Large language models demonstrate an emergent ability to learn a new task from a small number of input-output demonstrations, referred to as in-context few-shot learning. However, recent work shows that in such settings, models mainly learn to mimic the new task distribution, instead of the mechanics of the new task. We argue that the commonly-used evaluation settings of few-shot models utilizing a random selection of in-context demonstrations is not able to disentangle models' ability to learn new skills from demonstrations, as most of the such-selected demonstrations are not informative for prediction beyond exposing the new task's input and output distribution. Therefore, we introduce an evaluation technique that disentangles few-shot learners' gain from in-context learning by picking the demonstrations sharing a specific, informative concept with the predicted sample, in addition to the performance reached by mainly non-informative samples. We find that regardless of the model size, existing few-shot learners are not able to benefit from observing such informative concepts in demonstrations. We also find that such ability may not be obtained trivially by exposing the informative demonstrations in the training process, leaving the challenge of training true in-context learners open.

Domain adaptation allows generative language models to address specific flaws caused by the domain shift of their application. However, the traditional adaptation by further training on in-domain data rapidly weakens the model's ability to generalize to other domains, making the open-ended deployments of the adapted models prone to errors. This work introduces novel training objectives built upon a semantic similarity of the predicted tokens to the reference. Our results show that (1) avoiding the common assumption of a single correct prediction by constructing the training target from tokens' semantic similarity can mitigate catastrophic forgetting during domain adaptation, while (2) preserving the quality of the adaptation, (3) with negligible additions to compute costs. In the broader perspective, the objectives grounded in a soft token alignment pioneer the exploration of the middle ground between the efficient but naive exact-match token-level objectives and expressive but computationally- and resource-intensive sequential objectives.

尽管表现出色，但大型语言模型（LLMS）还是臭名昭著的缺陷，因为它们偏爱简单的，表面级的文本关系而不是问题的完全语义复杂性。该提案调查了该问题的共同点，其在训练领域之外概括的能力较弱。我们调查了各种研究方向，提供了模型泛化能力的估计，发现将其中一些措施纳入培训目标会导致神经模型的分布鲁棒性增强。基于这些发现，我们提出了未来的研究指导，以增强LLM的鲁棒性。

自Mikolov等人的开创性工作以来。 （2013A）和Bojanowski等。 （2017），浅日志双线性语言模型的单词表示已成为许多NLP应用程序。 Mikolov等人。 （2018）介绍了一个位置日志双线性语言模型，具有基于关注的语言模型的特征，并且在内在单词类比任务上达到了最先进的性能。然而，位置模型从未评估了定性标准或外在任务，其速度是不切实际的。我们概述了注意机制与位置模型之间的相似性，并提出了一个受约束的位置模型，它适应Dai等人的稀疏注意机制。 （2018）。我们评估了三个新型定性标准的位置和受约束的位置模型及其对两种和布鲁森的外在语言建模任务（2014）。我们表明，位置和约束位置模型包含有关字令的可解释信息，优于Bojanowski等人的子字模型。 （2017）语言建模。我们还表明，受约束的位置模型优于语言建模的位置模型，并且是快速的两倍。

Deep neural networks (DNN) have outstanding performance in various applications. Despite numerous efforts of the research community, out-of-distribution (OOD) samples remain significant limitation of DNN classifiers. The ability to identify previously unseen inputs as novel is crucial in safety-critical applications such as self-driving cars, unmanned aerial vehicles and robots. Existing approaches to detect OOD samples treat a DNN as a black box and assess the confidence score of the output predictions. Unfortunately, this method frequently fails, because DNN are not trained to reduce their confidence for OOD inputs. In this work, we introduce a novel method for OOD detection. Our method is motivated by theoretical analysis of neuron activation patterns (NAP) in ReLU based architectures. The proposed method does not introduce high computational workload due to the binary representation of the activation patterns extracted from convolutional layers. The extensive empirical evaluation proves its high performance on various DNN architectures and seven image datasets. ion.

Imperfect information games (IIG) are games in which each player only partially observes the current game state. We study how to learn $\epsilon$-optimal strategies in a zero-sum IIG through self-play with trajectory feedback. We give a problem-independent lower bound $\mathcal{O}(H(A_{\mathcal{X}}+B_{\mathcal{Y}})/\epsilon^2)$ on the required number of realizations to learn these strategies with high probability, where $H$ is the length of the game, $A_{\mathcal{X}}$ and $B_{\mathcal{Y}}$ are the total number of actions for the two players. We also propose two Follow the Regularize leader (FTRL) algorithms for this setting: Balanced-FTRL which matches this lower bound, but requires the knowledge of the information set structure beforehand to define the regularization; and Adaptive-FTRL which needs $\mathcal{O}(H^2(A_{\mathcal{X}}+B_{\mathcal{Y}})/\epsilon^2)$ plays without this requirement by progressively adapting the regularization to the observations.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

We present the Verifee Dataset: a novel dataset of news articles with fine-grained trustworthiness annotations. We develop a detailed methodology that assesses the texts based on their parameters encompassing editorial transparency, journalist conventions, and objective reporting while penalizing manipulative techniques. We bring aboard a diverse set of researchers from social, media, and computer sciences to overcome barriers and limited framing of this interdisciplinary problem. We collect over $10,000$ unique articles from almost $60$ Czech online news sources. These are categorized into one of the $4$ classes across the credibility spectrum we propose, raging from entirely trustworthy articles all the way to the manipulative ones. We produce detailed statistics and study trends emerging throughout the set. Lastly, we fine-tune multiple popular sequence-to-sequence language models using our dataset on the trustworthiness classification task and report the best testing F-1 score of $0.52$. We open-source the dataset, annotation methodology, and annotators' instructions in full length at https://verifee.ai/research to enable easy build-up work. We believe similar methods can help prevent disinformation and educate in the realm of media literacy.

Artificial intelligence (AI) technologies revolutionize vast fields of society. Humans using these systems are likely to expect them to work in a potentially hyperrational manner. However, in this study, we show that some AI systems, namely large language models (LLMs), exhibit behavior that strikingly resembles human-like intuition - and the many cognitive errors that come with them. We use a state-of-the-art LLM, namely the latest iteration of OpenAI's Generative Pre-trained Transformer (GPT-3.5), and probe it with the Cognitive Reflection Test (CRT) as well as semantic illusions that were originally designed to investigate intuitive decision-making in humans. Our results show that GPT-3.5 systematically exhibits "machine intuition," meaning that it produces incorrect responses that are surprisingly equal to how humans respond to the CRT as well as to semantic illusions. We investigate several approaches to test how sturdy GPT-3.5's inclination for intuitive-like decision-making is. Our study demonstrates that investigating LLMs with methods from cognitive science has the potential to reveal emergent traits and adjust expectations regarding their machine behavior.

Searching for a path between two nodes in a graph is one of the most well-studied and fundamental problems in computer science. In numerous domains such as robotics, AI, or biology, practitioners develop search heuristics to accelerate their pathfinding algorithms. However, it is a laborious and complex process to hand-design heuristics based on the problem and the structure of a given use case. Here we present PHIL (Path Heuristic with Imitation Learning), a novel neural architecture and a training algorithm for discovering graph search and navigation heuristics from data by leveraging recent advances in imitation learning and graph representation learning. At training time, we aggregate datasets of search trajectories and ground-truth shortest path distances, which we use to train a specialized graph neural network-based heuristic function using backpropagation through steps of the pathfinding process. Our heuristic function learns graph embeddings useful for inferring node distances, runs in constant time independent of graph sizes, and can be easily incorporated in an algorithm such as A* at test time. Experiments show that PHIL reduces the number of explored nodes compared to state-of-the-art methods on benchmark datasets by 58.5\% on average, can be directly applied in diverse graphs ranging from biological networks to road networks, and allows for fast planning in time-critical robotics domains.