Papers by Kumar Tanmay

6 papers
Automatic Generation of Socratic Subquestions for Teaching Math Word Problems (2022.emnlp-main)

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Challenge: We hypothesize that questioning can enhance human performance and assist solvers .
Approach: They propose to use large language models to generate sequential questions for math word problem-solving . they propose to apply these models to a variety of math word problems .
Outcome: The proposed model improves the performance of a math word problem solver by generating more questions than other models.
Ethical Reasoning and Moral Value Alignment of LLMs Depend on the Language We Prompt Them in (2024.lrec-main)

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Challenge: Ethical reasoning is a crucial skill for Large Language Models (LLMs). However, moral values are not universal, but rather influenced by language and culture.
Approach: They extend the study of ethical reasoning of LLMs by (CITATION) to a multilingual setup using six languages: English, Spanish, Russian, Chinese, Hindi, and Swahili.
Outcome: The proposed model is based on a multilingual setup in English, Spanish, Russian, Chinese, Hindi, and Swahili.
Ethical Reasoning over Moral Alignment: A Case and Framework for In-Context Ethical Policies in LLMs (2023.findings-emnlp)

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Challenge: a paper by a team of researchers proposes that large language models should be morally aligned to ethical principles . a moral compass is a model that integrates moral dilemmas with moral principles pertaining to different foramlisms of normative ethics .
Approach: They propose to infuse generic ethical reasoning capabilities into large-scale models . they argue that LLMs should take a moral stance on value pluralism .
Outcome: a new ethical reasoning framework integrates moral dilemmas with moral principles . the framework is based on the results of a hypothetical case study on a large-scale model .
DUBLIN: Visual Document Understanding By Language-Image Network (2023.emnlp-industry)

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Challenge: DUBLIN is a pixel-based visual document understanding model that does not rely on OCR.
Approach: They propose a pixel-based visual document understanding model that does not rely on OCR.
Outcome: The proposed model performs on extractive tasks such as DocVQA, InfoVQA and AI2D, and strong performance on abstraction datasets such as VisualMRC and text captioning.
Do Moral Judgment and Reasoning Capability of LLMs Change with Language? A Study using the Multilingual Defining Issues Test (2024.eacl-long)

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Challenge: Existing studies have shown that moral judgment depends on the language in which the dilemma is presented.
Approach: They extend the work of beyond English, to 5 new languages (Chinese, Hindi, Russian, Spanish and Swahili) and probe three LLMs that show substantial multilingual text processing and generation abilities.
Outcome: The models show substantial multilingual text processing and generation abilities.
ReviewEval: An Evaluation Framework for AI-Generated Reviews (2025.findings-emnlp)

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Challenge: escalating volume of academic research necessitates innovative approaches to peer review . authors propose reviewEval, ReviewAgent and ReviewEval to improve on existing reviews .
Approach: They propose a framework for AI-generated reviews that measures alignment with human assessments . they propose 'reviewAgent' that iteratively optimizes its intermediate outputs and external improvement loops .
Outcome: The proposed framework improves actionable insights and analytical depth by 6.78% and 47.62% over baselines and expert reviews.

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