We study the ability of state-of-the art models to answer constraint satisfaction queries for information retrieval (e.g., 'a list of ice cream shops in San Diego'). In the past, such queries were considered to be tasks that could only be solved via web-search or knowledge bases. More recently, large language models (LLMs) have demonstrated initial emergent abilities in this task. However, many current retrieval benchmarks are either saturated or do not measure constraint satisfaction. Motivated by rising concerns around factual incorrectness and hallucinations of LLMs, we present KITAB, a new dataset for measuring constraint satisfaction abilities of language models. KITAB consists of book-related data across more than 600 authors and 13,000 queries, and also offers an associated dynamic data collection and constraint verification approach for acquiring similar test data for other authors. Our extended experiments on GPT4 and GPT3.5 characterize and decouple common failure modes across dimensions such as information popularity, constraint types, and context availability. Results show that in the absence of context, models exhibit severe limitations as measured by irrelevant information, factual errors, and incompleteness, many of which exacerbate as information popularity decreases. While context availability mitigates irrelevant information, it is not helpful for satisfying constraints, identifying fundamental barriers to constraint satisfaction. We open source our contributions to foster further research on improving constraint satisfaction abilities of future models.

Financial markets analysis has been and remains a topic of intense research interest since the seminal work of Markowitz [1] detailing his theory on portfolio choice, for which he was awarded the Nobel Prize in 1990. The rapid advancements of Machine Learning (ML) and, more specifically those made in the field of Deep Learning (DL) and Deep Reinforcement Learning (DRL), further fueled interest in the field. Financial markets analysts began using ML-based techniques and combining them with their own knowledge of the field [2]. As early as 1992, Neural Networks (NNs) were already being used for equity index futures trading [3]. More recently, DL research in financial market analysis has focused on high frequency trading, i.e., an algorithmic financial trading method where high speeds and large volumes are the main characteristics. The kind of data used in works that focus on this type of trading include Limit Order Book (LOB) data [4] as well as candle data for assets such as FOREX or Cryptocurrencies [5]. Candle data contain the Open, High, Low and Close prices for assets in a requested frequency, e.g., at the minute or hour level. Price forecasting is a first step towards solving the very complex task of portfolio management, and has proved to be a sufficiently difficult problem to tackle itself. One way to sufficiently solve it is by transforming the problem into one of classification, i.e., predicting the price movement instead of its actual value in the next step [4].

Questions in open-domain question answering are often ambiguous, allowing multiple interpretations. One approach to handling them is to identify all possible interpretations of the ambiguous question (AQ) and to generate a long-form answer addressing them all, as suggested by Stelmakh et al., (2022). While it provides a comprehensive response without bothering the user for clarification, considering multiple dimensions of ambiguity and gathering corresponding knowledge remains a challenge. To cope with the challenge, we propose a novel framework, Tree of Clarifications (ToC): It recursively constructs a tree of disambiguations for the AQ -- via few-shot prompting leveraging external knowledge -- and uses it to generate a long-form answer. ToC outperforms existing baselines on ASQA in a few-shot setup across the metrics, while surpassing fully-supervised baselines trained on the whole training set in terms of Disambig-F1 and Disambig-ROUGE. Code is available at https://github.com/gankim/tree-of-clarifications.

ChatGPT is an advanced natural language processing tool with growing applications across various disciplines in medical research. Thematic analysis, a qualitative research method to identify and interpret patterns in data, is one application that stands to benefit from this technology. This viewpoint explores the utilization of ChatGPT in three core phases of thematic analysis within a medical context: 1) direct coding of transcripts, 2) generating themes from a predefined list of codes, and 3) preprocessing quotes for manuscript inclusion. Additionally, we explore the potential of ChatGPT to generate interview transcripts, which may be used for training purposes. We assess the strengths and limitations of using ChatGPT in these roles, highlighting areas where human intervention remains necessary. Overall, we argue that ChatGPT can function as a valuable tool during analysis, enhancing the efficiency of the thematic analysis and offering additional insights into the qualitative data.

As the complexity of machine learning (ML) models increases and their application in different (and critical) domains grows, there is a strong demand for more interpretable and trustworthy ML. A direct, model-agnostic, way to interpret such models is to train surrogate models, such as rule sets and decision trees, that sufficiently approximate the original ones while being simpler and easier-to-explain. Yet, rule sets can become very lengthy, with many if-else statements, and decision tree depth grows rapidly when accurately emulating complex ML models. In such cases, both approaches can fail to meet their core goal, providing users with model interpretability. To tackle this, we propose DeforestVis, a visual analytics tool that offers user-friendly summarization of the behavior of complex ML models by providing surrogate decision stumps (one-level decision trees) generated with the adaptive boosting (AdaBoost) technique. DeforestVis helps users to explore the complexity vs fidelity trade-off by incrementally generating more stumps, creating attribute-based explanations with weighted stumps to justify decision making, and analyzing the impact of rule overriding on training instance allocation between one or more stumps. An independent test set allows users to monitor the effectiveness of manual rule changes and form hypotheses based on case-by-case analyses. We show the applicability and usefulness of DeforestVis with two use cases and expert interviews with data analysts and model developers.

Knowledge graphs represent known facts using triplets. While existing knowledge graph embedding methods only consider the connections between entities, we propose considering the relationships between triplets. For example, let us consider two triplets $T_1$ and $T_2$ where $T_1$ is (Academy_Awards, Nominates, Avatar) and $T_2$ is (Avatar, Wins, Academy_Awards). Given these two base-level triplets, we see that $T_1$ is a prerequisite for $T_2$. In this paper, we define a higher-level triplet to represent a relationship between triplets, e.g., $\langle T_1$, PrerequisiteFor, $T_2\rangle$ where PrerequisiteFor is a higher-level relation. We define a bi-level knowledge graph that consists of the base-level and the higher-level triplets. We also propose a data augmentation strategy based on the random walks on the bi-level knowledge graph to augment plausible triplets. Our model called BiVE learns embeddings by taking into account the structures of the base-level and the higher-level triplets, with additional consideration of the augmented triplets. We propose two new tasks: triplet prediction and conditional link prediction. Given a triplet $T_1$ and a higher-level relation, the triplet prediction predicts a triplet that is likely to be connected to $T_1$ by the higher-level relation, e.g., $\langle T_1$, PrerequisiteFor, ?$\rangle$. The conditional link prediction predicts a missing entity in a triplet conditioned on another triplet, e.g., $\langle T_1$, PrerequisiteFor, (Avatar, Wins, ?)$\rangle$. Experimental results show that BiVE significantly outperforms all other methods in the two new tasks and the typical base-level link prediction in real-world bi-level knowledge graphs.

CyberGuy explains how to leave FaceTime messages on iOS 17. Whether you're a new parent or a seasoned one, we know how stressful and challenging it can be to raise children in this fast-paced and ever-changing world. Many parents today are looking for ways to leverage technology to enhance their parenting skills and support their children's development. And the best part is, you don't need to be a tech expert to use them. All you need is a device, an internet connection, and a chatbot named ChatGPT.

Can artificial intelligence really summon dead relatives back from beyond the grave? A growing number of people are trying to find out, with pioneers such as inventor and futurist Ray Kurzweil using artificial intelligence to recreate lost relatives. Kurzweil's attempts to'bring back' his father - who died when Kurzweil was 22 - using AI began more than 10 years ago and are chronicled this year in a comic book by Kurzweil's daughter Amy. Kurzweil created a'replicant' of his father by feeding an artificial intelligence system with his father's letters, essays and musical compositions. He now has even more ambitious plans to bring his father back to life using nanotechnology and DNA from his father's buried bones.

Large language models (LLMs) have produced notable breakthroughs in downstream performance [61; 11; 19; 62; 91; 49; 8; 78], but have also introduced new challenges in model evaluation. The success of LLMs has initiated a fundamental paradigm shift away from small specialized models designed for single tasks to universal models expected to perform well across a wide range of tasks. This shift has also posed an existential challenge for evaluation, with a need to move away from solely task-specific automatic metrics of evaluation and increasing reliance on human evaluation. While automatic metrics offer a degree of objectivity and reproducibility, alongside the benefits of speed and cost-effectiveness, they often fall short in fully capturing the complexities and nuances of natural language [48; 68]. Moreover, automatic metrics often rely on auxiliary models which introduce potential points of failure and unexpected challenges over time [58]. For example, reference-based metrics such as BLEU [54] and ROUGE [45] are usually poor indicators of human judgment, as they emphasize lexical overlap and struggle to account for the diverse expressions inherent in semantic representation [34; 84; 9].

Text sanitization is the task of redacting a document to mask all occurrences of (direct or indirect) personal identifiers, with the goal of concealing the identity of the individual(s) referred in it. In this paper, we consider a two-step approach to text sanitization and provide a detailed analysis of its empirical performance on two recently published datasets: the Text Anonymization Benchmark (Pil\'an et al., 2022) and a collection of Wikipedia biographies (Papadopoulou et al., 2022). The text sanitization process starts with a privacy-oriented entity recognizer that seeks to determine the text spans expressing identifiable personal information. This privacy-oriented entity recognizer is trained by combining a standard named entity recognition model with a gazetteer populated by person-related terms extracted from Wikidata. The second step of the text sanitization process consists in assessing the privacy risk associated with each detected text span, either isolated or in combination with other text spans. We present five distinct indicators of the re-identification risk, respectively based on language model probabilities, text span classification, sequence labelling, perturbations, and web search. We provide a contrastive analysis of each privacy indicator and highlight their benefits and limitations, notably in relation to the available labeled data.