Papers by Akshay Chaturvedi
CNN for Text-Based Multiple Choice Question Answering (P18-2)
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| Challenge: | Existing models for text-based multiple choice question answering are based on a text. |
| Approach: | They propose a Convolutional Neural Network (CNN) model for text-based multiple choice question answering where questions are based on a particular article. |
| Outcome: | The proposed model outperforms several baseline models on the SciQ and TQA datasets. |
Limits for learning with language models (2023.starsem-1)
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| Challenge: | Recent studies show that large language models fail to capture important aspects of linguistic meaning . authors argue that LLMs cannot learn fundamental semantic properties defined in formal semantics . |
| Approach: | They propose a theoretical explanation for some of the observed failings of large language models . they show that LLMs cannot learn certain fundamental semantic properties . |
| Outcome: | The proposed model fails to learn semantic entailment and consistency as defined in formal semantics, the authors argue . their model fails on tasks that require engorgements and deep linguistic understanding, they argue - but not on universal quantification. |
Learning Semantic Structure through First-Order-Logic Translation (2024.findings-emnlp)
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| Challenge: | a recent study shows that transformer-based language models can confuse which predicates apply to which objects . a this is a crucial building block of semantic structure, but if an LM mixes up which objects have which property, it makes errors in reasoning . |
| Approach: | They propose to use transformer-based language models to learn predicate argument structure from simple sentences. |
| Outcome: | The proposed model can learn predicate argument structure from simple sentences. |
Nebula: A discourse aware Minecraft Builder (2024.findings-emnlp)
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| Challenge: | Recent work has shown that at least some context is needed to understand and carry out conversationally given instructions. |
| Approach: | They propose to incorporate prior discourse and nonlinguistic contexts of a conversation situated in a nonlinguistic environment into an LLM model to improve the "language to action" component of collaborative tasks. |
| Outcome: | The proposed model doubles the baseline on the task of Jayannavar et al. (2020) and can construct shapes and understand location descriptions using a synthetic dataset. |
sudoLLM: On Multi-role Alignment of Language Models (2025.findings-emnlp)
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| Challenge: | a framework that allows users to control access rights has not been extensively studied in the large language model realm. |
| Approach: | They propose a framework that allows users to control access rights in a multi-role manner. |
| Outcome: | The proposed framework improves alignment, generalization and resistance to prefix-based jailbreaking attacks. |
Llamipa: An Incremental Discourse Parser (2024.findings-emnlp)
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| Challenge: | Discourse parsing is a task of predicting relationships between utterances and their semantic content . lack of surface cues in discourse graphs forces parsers to rely on deep, semantic information . a large language model (LLM) can significantly improve discourse parser performance . |
| Approach: | They propose a large language model (LLM) that leverages discourse context to parse a discourse . this model provides local, context-sensitive representations of discourse units . |
| Outcome: | The proposed model can provide local, context-sensitive representations of discourse units . it can process discourse data incrementally, which is essential for later use of discourse information . |