222 research outputs found
Extracting Noun Phrases from Large-Scale Texts: A Hybrid Approach and Its Automatic Evaluation
To acquire noun phrases from running texts is useful for many applications,
such as word grouping,terminology indexing, etc. The reported literatures adopt
pure probabilistic approach, or pure rule-based noun phrases grammar to tackle
this problem. In this paper, we apply a probabilistic chunker to deciding the
implicit boundaries of constituents and utilize the linguistic knowledge to
extract the noun phrases by a finite state mechanism. The test texts are
SUSANNE Corpus and the results are evaluated by comparing the parse field of
SUSANNE Corpus automatically. The results of this preliminary experiment are
encouraging.Comment: 8 pages, Postscript file, Unix compressed, uuencode
Equity Building Actions of New Ventures in A High-Velocity Market: Research on Taiwan\u27s Internet Entrepreneurial Organizations
Based on the theories, such as the resources-based theory, new product development and strategic alliances, we proposed the equity-building actions of new ventures in the Internet industry. We note that the new venturesā purpose in capital raising actions before going public is not simply to raise funds, but to obtain rare resources and build core competence through equity invested or conjoined. Through interviews, we discuss factors that affect the equity-building process, and propose two propositions. Firstly, the original core resources of new ventures will affect the equity-building process. Especially, on target selecting, alliance timing, and alliance preference. Secondly, equity-building actions before IPO are parts of a growing strategy for emerging firms. The findings of this research are helpful in understanding the linkage between resource endowment and equity-building actions, and for new ventures to build up competitive advantages during founding period
Self-ICL: Zero-Shot In-Context Learning with Self-Generated Demonstrations
Large language models (LMs) have exhibited superior in-context learning (ICL)
ability to adopt to target tasks by prompting with a few input-output
demonstrations. Towards better ICL, different methods are proposed to select
representative demonstrations from existing training corpora. However, such a
setting is not aligned with real-world practices, as end-users usually query
LMs without accesses to demonstration pools. Inspired by evidence suggesting
LMs' zero-shot capabilities are underrated, and the role of demonstrations are
primarily for exposing models' intrinsic functionalities, we introduce
Self-ICL, a simple framework for zero-shot ICL. Given a test input, Self-ICL
first prompts the model to generate pseudo-inputs. Next, the model predicts
pseudo-labels for the pseudo-inputs via zero-shot prompting. Finally, we
construct pseudo-demonstrations from pseudo-input-label pairs, and perform ICL
for the test input. Evaluation on BIG-Bench Hard shows Self-ICL steadily
surpasses zero-shot and zero-shot chain-of-thought baselines on head-to-head
and all-task average performance. Our findings suggest the possibility to
bootstrap LMs' intrinsic capabilities towards better zero-shot performance.Comment: Work in progres
Large Language Models Perform Diagnostic Reasoning
We explore the extension of chain-of-thought (CoT) prompting to medical
reasoning for the task of automatic diagnosis. Motivated by doctors' underlying
reasoning process, we present Diagnostic-Reasoning CoT (DR-CoT). Empirical
results demonstrate that by simply prompting large language models trained only
on general text corpus with two DR-CoT exemplars, the diagnostic accuracy
improves by 15% comparing to standard prompting. Moreover, the gap reaches a
pronounced 18% in out-domain settings. Our findings suggest expert-knowledge
reasoning in large language models can be elicited through proper promptings.Comment: Accepted as a Tiny Paper at ICLR 2023 (10 pages, 5 figures
Fidelity-Enriched Contrastive Search: Reconciling the Faithfulness-Diversity Trade-Off in Text Generation
In this paper, we address the hallucination problem commonly found in natural
language generation tasks. Language models often generate fluent and convincing
content but can lack consistency with the provided source, resulting in
potential inaccuracies. We propose a new decoding method called
Fidelity-Enriched Contrastive Search (FECS), which augments the contrastive
search framework with context-aware regularization terms. FECS promotes tokens
that are semantically similar to the provided source while penalizing
repetitiveness in the generated text. We demonstrate its effectiveness across
two tasks prone to hallucination: abstractive summarization and dialogue
generation. Results show that FECS consistently enhances faithfulness across
various language model sizes while maintaining output diversity comparable to
well-performing decoding algorithms.Comment: Accepted as a short paper at EMNLP 202
ZARA: Improving Few-Shot Self-Rationalization for Small Language Models
Language models (LMs) that jointly generate end-task answers as well as
free-text rationales are known as self-rationalization models. Recent works
demonstrate great performance gain for self-rationalization by few-shot
prompting LMs with rationale-augmented exemplars. However, the ability to
benefit from explanations only emerges with large-scale LMs, which have poor
accessibility. In this work, we explore the less-studied setting of leveraging
explanations for small LMs to improve few-shot self-rationalization. We first
revisit the relationship between rationales and answers. Inspired by the
implicit mental process of how human beings assess explanations, we present a
novel approach, Zero-shot Augmentation of Rationale-Answer pairs (ZARA), to
automatically construct pseudo-parallel data for self-training by reducing the
problem of plausibility judgement to natural language inference. Experimental
results show ZARA achieves SOTA performance on the FEB benchmark, for both the
task accuracy and the explanation metric. In addition, we conduct human and
quantitative evaluation validating ZARA's ability to automatically identify
plausible and accurate rationale-answer pairs.Comment: Accepted as a long paper at EMNLP Findings 202
Sorafenib for hepatocellular carcinoma patients beyond Milan criteria after orthotopic liver transplantation: a case control study
<p>Abstract</p> <p>Background</p> <p>Orthotopic liver transplantation (OLT) is one of the most effective treatments for patients with hepatocellular carcinoma (HCC) within the Milan criteria. However, for patients beyond these criteria, the recurrence rate is higher and the prognosis is worse. Sorafenib is the only drug showing survival benefits in advanced HCC patients; however, its role in patients beyond the Milan criteria after OLT remains unclear and requires further investigation.</p> <p>Methods</p> <p>As a case-control study, we retrospectively analyzed 17 Chinese patients beyond Milan criteria undergoing OLT for HCC. These patients were stratified into adjuvant (n = 5), palliative (n = 6), and control groups (n = 6).</p> <p>Results</p> <p>Nine of 11 patients who received sorafenib after OLT needed dose reduction due to more than grade 2 side effects. The disease-free survival rates for patients with or without adjuvant sorafenib were 100% versus 37.5% (p = 0.034) at 6 months, 66.7% versus 9.4% (p = 0.026) at 12 months, and 66.7% versus 0.0% (p = 0.011) at 18 months, respectively. The overall survival rates for patients in palliative and control groups were 66.7% versus 40.0% (p = 0.248) at 6 months, 66.7% versus 40.0% (p = 0.248) at 12 months, and 50.0% versus 20.0% (p = 0.17) at 18 months, respectively. Patients in the adjuvant group had better overall survival rates than those in the palliative and control groups (p = 0.031) at 24-month follow-up.</p> <p>Conclusions</p> <p>Adjuvant sorafenib could possibly extend both disease-free and overall survival for HCC patients beyond Milan criteria after OLT.</p
Further evidence on bear market predictability: The role of the external finance premium
In this paper, we revisit bear market predictability by employing a number of variables widely used in forecasting stock returns. In particular, we focus on variables related to the presence of imperfect credit markets. We evaluate prediction performance using in-sample and out-of-sample tests. Empirical evidence from the US stock market suggests that among the variables we investigate, the default yield spread, inflation, and the term spread are useful in predicting bear markets. Further, we find that the default yield spread provides superior out-of-sample predictability for bear markets one to three months ahead, which suggests that the external finance premium has an informative content on the financial market
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