495 research outputs found
Information-Coupled Turbo Codes for LTE Systems
We propose a new class of information-coupled (IC) Turbo codes to improve the
transport block (TB) error rate performance for long-term evolution (LTE)
systems, while keeping the hybrid automatic repeat request protocol and the
Turbo decoder for each code block (CB) unchanged. In the proposed codes, every
two consecutive CBs in a TB are coupled together by sharing a few common
information bits. We propose a feed-forward and feed-back decoding scheme and a
windowed (WD) decoding scheme for decoding the whole TB by exploiting the
coupled information between CBs. Both decoding schemes achieve a considerable
signal-to-noise-ratio (SNR) gain compared to the LTE Turbo codes. We construct
the extrinsic information transfer (EXIT) functions for the LTE Turbo codes and
our proposed IC Turbo codes from the EXIT functions of underlying convolutional
codes. An SNR gain upper bound of our proposed codes over the LTE Turbo codes
is derived and calculated by the constructed EXIT charts. Numerical results
show that the proposed codes achieve an SNR gain of 0.25 dB to 0.72 dB for
various code parameters at a TB error rate level of , which complies
with the derived SNR gain upper bound.Comment: 13 pages, 12 figure
Optimization of Coastal Cruise Lines in China
The paper analyzes the current state of the Chinese cruise market and presents the idea of building a business model of coastal cruising. The cruise demand of middle-income families, which includes the desired travel days, ports of call, is surveyed. The data of the previous non-cruise travels and the data of future cruises of middle-income families are used to develop a model designed to identify the maximum passenger volume with minimum operating costs while taking cruise itineraries and schedules into account. A matrix coding genetic algorithm was designed to solve the model. The case study found that a voyage of 4.79 days results in equilibrium, that the annual demand is 200,840 passengers, and that the daily voyage cost is 0.843 million Yuan
InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning
We present a new financial domain large language model, InvestLM, tuned on
LLaMA-65B (Touvron et al., 2023), using a carefully curated instruction dataset
related to financial investment. Inspired by less-is-more-for-alignment (Zhou
et al., 2023), we manually curate a small yet diverse instruction dataset,
covering a wide range of financial related topics, from Chartered Financial
Analyst (CFA) exam questions to SEC filings to Stackexchange quantitative
finance discussions. InvestLM shows strong capabilities in understanding
financial text and provides helpful responses to investment related questions.
Financial experts, including hedge fund managers and research analysts, rate
InvestLM's response as comparable to those of state-of-the-art commercial
models (GPT-3.5, GPT-4 and Claude-2). Zero-shot evaluation on a set of
financial NLP benchmarks demonstrates strong generalizability. From a research
perspective, this work suggests that a high-quality domain specific LLM can be
tuned using a small set of carefully curated instructions on a well-trained
foundation model, which is consistent with the Superficial Alignment Hypothesis
(Zhou et al., 2023). From a practical perspective, this work develops a
state-of-the-art financial domain LLM with superior capability in understanding
financial texts and providing helpful investment advice, potentially enhancing
the work efficiency of financial professionals. We release the model parameters
to the research community.Comment: Link: https://github.com/AbaciNLP/InvestL
Specialist or Generalist? Instruction Tuning for Specific NLP Tasks
The potential of large language models (LLMs) to simultaneously perform a
wide range of natural language processing (NLP) tasks has been the subject of
extensive research. Although instruction tuning has proven to be a
data-efficient method for transforming LLMs into such generalist models, their
performance still lags behind specialist models trained exclusively for
specific tasks. In this paper, we investigate whether incorporating
broad-coverage generalist instruction tuning can contribute to building a
specialist model. We hypothesize that its efficacy depends on task specificity
and skill requirements. Our experiments assess four target tasks with distinct
coverage levels, revealing that integrating generalist instruction tuning
consistently enhances model performance when the task coverage is broad. The
effect is particularly pronounced when the amount of task-specific training
data is limited. Further investigation into three target tasks focusing on
different capabilities demonstrates that generalist instruction tuning improves
understanding and reasoning abilities. However, for tasks requiring factual
knowledge, generalist data containing hallucinatory information may negatively
affect the model's performance. Overall, our work provides a systematic guide
for developing specialist models with general instruction tuning. Our code and
other related resources can be found at
https://github.com/DavidFanzz/Generalist_or_Specialist.Comment: Accepted to EMNLP 202
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