robustness testing. Current testing methods focus solely on the robustness of LLM-based software to prompts. Given the complexity and diversity of real-world inputs, studying the robustness of LLMbased software in handling comprehensive inputs (including prompts and examples) is crucial for a thorough understanding of its performance. To this end, this paper introduces RITFIS, a Robust Input Testing Framework for LLM-based Intelligent Software. To our knowledge, RITFIS is the first framework designed to assess the robustness of LLM-based intelligent software against natural language inputs. This framework, based on given threat models and prompts, primarily defines the testing process as a combinatorial optimization problem. Successful test cases are determined by a goal function, creating a transformation space for the original examples through perturbation means, and employing a series of search methods to filter cases that meet both the testing objectives and language constraints. RITFIS, with its modular design, offers a comprehensive method for evaluating the robustness of LLMbased intelligent software. RITFIS adapts 17 automated testing methods, originally designed for Deep Neural Network (DNN)-based intelligent software, to the LLM-based software testing scenario. It demonstrates the effectiveness of RITFIS in evaluating LLM-based intelligent software through empirical validation. However, existing methods generally have limitations, especially when dealing with lengthy texts and structurally complex threat models. Therefore, we conducted a comprehensive analysis based on five metrics and provided insightful testing method optimization strategies, benefiting both researchers and everyday users.

The introduction of Vec2Text, a technique for inverting text embeddings, has raised serious privacy concerns within dense retrieval systems utilizing text embeddings, including those provided by OpenAI and Cohere. This threat comes from the ability for a malicious attacker with access to text embeddings to reconstruct the original text. In this paper, we investigate various aspects of embedding models that could influence the recoverability of text using Vec2Text. Our exploration involves factors such as distance metrics, pooling functions, bottleneck pre-training, training with noise addition, embedding quantization, and embedding dimensions -- aspects not previously addressed in the original Vec2Text paper. Through a thorough analysis of these factors, our aim is to gain a deeper understanding of the critical elements impacting the trade-offs between text recoverability and retrieval effectiveness in dense retrieval systems. This analysis provides valuable insights for practitioners involved in designing privacy-aware dense retrieval systems. Additionally, we propose a straightforward fix for embedding transformation that ensures equal ranking effectiveness while mitigating the risk of text recoverability. Furthermore, we extend the application of Vec2Text to the separate task of corpus poisoning, where, theoretically, Vec2Text presents a more potent threat compared to previous attack methods. Notably, Vec2Text does not require access to the dense retriever's model parameters and can efficiently generate numerous adversarial passages. In summary, this study highlights the potential threat posed by Vec2Text to existing dense retrieval systems, while also presenting effective methods to patch and strengthen such systems against such risks.