38 research outputs found
LARCH: Large Language Model-based Automatic Readme Creation with Heuristics
Writing a readme is a crucial aspect of software development as it plays a
vital role in managing and reusing program code. Though it is a pain point for
many developers, automatically creating one remains a challenge even with the
recent advancements in large language models (LLMs), because it requires
generating an abstract description from thousands of lines of code. In this
demo paper, we show that LLMs are capable of generating a coherent and
factually correct readmes if we can identify a code fragment that is
representative of the repository. Building upon this finding, we developed
LARCH (LLM-based Automatic Readme Creation with Heuristics) which leverages
representative code identification with heuristics and weak supervision.
Through human and automated evaluations, we illustrate that LARCH can generate
coherent and factually correct readmes in the majority of cases, outperforming
a baseline that does not rely on representative code identification. We have
made LARCH open-source and provided a cross-platform Visual Studio Code
interface and command-line interface, accessible at
https://github.com/hitachi-nlp/larch. A demo video showcasing LARCH's
capabilities is available at https://youtu.be/ZUKkh5ED-O4.Comment: This is a pre-print of a paper accepted at CIKM'23 Demo. Refer to the
DOI URL for the original publicatio
Text Retrieval with Multi-Stage Re-Ranking Models
The text retrieval is the task of retrieving similar documents to a search
query, and it is important to improve retrieval accuracy while maintaining a
certain level of retrieval speed. Existing studies have reported accuracy
improvements using language models, but many of these do not take into account
the reduction in search speed that comes with increased performance. In this
study, we propose three-stage re-ranking model using model ensembles or larger
language models to improve search accuracy while minimizing the search delay.
We ranked the documents by BM25 and language models, and then re-ranks by a
model ensemble or a larger language model for documents with high similarity to
the query. In our experiments, we train the MiniLM language model on the
MS-MARCO dataset and evaluate it in a zero-shot setting. Our proposed method
achieves higher retrieval accuracy while reducing the retrieval speed decay
Learning Deductive Reasoning from Synthetic Corpus based on Formal Logic
We study a synthetic corpus-based approach for language models (LMs) to
acquire logical deductive reasoning ability. The previous studies generated
deduction examples using specific sets of deduction rules. However, these rules
were limited or otherwise arbitrary. This can limit the generalizability of
acquired deductive reasoning ability. We rethink this and adopt a well-grounded
set of deduction rules based on formal logic theory, which can derive any other
deduction rules when combined in a multistep way. We empirically verify that
LMs trained on the proposed corpora, which we name
(ormal ogic eduction), acquire more
generalizable deductive reasoning ability. Furthermore, we identify the aspects
of deductive reasoning ability on which deduction corpora can enhance LMs and
those on which they cannot. Finally, on the basis of these results, we discuss
the future directions for applying deduction corpora or other approaches for
each aspect. We release the code, data, and models
Controlling keywords and their positions in text generation
One of the challenges in text generation is to control generation as intended
by a user. Previous studies have proposed to specify the keywords that should
be included in the generated text. However, this is insufficient to generate
text which reflect the user intent. For example, placing the important keyword
beginning of the text would helps attract the reader's attention, but existing
methods do not enable such flexible control. In this paper, we tackle a novel
task of controlling not only keywords but also the position of each keyword in
the text generation. To this end, we show that a method using special tokens
can control the relative position of keywords. Experimental results on
summarization and story generation tasks show that the proposed method can
control keywords and their positions. We also demonstrate that controlling the
keyword positions can generate summary texts that are closer to the user's
intent than baseline. We release our code
Theory of optical transitions in graphene nanoribbons
Matrix elements of electron-light interactions for armchair and zigzag
graphene nanoribbons are constructed analytically using a tight-binding model.
The changes in wavenumber () and pseudospin are the necessary
elements if we are to understand the optical selection rule. It is shown that
an incident light with a specific polarization and energy, induces an indirect
transition (), which results in a characteristic peak in
absorption spectra. Such a peak provides evidence that the electron standing
wave is formed by multiple reflections at both edges of a ribbon. It is also
suggested that the absorption of low-energy light is sensitive to the position
of the Fermi energy, direction of light polarization, and irregularities in the
edge. The effect of depolarization on the absorption peak is briefly discussed.Comment: 11 pages, 7 figure
DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model
Structural equation models and Bayesian networks have been widely used to analyze causal relations between continuous variables. In such frameworks, linear acyclic models are typically used to model the data-generating process of variables. Recently, it was shown that use of non-Gaussianity identifies the full structure of a linear acyclic model, i.e., a causal ordering of variables and their connection strengths, without using any prior knowledge on the network structure, which is not the case with conventional methods. However, existing estimation methods are based on iterative search algorithms and may not converge to a correct solution in a finite number of steps. In this paper, we propose a new direct method to estimate a causal ordering and connection strengths based on non-Gaussianity. In contrast to the previous methods, our algorithm requires no algorithmic parameters and is guaranteed to converge to the right solution within a small fixed number of steps if the data strictly follows the model