Papers by Ambuj Singh
XplainLLM: A Knowledge-Augmented Dataset for Reliable Grounded Explanations in LLMs (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have achieved remarkable success in natural language tasks, yet understanding their reasoning processes remains a significant challenge. |
| Approach: | They propose a dataset that includes 24204 instances where each instance interprets the LLM’s reasoning behavior using knowledge graphs and graph attention networks (GAT). |
| Outcome: | The proposed explanation framework reduces hallucinations and improves grounded explanation generation in large language models. |
GraphEval36K: Benchmarking Coding and Reasoning Capabilities of Large Language Models on Graph Datasets (2025.findings-naacl)
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| Challenge: | Large language models (LLMs) have demonstrated significant capabilities in processing and understanding text data. |
| Approach: | They propose a structure-based instruction-based method to enhance LLM performance on complex graph tasks. |
| Outcome: | The proposed framework outperforms open-source models on graph problem-solving, but the gap is narrowing. |
Are LLMs Truly Graph-Savvy? A Comprehensive Evaluation of Graph Generation (2025.acl-srw)
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| Challenge: | Large language models have demonstrated impressive capabilities across diverse tasks . however, their ability to generate valid graph structures remains underexplored . |
| Approach: | They evaluate large language models on five specialized graph generation tasks . they also test the models using 3 different prompt types: direct, iterative feedback, and program-augmented. |
| Outcome: | The proposed models solve twice as many tasks as general-purpose models, compared to their general-usage peers. |