Papers by Abhinav Anand
M-QALM: A Benchmark to Assess Clinical Reading Comprehension and Knowledge Recall in Large Language Models via Question Answering (2024.findings-acl)
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Anand Subramanian, Viktor Schlegel, Abhinav Ramesh Kashyap, Thanh-Tung Nguyen, Vijay Prakash Dwivedi, Stefan Winkler
| Challenge: | Existing studies on adapting large language models to perform a variety of tasks in high-stakes domains such as healthcare lack understanding of the extent and contributing factors that allow them to recall relevant knowledge and combine it with presented information. |
| Approach: | They propose to use multiple choice and abstractive question answering to investigate the extent and contributing factors that allow LLMs to recall relevant knowledge and combine it with presented information in the clinical and biomedical domain. |
| Outcome: | The proposed models perform better on 22 datasets in three generalist and three specialist biomedical sub-domains, and show that they can generalise to unseen sub- domains. |
Learning to Retrieve Engaging Follow-Up Queries (2023.findings-eacl)
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Christopher Richardson, Sudipta Kar, Anjishnu Kumar, Anand Ramachandran, Zeynab Raeesy, Omar Khan, Abhinav Sethy
| Challenge: | Open domain conversational agents can answer a wide range of targeted queries, but knowledge exploration is a lengthy task. |
| Approach: | They propose a retrieval based system for predicting the next questions that the user might have . they train ranking models on a dataset called the Follow-up Query Bank . |
| Outcome: | The proposed system can proactively assist users in knowledge exploration leading to a more engaging dialog. |
Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs (2025.naacl-srw)
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| Challenge: | Code-generating Large Language Models (LLMs) have become essential tools in modern software development, enhancing productivity and accelerating development. |
| Approach: | They propose to use Reinforcement Learning and Direct Preference Optimization to fine-tune code-generating Large Language Models (LLMs) by enhancing the training data with symbolic execution techniques. |
| Outcome: | The proposed model improves on the CodeRL benchmark and shows that it is more accurate and objective than the baseline model. |
A Critical Study of What Code-LLMs (Do Not) Learn (2024.findings-acl)
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| Challenge: | Large Language Models trained on code corpora have limitations such as suggesting codes with syntactic errors, variable misuse etc. |
| Approach: | They conduct a fine-grained analysis of attention maps and hidden representations of large-scale Large Language Models (cLLMs) trained on a large corpus of code and natural language -programming language pairs. |
| Outcome: | The proposed models encode relations among syntactic tokens and identifiers, but fail to encode relations between syntaktic token and identifier. |
CodeSSM: Towards State Space Models for Code Understanding (2025.emnlp-main)
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| Challenge: | Existing transformers have limitations, such as quadratic complexity and high inference costs. |
| Approach: | They propose a state space model that is trained on code corpora to assess its effectiveness. |
| Outcome: | The proposed model reduces memory usage by up to 64% compared to transformers at a context length of 2048. |