API-Assisted Code Generation for Question Answering on Varied Table Structures

A persistent challenge to table question answering (TableQA) by generating executable programs has been adapting to varied table structures, typically requiring domain-specific logical forms. In response, this paper introduces a unified TableQA framework that: (1) provides a unified representation for structured tables as multi-index Pandas data frames, (2) uses Python as a powerful querying language, and (3) uses few-shot prompting to translate NL questions into Python programs, which are executable on Pandas data frames. Furthermore, to answer complex relational questions with extended program functionality and external knowledge, our framework allows customized APIs that Python programs can call. We experiment with four TableQA datasets that involve tables of different structures -- relational, multi-table, and hierarchical matrix shapes -- and achieve prominent improvements over past state-of-the-art systems. In ablation studies, we (1) show benefits from our multi-index representation and APIs over baselines that use only an LLM, and (2) demonstrate that our approach is modular and can incorporate additional APIs.Comment: EMNLP 2023 camera ready, 13 pages, 11 figure

NLQxform: A Language Model-based Question to SPARQL Transformer

In recent years, scholarly data has grown dramatically in terms of both scale and complexity. It becomes increasingly challenging to retrieve information from scholarly knowledge graphs that include large-scale heterogeneous relationships, such as authorship, affiliation, and citation, between various types of entities, e.g., scholars, papers, and organizations. As part of the Scholarly QALD Challenge, this paper presents a question-answering (QA) system called NLQxform, which provides an easy-to-use natural language interface to facilitate accessing scholarly knowledge graphs. NLQxform allows users to express their complex query intentions in natural language questions. A transformer-based language model, i.e., BART, is employed to translate questions into standard SPARQL queries, which can be evaluated to retrieve the required information. According to the public leaderboard of the Scholarly QALD Challenge at ISWC 2023 (Task 1: DBLP-QUAD - Knowledge Graph Question Answering over DBLP), NLQxform achieved an F1 score of 0.85 and ranked first on the QA task, demonstrating the competitiveness of the system

Learning to Filter Context for Retrieval-Augmented Generation

On-the-fly retrieval of relevant knowledge has proven an essential element of reliable systems for tasks such as open-domain question answering and fact verification. However, because retrieval systems are not perfect, generation models are required to generate outputs given partially or entirely irrelevant passages. This can cause over- or under-reliance on context, and result in problems in the generated output such as hallucinations. To alleviate these problems, we propose FILCO, a method that improves the quality of the context provided to the generator by (1) identifying useful context based on lexical and information-theoretic approaches, and (2) training context filtering models that can filter retrieved contexts at test time. We experiment on six knowledge-intensive tasks with FLAN-T5 and LLaMa2, and demonstrate that our method outperforms existing approaches on extractive question answering (QA), complex multi-hop and long-form QA, fact verification, and dialog generation tasks. FILCO effectively improves the quality of context, whether or not it supports the canonical output

K-BERT: Enabling Language Representation with Knowledge Graph

Pre-trained language representation models, such as BERT, capture a general language representation from large-scale corpora, but lack domain-specific knowledge. When reading a domain text, experts make inferences with relevant knowledge. For machines to achieve this capability, we propose a knowledge-enabled language representation model (K-BERT) with knowledge graphs (KGs), in which triples are injected into the sentences as domain knowledge. However, too much knowledge incorporation may divert the sentence from its correct meaning, which is called knowledge noise (KN) issue. To overcome KN, K-BERT introduces soft-position and visible matrix to limit the impact of knowledge. K-BERT can easily inject domain knowledge into the models by equipped with a KG without pre-training by-self because it is capable of loading model parameters from the pre-trained BERT. Our investigation reveals promising results in twelve NLP tasks. Especially in domain-specific tasks (including finance, law, and medicine), K-BERT significantly outperforms BERT, which demonstrates that K-BERT is an excellent choice for solving the knowledge-driven problems that require experts.Comment: 8 pages, 2019091

Improving Factuality of Abstractive Summarization via Contrastive Reward Learning

Modern abstractive summarization models often generate summaries that contain hallucinated or contradictory information. In this paper, we propose a simple but effective contrastive learning framework that incorporates recent developments in reward learning and factuality metrics. Empirical studies demonstrate that the proposed framework enables summarization models to learn from feedback of factuality metrics using contrastive reward learning, leading to more factual summaries by human evaluations. This suggests that further advances in learning and evaluation algorithms can feed directly into providing more factual summaries.Comment: TrustNLP @ ACL 202

SPAE: Semantic Pyramid AutoEncoder for Multimodal Generation with Frozen LLMs

In this work, we introduce Semantic Pyramid AutoEncoder (SPAE) for enabling frozen LLMs to perform both understanding and generation tasks involving non-linguistic modalities such as images or videos. SPAE converts between raw pixels and interpretable lexical tokens (or words) extracted from the LLM's vocabulary. The resulting tokens capture both the semantic meaning and the fine-grained details needed for visual reconstruction, effectively translating the visual content into a language comprehensible to the LLM, and empowering it to perform a wide array of multimodal tasks. Our approach is validated through in-context learning experiments with frozen PaLM 2 and GPT 3.5 on a diverse set of image understanding and generation tasks. Our method marks the first successful attempt to enable a frozen LLM to generate image content while surpassing state-of-the-art performance in image understanding tasks, under the same setting, by over 25%.Comment: NeurIPS 2023 spotligh

Retinal microvasculature features in patients with migraine: a systematic review and meta-analysis

BackgroundMigraine is a central nervous system disorder involving neuronal and vascular factors. The brain has a close anatomical relationship with retinal vessels and similar regulatory processes, and the retinal vascular system is the only in vivo vessel that can be directly visualized, while optical coherence tomography angiography (OCTA) is an advanced retinal vascular imaging technique. In this study, OCTA was used to study the retinal vascular density (VD) and foveal avascular zone (FAZ) in migraine patients, which provided a theoretical basis for its use as a candidate for rapid and non-invasive diagnosis of migraine.MethodsPublished studies comparing retinal microvascular profiles between migraine patients and healthy controls were obtained by a comprehensive search of electronic databases. Nine studies were finally included, including 775 eyes (migraine group: 444 eyes, control group: 331 eyes). Pooled effect sizes were presented as standardized mean differences (SMDs) and 95% confidence intervals (CIs). Statistical analysis was performed using Review Manager software (version 5.30).ResultsThe combined results revealed that the superficial and deep macular whole enface VD (MWEVD) (superficial VD: SMD = −0.30, P = 0.0001; deep VD: SMD = −0.61, P = 0.02), superficial foveal VD (FVD) (SMD = −0.42, P = 0.03), deep parafoveal VD (PFVD) (SMD = −0.31, P = 0.002), and peripapillary VD (PVD) (SMD = −0.49, P = 0.002) were significantly reduced in migraine patients compared with healthy people. However, there was a significant increase in the area of the FAZ in migraine patients (SMD = 0.56, P < 0.0001).ConclusionMigraine patients are prone to retinal microcirculation disorders, such as decreased blood vessel density and increased avascular area in the fovea. This provides a theoretical basis for OCTA as a candidate for rapid, non-invasive diagnosis of migraine

Flow Identification for Supporting Resource Reservation

This paper considers the problem of flow identification for supporting resource reservation. We propose several hashing-based schemes for flow identification and present a quantitative analysis of their performance and scalability limits. Of the hash functions we studied using simulation with real traffic traces, 32-bit CRC and XOR-folding of the five-tuple demonstrate excellent performance, both on the memory requirement for a collision rate target, and on the number of collided flows on average and in the worst-case. Our findings show that, with hashing-based schemes, it is feasible to implement flow identification at high speeds to support hundreds of thousands of reserved flows