Bring Your Own KG: Self-Supervised Program Synthesis for Zero-Shot KGQA

We present BYOKG, a universal question-answering (QA) system that can operate on any knowledge graph (KG), requires no human-annotated training data, and can be ready to use within a day -- attributes that are out-of-scope for current KGQA systems. BYOKG draws inspiration from the remarkable ability of humans to comprehend information present in an unseen KG through exploration -- starting at random nodes, inspecting the labels of adjacent nodes and edges, and combining them with their prior world knowledge. In BYOKG, exploration leverages an LLM-backed symbolic agent that generates a diverse set of query-program exemplars, which are then used to ground a retrieval-augmented reasoning procedure to predict programs for arbitrary questions. BYOKG is effective over both small- and large-scale graphs, showing dramatic gains in QA accuracy over a zero-shot baseline of 27.89 and 58.02 F1 on GrailQA and MetaQA, respectively. On GrailQA, we further show that our unsupervised BYOKG outperforms a supervised in-context learning method, demonstrating the effectiveness of exploration. Lastly, we find that performance of BYOKG reliably improves with continued exploration as well as improvements in the base LLM, notably outperforming a state-of-the-art fine-tuned model by 7.08 F1 on a sub-sampled zero-shot split of GrailQA.

Machine Reading Comprehension using Case-based Reasoning

We present an accurate and interpretable method for answer extraction in machine reading comprehension that is reminiscent of case-based reasoning (CBR) from classical AI. Our method (CBR-MRC) builds on the hypothesis that contextualized answers to similar questions share semantic similarities with each other. Given a target question, CBR-MRC retrieves a set of similar questions from a memory of observed cases and predicts an answer by selecting the span in the target context that is most similar to the contextualized representations of answers in the retrieved cases. The semi-parametric nature of our approach allows CBR-MRC to attribute a prediction to the specific set of cases used during inference, making it a desirable choice for building reliable and debuggable QA systems. We show that CBR-MRC achieves high test accuracy comparable with large reader models, outperforming baselines by 11.5 and 8.4 EM on NaturalQuestions and NewsQA, respectively. Further, we also demonstrate the ability of CBR-MRC in identifying not just the correct answer tokens but also the span with the most relevant supporting evidence. Lastly, we observe that contexts for certain question types show higher lexical diversity than others and find CBR-MRC to be robust to these variations while performance using fully-parametric methods drops.

When Not to Trust Language Models: Investigating Effectiveness and Limitations of Parametric and Non-Parametric Memories

Despite their impressive performance on diverse tasks, large language models (LMs) still struggle with tasks requiring rich world knowledge, implying the limitations of relying solely on their parameters to encode a wealth of world knowledge. This paper aims to understand LMs' strengths and limitations in memorizing factual knowledge, by conducting large-scale knowledge probing experiments of 10 models and 4 augmentation methods on PopQA, our new open-domain QA dataset with 14k questions. We find that LMs struggle with less popular factual knowledge, and that scaling fails to appreciably improve memorization of factual knowledge in the tail. We then show that retrieval-augmented LMs largely outperform orders of magnitude larger LMs, while unassisted LMs remain competitive in questions about high-popularity entities. Based on those findings, we devise a simple, yet effective, method for powerful and efficient retrieval-augmented LMs, which retrieves non-parametric memories only when necessary. Experimental results show that this significantly improves models' performance while reducing the inference costs.

CBR-iKB: A Case-Based Reasoning Approach for Question Answering over Incomplete Knowledge Bases

Knowledge bases (KBs) are often incomplete and constantly changing in practice. Yet, in many question answering applications coupled with knowledge bases, the sparse nature of KBs is often overlooked. To this end, we propose a case-based reasoning approach, CBR-iKB, for knowledge base question answering (KBQA) with incomplete-KB as our main focus. Our method ensembles decisions from multiple reasoning chains with a novel nonparametric reasoning algorithm. By design, CBR-iKB can seamlessly adapt to changes in KBs without any task-specific training or fine-tuning. Our method achieves 100% accuracy on MetaQA and establishes new state-of-the-art on multiple benchmarks. For instance, CBR-iKB achieves an accuracy of 70% on WebQSP under the incomplete-KB setting, outperforming the existing state-of-the-art method by 22.3%.

Calibration of Machine Reading Systems at Scale

In typical machine learning systems, an estimate of the probability of the prediction is used to assess the system's confidence in the prediction. This confidence measure is usually uncalibrated; i.e.\ the system's confidence in the prediction does not match the true probability of the predicted output. In this paper, we present an investigation into calibrating open setting machine reading systems such as open-domain question answering and claim verification systems. We show that calibrating such complex systems which contain discrete retrieval and deep reading components is challenging and current calibration techniques fail to scale to these settings. We propose simple extensions to existing calibration approaches that allows us to adapt them to these settings. Our experimental results reveal that the approach works well, and can be useful to selectively predict answers when question answering systems are posed with unanswerable or out-of-the-training distribution questions.

Knowledge Base Question Answering by Case-based Reasoning over Subgraphs

Question answering (QA) over real-world knowledge bases (KBs) is challenging because of the diverse (essentially unbounded) types of reasoning patterns needed. However, we hypothesize in a large KB, reasoning patterns required to answer a query type reoccur for various entities in their respective subgraph neighborhoods. Leveraging this structural similarity between local neighborhoods of different subgraphs, we introduce a semiparametric model with (i) a nonparametric component that for each query, dynamically retrieves other similar $k$-nearest neighbor (KNN) training queries along with query-specific subgraphs and (ii) a parametric component that is trained to identify the (latent) reasoning patterns from the subgraphs of KNN queries and then apply it to the subgraph of the target query. We also propose a novel algorithm to select a query-specific compact subgraph from within the massive knowledge graph (KG), allowing us to scale to full Freebase KG containing billions of edges. We show that our model answers queries requiring complex reasoning patterns more effectively than existing KG completion algorithms. The proposed model outperforms or performs competitively with state-of-the-art models on several KBQA benchmarks.

A Dataset for Discourse Structure in Peer Review Discussions

At the foundation of scientific evaluation is the labor-intensive process of peer review. This critical task requires participants to consume and interpret vast amounts of highly technical text. We show that discourse cues from rebuttals can shed light on the quality and interpretation of reviews. Further, an understanding of the argumentative strategies employed by the reviewers and authors provides useful signal for area chairs and other decision makers. This paper presents a new labeled dataset of 20k sentences contained in 506 review-rebuttal pairs in English, annotated by experts. While existing datasets annotate a subset of review sentences using various schemes, ours synthesizes existing label sets and extends them to include fine-grained annotation of the rebuttal sentences, characterizing the authors' stance towards the reviewers' criticisms and their commitment to addressing them. Further, we annotate \textit{every} sentence in both the review and the rebuttal, including a description of the context for each rebuttal sentence.

Case-based Reasoning for Natural Language Queries over Knowledge Bases

It is often challenging for a system to solve a new complex problem from scratch, but much easier if the system can access other similar problems and description of their solutions -- a paradigm known as case-based reasoning (CBR). We propose a neuro-symbolic CBR approach for question answering over large knowledge bases (CBR-KBQA). While the idea of CBR is tempting, composing a solution from cases is nontrivial, when individual cases only contain partial logic to the full solution. To resolve this, CBR-KBQA consists of two modules: a non-parametric memory that stores cases (question and logical forms) and a parametric model which can generate logical forms by retrieving relevant cases from memory. Through experiments, we show that CBR-KBQA can effectively derive novel combination of relations not presented in case memory that is required to answer compositional questions. On several KBQA datasets that test compositional generalization, CBR-KBQA achieves competitive performance. For example, on the challenging ComplexWebQuestions dataset, CBR-KBQA outperforms the current state of the art by 11% accuracy. Furthermore, we show that CBR-KBQA is capable of using new cases \emph{without} any further training. Just by incorporating few human-labeled examples in the non-parametric case memory, CBR-KBQA is able to successfully generate queries containing unseen KB relations.

Long Document Summarization in a Low Resource Setting using Pretrained Language Models

Abstractive summarization is the task of compressing a long document into a coherent short document while retaining salient information. Modern abstractive summarization methods are based on deep neural networks which often require large training datasets. Since collecting summarization datasets is an expensive and time-consuming task, practical industrial settings are usually low-resource. In this paper, we study a challenging low-resource setting of summarizing long legal briefs with an average source document length of 4268 words and only 120 available (document, summary) pairs. To account for data scarcity, we used a modern pretrained abstractive summarizer BART (Lewis et al., 2020), which only achieves 17.9 ROUGE-L as it struggles with long documents. We thus attempt to compress these long documents by identifying salient sentences in the source which best ground the summary, using a novel algorithm based on GPT-2 (Radford et al., 2019) language model perplexity scores, that operates within the low resource regime. On feeding the compressed documents to BART, we observe a 6.0 ROUGE-L improvement. Our method also beats several competitive salience detection baselines. Furthermore, the identified salient sentences tend to agree with an independent human labeling by domain experts.

Probabilistic Case-based Reasoning for Open-World Knowledge Graph Completion

A case-based reasoning (CBR) system solves a new problem by retrieving `cases' that are similar to the given problem. If such a system can achieve high accuracy, it is appealing owing to its simplicity, interpretability, and scalability. In this paper, we demonstrate that such a system is achievable for reasoning in knowledge-bases (KBs). Our approach predicts attributes for an entity by gathering reasoning paths from similar entities in the KB. Our probabilistic model estimates the likelihood that a path is effective at answering a query about the given entity. The parameters of our model can be efficiently computed using simple path statistics and require no iterative optimization. Our model is non-parametric, growing dynamically as new entities and relations are added to the KB. On several benchmark datasets our approach significantly outperforms other rule learning approaches and performs comparably to state-of-the-art embedding-based approaches. Furthermore, we demonstrate the effectiveness of our model in an "open-world" setting where new entities arrive in an online fashion, significantly outperforming state-of-the-art approaches and nearly matching the best offline method. Code available at https://github.com/ameyagodbole/Prob-CBR