35.3544, Calls: Large Language Models for the History, Philosophy, and Sociology of Science

[The LINGUIST List]

LINGUIST List: Vol-35-3544. Mon Dec 16 2024. ISSN: 1069 - 4875.

Subject: 35.3544, Calls: Large Language Models for the History, Philosophy, and Sociology of Science

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Date: 13-Dec-2024
From: Arno Simons [arno.simons at tu-berlin.de]
Subject: Large Language Models for the History, Philosophy, and Sociology of Science


Full Title: Large Language Models for the History, Philosophy, and
Sociology of Science
Short Title: LLMs for HPSS

Date: 02-Apr-2025 - 04-Apr-2025
Location: Berlin, Germany
Contact Person: Arno Simons
Meeting Email: arno.simons at tu-berlin.de
Web Site: https://www.tu.berlin/hps-mod-sci/workshop-llms-for-hpss

Linguistic Field(s): Applied Linguistics; Computational Linguistics;
Historical Linguistics; Semantics; Text/Corpus Linguistics

Call Deadline: 31-Dec-2024

Meeting Description:

Computational approaches to the history of science are in the process
of establishing themselves among the standard repertoire of tools in
the field and we have seen remarkable successes in their application
already. Subfields of sociology of science have focused, since long,
on quantitative methods such as bibliometrics and scientometrics. More
recently, philosophy of science has experienced a shift towards
allowing more empirical approaches including large-scale algorithmic
analyses of scientific or methodological concepts. Computational tools
can not only help reduce the workload in traditional research in these
fields but, more importantly, also open up new avenues which to
explore would otherwise be hopeless.

Analyses of co-occurrences and word frequencies as well as more
advanced techniques such as topic modeling have helped go beyond
identifying only structural features of scientific activities and
began scratching the surface of semantics. However, a deeper
understanding of scientific concepts, the structure of scientific
arguments, and the process of knowledge transformation and spread have
remained formidable challenges for computational approaches in the
mentioned fields.

With the advent of LLMs this might change now. Natural language
processing and machine learning have made a spectacular leap forward
in their attempt to capture and analyze meaning and grammatical
structures of texts. This promises that LLMs can help HPSS researchers
meet the aforementioned challenges. However—besides general issues
such as opacity, bias and interpretability—the use of LLMs for HPSS is
likely to face unique obstacles arising from the specialized nature of
scientific language as well as the specific perspectives and
objectives of HPSS. It will be the main goal of this workshop to see
how, given these obstacles, the most recent advances in LLM
development can help overcome long-standing challenges in HPSS.

Accordingly, the workshop will address two key themes, with the goal
of synthesizing them over the course of the event. On one hand,
contributions should articulate the specific needs and desiderata of
HPSS researchers—what they hope LLMs can achieve for their work. On
the other hand, the current state of LLM development should be
critically examined to determine to what extent these research goals
are becoming attainable. Ideally, contributions will address both
these objectives, though submissions focused on only one of them are
also welcome.

We particularly encourage contributions that focus on:
 • Use cases that demonstrate how LLMs can help resolve current issues
in HPSS
 • Examples of how LLMs allow researchers to ask and answer new types
of questions in HPSS
 • How new types of sources and data, made analyzable through LLMs,
contribute to novel insights in HPSS research

We look for contributions that help resolve questions like these:
 • How can LLMs help gain new perspectives on long-standing problems
in HPSS such as determining the relevant contexts of knowledge claims,
the dynamics of scientific controversies, problems of
incommensurability, and generalizability of case studies?
 • How can LLMs handle the specialized language of scientific texts,
including technical jargon, citations, and mathematical formulas?
 • How can LLMs bridge the gap between qualitative and computational
methods and help overcome their limitations?
 • How can LLMs be integrated into existing theoretical and
methodological frameworks in HPSS, or how should these frameworks
evolve to accommodate LLM-based analysis?
 • How can we evaluate the validity of results generated by LLMs,
given their opacity?
 • How can LLMs account for the temporal development of scientific
language and knowledge over time?

2nd Call for Papers:

This is a friendly reminder that the deadline for submissions to our
workshop on "Large Language Models (LLMs) in the History, Philosophy,
and Sociology of Science (HPSS)" is fast approaching!

The deadline is December 31, 2024—just two weeks away. To contribute a
talk, please send an abstract of your planned contribution of 300-600
words by e-mail to arno.simons at tu-berlin.de. We encourage every
contributor to present on site and to participate in the whole
workshop program. In exceptional cases, we will offer the possibility
to present remotely.

The workshop will focus on exploring use cases and proposals for how,
and to what extent, LLMs might help overcome long-standing challenges
in studies of how science works. The event will take place from April
2–4, 2025, at Technische Universität Berlin, Germany.

We look for contributions that help resolve questions like these:

- How can LLMs help gain new perspectives on long-standing problems in
HPSS such as determining the relevant contexts of knowledge claims,
the dynamics of scientific controversies, problems of
incommensurability, and generalizability of case studies?
- How can LLMs handle the specialized language of scientific texts,
including technical jargon, citations, and mathematical formulas?
- How can LLMs bridge the gap between qualitative and computational
methods and help overcome their limitations?
- How can LLMs be integrated into existing theoretical and
methodological frameworks in HPSS, or how should these frameworks
evolve to accommodate LLM-based analysis?
- How can we evaluate the validity of results generated by LLMs, given
their opacity?
- How can LLMs account for the temporal development of scientific
language and knowledge over time?

For more details about the workshop and the CFP, please visit:
https://www.tu.berlin/hps-mod-sci/workshop-llms-for-hpss

We look forward to your contributions!



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