Large language models (LLMs) excel at answering questions but remain passive learners—absorbing static data without the ability to question and refine knowledge. This paper explores how LLMs can transition to interactive, question-driven learning through student-teacher dialogues. We introduce INTERACT (INTERactive learning for Adaptive Concept Transfer), a framework in which a “student” LLM engages a “teacher” LLM through iterative inquiries to acquire knowledge across 1,347 contexts, including song lyrics, news articles, movie plots, academic papers, and images. Our experiments show that across a wide range of scenarios and LLM architectures, interactive learning consistently enhances performance, achieving up to a 25% improvement, with ‘cold-start’ student models matching static learning baselines in as few as five dialogue turns. Interactive setups can also mitigate the disadvantages of weaker teachers, showcasing the robustness of question-driven learning.

With the growing adoption of machine learning models in critical domains, techniques for explaining differences between models have become essential for trust, debugging, and informed deployment.Previous approaches address this by identifying input transformations that cause divergent predictions (Shah et al., 2022) or by learning joint surrogate models to align and contrast behaviors (Haldar et al., 2023).These methods often require access to training data and do not produce natural language explanations.In this paper, we introduce SLED , a framework that generates faithful natural language explanations of when and how two ML models converge or diverge in their predictions.SLED first uses gradient-based optimization to synthesize input samples that highlight divergence and convergence patterns, and then leverages a large language model (LLM) to generate explanations grounded in these synthetic samples.Across both text-based (3 tasks, 7 models) and structured (10 tasks, 4 models) classification tasks, we show that SLED explanations are 18-24% more faithful than the strongest baselines.User studies also indicate that SLED explanations achieve a real-world simulatability of 63.5%.Importantly, SLED requires minimal access to training data and generalizes well to real-world samples, enabling transparent model comparison. 1

Despite their high predictive accuracies, current machine learning systems often exhibit systematic biases stemming from annotation artifacts or insufficient support for certain classes in the dataset.Recent work proposes automatic methods for identifying and explaining systematic biases using keywords.We introduce DIS-CERN, a framework for interpreting systematic biases in text classifiers using language explanations.DISCERN iteratively generates precise natural language descriptions of systematic errors by employing an interactive loop between two large language models.Finally, we use the descriptions to improve classifiers by augmenting classifier training sets with synthetically generated instances or annotated examples via active learning.On three text-classification datasets, we demonstrate that language explanations from our framework induce consistent performance improvements that go beyond what is achievable with exemplars of systematic bias.Finally, in human evaluations, we show that users can interpret systematic biases more effectively (by over 25% relative) and efficiently when described through language explanations as opposed to cluster exemplars.1

Generalized quantifiers (e.g., few, most) are used to indicate the proportions predicates are satisfied (for example, some apples are red).One way to interpret quantifier semantics is to explicitly bind these satisfactions with percentage scopes (e.g., 30%-40% of apples are red).This approach can be helpful for tasks like logic formalization and surface-form quantitative reasoning (Gordon and Schubert, 2010;Roy et al., 2015).However, it remains unclear if recent foundation models possess this ability, as they lack direct training signals.To explore this, we introduce QuRe, a crowdsourced dataset of human-annotated generalized quantifiers in Wikipedia sentences featuring percentage-equipped predicates.We explore quantifier comprehension in language models using PRESQUE, a framework that combines natural language inference and the Rational Speech Acts framework.Experimental results on the HVD dataset and QuRe illustrate that PRESQUE, employing pragmatic reasoning, performs 20% better than a literal reasoning baseline when predicting quantifier percentage scopes, with no additional training required 1 .

Understanding the internal reasoning behind the predictions of machine learning systems is increasingly vital, given their rising adoption and acceptance.While previous approaches, such as LIME generate algorithmic explanations by attributing importance to input features for individual examples, recent research indicates that practitioners prefer examining language explanations that explain sub-groups of examples (Lakkaraju et al., 2022).In this paper, we introduce MaNtLE, a model-agnostic natural language explainer that analyzes a set of classifier predictions and generates faithful natural language explanations of classifier rationale for structured classification tasks.MaNtLE uses multi-task training on thousands of synthetic classification tasks to generate faithful explanations.Our experiments indicate that, on average, MaNtLE-generated explanations are at least 11% more faithful compared to LIME and Anchors explanations across three tasks.Human evaluations demonstrate that users predict model behavior better using explanations from MaNtLE compared to other techniques. 1

Supervised learning has traditionally focused on inductive learning by observing labeled examples of a task.In contrast, humans have the ability to learn new concepts from language.Here, we explore learning zero-shot classifiers for structured data 1 purely from language from natural language explanations as supervision.For this, we introduce CLUES, a benchmark for Classifier Learning Using natural language ExplanationS, consisting of a range of classification tasks over structured data along with natural language supervision in the form of explanations.CLUES consists of 36 real-world and 144 synthetic classification tasks.It contains crowdsourced explanations describing real-world tasks from multiple teachers and programmatically generated explanations for the synthetic tasks.We also introduce ExEnt, an entailment-based method for training classifiers from language explanations, which explicitly models the influence of individual explanations in making a prediction.ExEnt generalizes up to 18% better (relative) on novel tasks than a baseline that does not use explanations.We identify key challenges in learning from explanations, addressing which can lead to progress on CLUES in the future.Our code and datasets are available at: https: //clues-benchmark.github.io.

Recently, pre-trained language models (LMs) have achieved strong performance when finetuned on difficult benchmarks like Super-GLUE.However, performance can suffer when there are very few labeled examples available for fine-tuning.Pattern Exploiting Training (PET) is a recent approach that leverages patterns for few-shot learning.However, PET uses task-specific unlabeled data.In this paper, we focus on few shot learning without any unlabeled data and introduce ADAPET, which modifies PET's objective to provide denser supervision during fine-tuning.As a result, ADAPET outperforms PET on Su-perGLUE without any task-specific unlabeled data.

Traditional methods of motion segmentation use powerful geometric constraints to understand motion, but fail to leverage the semantics of high-level image understanding. Modern CNN methods of motion analysis, on the other hand, excel at identifying well-known structures, but may not precisely characterize well-known geometric constraints. In this work, we build a new statistical model of rigid motion flow based on classical perspective projection constraints. We then combine piecewise rigid motions into complex deformable and articulated objects, guided by semantic segmentation from CNNs and a second "object-level" statistical model. This combination of classical geometric knowledge combined with the pattern recognition abilities of CNNs yields excellent performance on a wide range of motion segmentation benchmarks, from complex geometric scenes to camouflaged animals.