AutoSense Model for Word Sense Induction

Word sense induction (WSI), or the task of automatically discovering multiple senses or meanings of a word, has three main challenges: domain adaptability, novel sense detection, and sense granularity flexibility. While current latent variable models are known to solve the first two challenges, they are not flexible to different word sense granularities, which differ very much among words, from aardvark with one sense, to play with over 50 senses. Current models either require hyperparameter tuning or nonparametric induction of the number of senses, which we find both to be ineffective. Thus, we aim to eliminate these requirements and solve the sense granularity problem by proposing AutoSense, a latent variable model based on two observations: (1) senses are represented as a distribution over topics, and (2) senses generate pairings between the target word and its neighboring word. These observations alleviate the problem by (a) throwing garbage senses and (b) additionally inducing fine-grained word senses. Results show great improvements over the state-of-the-art models on popular WSI datasets. We also show that AutoSense is able to learn the appropriate sense granularity of a word. Finally, we apply AutoSense to the unsupervised author name disambiguation task where the sense granularity problem is more evident and show that AutoSense is evidently better than competing models. We share our data and code here: https://github.com/rktamplayo/AutoSense.Comment: AAAI 201

Evidentiality-aware Retrieval for Overcoming Abstractiveness in Open-Domain Question Answering

The long-standing goal of dense retrievers in abtractive open-domain question answering (ODQA) tasks is to learn to capture evidence passages among relevant passages for any given query, such that the reader produce factually correct outputs from evidence passages. One of the key challenge is the insufficient amount of training data with the supervision of the answerability of the passages. Recent studies rely on iterative pipelines to annotate answerability using signals from the reader, but their high computational costs hamper practical applications. In this paper, we instead focus on a data-centric approach and propose Evidentiality-Aware Dense Passage Retrieval (EADPR), which leverages synthetic distractor samples to learn to discriminate evidence passages from distractors. We conduct extensive experiments to validate the effectiveness of our proposed method on multiple abstractive ODQA tasks.Comment: Findings of EACL 202

Myotonic Dystrophy Type 1 Presenting as Male Infertility

Myotonic dystrophy 1 (DM1) is a multi-system disorder characterized by endocrine defects that include testicular and tubular atrophy, oligospermia and azoospermia, and increased follicle-stimulating hormone levels. We describe a rare case of DM1 presenting as infertility in a 29-year-old man

A High-Yield Fabrication Process for Silicon Neural Probes

There is a great need for silicon microelectrodes that can simultaneously monitor the activity of many neurons in the brain. However, one of the existing processes for fabricating silicon microelectrodes—reactive- ion etching in combination with anisotropic KOH etching—breaks down at the wet-etching step for device release. Here we describe amodified wet-etching sidewall-protection technique for the high-yield fabrication of well-defined silicon probe structures, using a Teflon® shield and low-pressure chemical vapor deposition (LPCVD) silicon nitride. In the proposed method, a micro-tab holds each individual probe to the central scaffold, allowing uniform anisotropicKOHetching. Using this approach, we obtained a well-defined probe structure without device loss during the wet-etching process. This simple method yielded more accurate fabrication and an improved mechanical profile.This work was supported in part by the Korean Science and Foundation (KOSEF) through the Nano-Bioelectronics and Systems Research Center, Seoul National Universit

Coffee: Boost Your Code LLMs by Fixing Bugs with Feedback

Code editing is an essential step towards reliable program synthesis to automatically correct critical errors generated from code LLMs. Recent studies have demonstrated that closed-source LLMs (i.e., ChatGPT and GPT-4) are capable of generating corrective feedback to edit erroneous inputs. However, it remains challenging for open-source code LLMs to generate feedback for code editing, since these models tend to adhere to the superficial formats of feedback and provide feedback with misleading information. Hence, the focus of our work is to leverage open-source code LLMs to generate helpful feedback with correct guidance for code editing. To this end, we present Coffee, a collected dataset specifically designed for code fixing with feedback. Using this dataset, we construct CoffeePots, a framework for COde Fixing with FEEdback via Preference-Optimized Tuning and Selection. The proposed framework aims to automatically generate helpful feedback for code editing while minimizing the potential risk of superficial feedback. The combination of Coffee and CoffeePots marks a significant advancement, achieving state-of-the-art performance on HumanEvalFix benchmark. Codes and model checkpoints are publicly available at https://github.com/Lune-Blue/COFFEE.Comment: Work in progres

Challenges in Diagnosing Narcolepsy and Idiopathic Hypersomnia

Narcolepsy and idiopathic hypersomnia are central disorders of hypersomnolence accompanied by excessive daytime sleepiness, which are not caused by nocturnal sleep disturbance, sleep deficiency, or circadian rhythm sleep disorders. Several studies have questioned the repeatability of the Multiple Sleep Latency Test (MSLT) in type 2 narcolepsy (NT2) patients. After two or more repeated MSLTs, the diagnosis of type 1 narcolepsy (NT1) is maintained in more than 90% of cases, while only half of the NT2 patients retain their original diagnosis. The diagnosis of NT2 may shift to idiopathic hypersomnia based on the MSLT results, making the differential diagnosis of NT2 and idiopathic hypersomnia particularly challenging. Therefore, this study suggests the need for new tests in addition to the MSLT for diagnostic consistency in NT2 and idiopathic hypersomnia