Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs

In this study, we introduce adaptive KV cache compression, a plug-and-play method that reduces the memory footprint of generative inference for Large Language Models (LLMs). Different from the conventional KV cache that retains key and value vectors for all context tokens, we conduct targeted profiling to discern the intrinsic structure of attention modules. Based on the recognized structure, we then construct the KV cache in an adaptive manner: evicting long-range contexts on attention heads emphasizing local contexts, discarding non-special tokens on attention heads centered on special tokens, and only employing the standard KV cache for attention heads that broadly attend to all tokens. Moreover, with the lightweight attention profiling used to guide the construction of the adaptive KV cache, FastGen can be deployed without resource-intensive fine-tuning or re-training. In our experiments across various asks, FastGen demonstrates substantial reduction on GPU memory consumption with negligible generation quality loss. We will release our code and the compatible CUDA kernel for reproducibility.

Open-Domain Hierarchical Event Schema Induction by Incremental Prompting and Verification

Event schemas are a form of world knowledge about the typical progression of events. Recent methods for event schema induction use information extraction systems to construct a large number of event graph instances from documents, and then learn to generalize the schema from such instances. In contrast, we propose to treat event schemas as a form of commonsense knowledge that can be derived from large language models (LLMs). This new paradigm greatly simplifies the schema induction process and allows us to handle both hierarchical relations and temporal relations between events in a straightforward way. Since event schemas have complex graph structures, we design an incremental prompting and verification method to break down the construction of a complex event graph into three stages: event skeleton construction, event expansion, and event-event relation verification. Compared to directly using LLMs to generate a linearized graph, our method can generate large and complex schemas with 7.2% F1 improvement in temporal relations and 31.0% F1 improvement in hierarchical relations. In addition, compared to the previous state-of-the-art closed-domain schema induction model, human assessors were able to cover $\sim$10% more events when translating the schemas into coherent stories and rated our schemas 1.3 points higher (on a 5-point scale) in terms of readability.

ReactIE: Enhancing Chemical Reaction Extraction with Weak Supervision

Structured chemical reaction information plays a vital role for chemists engaged in laboratory work and advanced endeavors such as computer-aided drug design. Despite the importance of extracting structured reactions from scientific literature, data annotation for this purpose is cost-prohibitive due to the significant labor required from domain experts. Consequently, the scarcity of sufficient training data poses an obstacle to the progress of related models in this domain. In this paper, we propose ReactIE, which combines two weakly supervised approaches for pre-training. Our method utilizes frequent patterns within the text as linguistic cues to identify specific characteristics of chemical reactions. Additionally, we adopt synthetic data from patent records as distant supervision to incorporate domain knowledge into the model. Experiments demonstrate that ReactIE achieves substantial improvements and outperforms all existing baselines.

Weakly Supervised Multi-Label Classification of Full-Text Scientific Papers

Instead of relying on human-annotated training samples to build a classifier, weakly supervised scientific paper classification aims to classify papers only using category descriptions (e.g., category names, category-indicative keywords). Existing studies on weakly supervised paper classification are less concerned with two challenges: (1) Papers should be classified into not only coarse-grained research topics but also fine-grained themes, and potentially into multiple themes, given a large and fine-grained label space; and (2) full text should be utilized to complement the paper title and abstract for classification. Moreover, instead of viewing the entire paper as a long linear sequence, one should exploit the structural information such as citation links across papers and the hierarchy of sections and paragraphs in each paper. To tackle these challenges, in this study, we propose FUTEX, a framework that uses the cross-paper network structure and the in-paper hierarchy structure to classify full-text scientific papers under weak supervision. A network-aware contrastive fine-tuning module and a hierarchy-aware aggregation module are designed to leverage the two types of structural signals, respectively. Experiments on two benchmark datasets demonstrate that FUTEX significantly outperforms competitive baselines and is on par with fully supervised classifiers that use 1,000 to 60,000 ground-truth training samples.

Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation From Deductive, Inductive and Abductive Views

Large Language Models (LLMs) have achieved great success in various natural language tasks. It has aroused much interest in evaluating the specific reasoning capability of LLMs, such as multilingual reasoning and mathematical reasoning. However, as one of the key reasoning perspectives, logical reasoning capability has not yet been thoroughly evaluated. In this work, we aim to bridge those gaps and provide comprehensive evaluations. Firstly, to offer systematic evaluations, this paper selects fifteen typical logical reasoning datasets and organizes them into deductive, inductive, abductive and mixed-form reasoning settings. Considering the comprehensiveness of evaluations, we include three representative LLMs (i.e., text-davinci-003, ChatGPT and BARD) and evaluate them on all selected datasets under zero-shot, one-shot and three-shot settings. Secondly, different from previous evaluations relying only on simple metrics (e.g., accuracy), we propose fine-level evaluations from objective and subjective manners, covering both answers and explanations. Also, to uncover the logical flaws of LLMs, bad cases will be attributed to five error types from two dimensions. Thirdly, to avoid the influences of knowledge bias and purely focus on benchmarking the logical reasoning capability of LLMs, we propose a new dataset with neutral content. It contains 3K samples and covers deductive, inductive and abductive reasoning settings. Based on the in-depth evaluations, this paper finally concludes the ability maps of logical reasoning capability from six dimensions (i.e., correct, rigorous, self-aware, active, oriented and no hallucination). It reflects the pros and cons of LLMs and gives guiding directions for future works.

Explaining and Adapting Graph Conditional Shift

Graph Neural Networks (GNNs) have shown remarkable performance on graph-structured data. However, recent empirical studies suggest that GNNs are very susceptible to distribution shift. There is still significant ambiguity about why graph-based models seem more vulnerable to these shifts. In this work we provide a thorough theoretical analysis on it by quantifying the magnitude of conditional shift between the input features and the output label. Our findings show that both graph heterophily and model architecture exacerbate conditional shifts, leading to performance degradation. To address this, we propose an approach that involves estimating and minimizing the conditional shift for unsupervised domain adaptation on graphs. In our controlled synthetic experiments, our algorithm demonstrates robustness towards distribution shift, resulting in up to 10% absolute ROC AUC improvement versus the second-best algorithm. Furthermore, comprehensive experiments on both node classification and graph classification show its robust performance under various distribution shifts.

Text-Augmented Open Knowledge Graph Completion via Pre-Trained Language Models

The mission of open knowledge graph (KG) completion is to draw new findings from known facts. Existing works that augment KG completion require either (1) factual triples to enlarge the graph reasoning space or (2) manually designed prompts to extract knowledge from a pre-trained language model (PLM), exhibiting limited performance and requiring expensive efforts from experts. To this end, we propose TAGREAL that automatically generates quality query prompts and retrieves support information from large text corpora to probe knowledge from PLM for KG completion. The results show that TAGREAL achieves state-of-the-art performance on two benchmark datasets. We find that TAGREAL has superb performance even with limited training data, outperforming existing embedding-based, graph-based, and PLM-based methods.

Dynosaur: A Dynamic Growth Paradigm for Instruction-Tuning Data Curation

Instruction tuning has emerged to enhance the capabilities of large language models (LLMs) in providing appropriate outputs based on input instructions. However, existing methods for collecting instruction-tuning data suffer from limitations in scalability and affordability. In this paper, we propose Dynosaur, a dynamic growth paradigm for instruction-tuning data curation. Built upon the metadata of existing NLP datasets, we generate multiple task instructions applicable to various NLP datasets and determine the relevant data fields for constructing instruction-tuning data with LLMs. Dynosaur offers several advantages: 1) lower generation costs (less than $12 for generating 800K instruction-tuning data), 2) good quality of instruction-tuning data (better performance than Alpaca and Instruction GPT-4 on Super-NI with comparable data sizes), and 3) the ability to grow dynamically by incorporating new datasets from Huggingface Datasets Platform. We further investigate continual learning as an approach to learning with the ever-growing instruction-tuning dataset. We demonstrate that replay methods not only help mitigate forgetting issues but help generalize to unseen tasks better. As a novel continual learning scenario for instruction tuning, selecting tasks based on instruction representations can be an effective replaying strategy. Code and data are released at \url{https://github.com/WadeYin9712/Dynosaur}.

PromptClass: Weakly-Supervised Text Classification with Prompting Enhanced Noise-Robust Self-Training

Recently proposed weakly-supervised text classification settings train a classifier using the label name of each target class as the only supervision. Such weakly-supervised settings have been gaining increasing attention since they can largely reduce human annotation efforts compared to fully-supervised and semi-supervised settings. Most existing methods follow the strategy that first uses the label names as static features to generate pseudo labels, which are then used for classifier training. While reasonable, such a commonly adopted framework suffers from two limitations: (1) words can have different meanings in different contexts, so using label names for context-free matching can induce very noisy pseudo labels; and (2) the errors made in the pseudo label generation stage will directly propagate to the classifier training stage without a chance of being corrected. In this paper, we propose a new method, PromptClass, consisting of two modules: (1) a pseudo label acquisition module that uses zero-shot prompting of pre-trained language models (PLM) to get pseudo labels based on contextualized text understanding, and (2) a noise-robust self-training module that iteratively trains the classifier and updates pseudo labels by utilizing two PLM fine-tuning strategies that regularize each other. Extensive experiments show that PromptClass achieves overall better performance than existing strong baselines on four benchmark datasets and even achieves similar performance to fully-supervised classifiers on sentiment classification tasks.

OntoType: Ontology-Guided Zero-Shot Fine-Grained Entity Typing with Weak Supervision from Pre-Trained Language Models

Fine-grained entity typing (FET), which assigns entities in text with context-sensitive, fine-grained semantic types, will play an important role in natural language understanding. A supervised FET method, which typically relies on human-annotated corpora for training, is costly and difficult to scale. Recent studies leverage pre-trained language models (PLMs) to generate rich and context-aware weak supervision for FET. However, a PLM may still generate a mixture of rough and fine-grained types, or tokens unsuitable for typing. In this study, we vision that an ontology provides a semantics-rich, hierarchical structure, which will help select the best results generated by multiple PLM models and head words. Specifically, we propose a novel zero-shot, ontology-guided FET method, OntoType, which follows a type ontological structure, from coarse to fine, ensembles multiple PLM prompting results to generate a set of type candidates, and refines its type resolution, under the local context with a natural language inference model. Our experiments on the Ontonotes, FIGER, and NYT datasets using their associated ontological structures demonstrate that our method outperforms the state-of-the-art zero-shot fine-grained entity typing methods. Our error analysis shows that refinement of the existing ontology structures will further improve fine-grained entity typing.