Papers by Russa Biswas
Large Language Models are Easily Confused: A Quantitative Metric, Security Implications and Typological Analysis (2025.findings-naacl)
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| Challenge: | Language Confusion is a phenomenon where Large Language Models (LLMs) generate text that is neither in the desired language, nor in a contextually appropriate one. |
| Approach: | They propose a metric to measure and quantify language confusion in Large Language Models (LLMs) they link language confusion to LLM security and find patterns in the case of multilingual embedding inversion attacks. |
| Outcome: | The proposed metric reveals language confusion across LLMs and link it to LLM security and embedding inversion attacks. |
LLMs Cannot (Yet) Match the Specificity and Simplicity of Online Communities in Long Form Question Answering (2024.findings-emnlp)
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| Challenge: | Recent years have positioned Large Language Models (LLMs) as powerful question answering (QA) tools, shifting users away from interacting in communities towards discourse with AI-driven conversational interfaces. |
| Approach: | They propose to use a QA preference dataset to fine-tune and align Large Language Models (LLMs) from more than 7.4 million submissions and 82 million comments from 2008 to 2022 in Reddit’s 15 largest finance communities. |
| Outcome: | The proposed framework improves on the social quality of the data, and the proposed framework is more accurate and more specific. |
InFact: Informativeness Alignment for Improved LLM Factuality (2025.findings-emnlp)
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| Challenge: | despite factual errors, LLMs tend to generate factual text that is factually correct but less informative than other, more informative choices. |
| Approach: | They propose an objective that prioritizes answers that are both correct and informative . |
| Outcome: | a new mechanism prioritizes correct and informative answers based on factual benchmarks . the proposed model improves both accuracy and factuality by maximizing the objective . |
Follow the Path: Reasoning over Knowledge Graph Paths to Improve Large Language Model Factuality (2026.findings-acl)
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| Challenge: | fs1 improves factuality of reasoning traces by sourcing them from large reasoning models and conditioning them on knowledge graph paths. |
| Approach: | They propose a method that improves the factuality of reasoning traces by sourcing them from large reasoning models and grounding them by conditioning on knowledge graph (KG) paths. |
| Outcome: | The proposed method outperforms instruction-tuned models on open-domain questions . it significantly improves model performance over more complex questions and numerical answer types compared to baselines. |