Papers by Saurabh Goyal
NESTFUL: A Benchmark for Evaluating LLMs on Nested Sequences of API Calls (2025.emnlp-main)
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Kinjal Basu, Ibrahim Abdelaziz, Kiran Kate, Mayank Agarwal, Maxwell Crouse, Yara Rizk, Kelsey Bradford, Asim Munawar, Sadhana Kumaravel, Saurabh Goyal, Xin Wang, Luis A. Lastras, Pavan Kapanipathi
| Challenge: | Existing benchmarks and datasets for tool calling have lagged behind . nested sequencing is a common problem in LLMs, but it is not enough to evaluate them. |
| Approach: | They propose a benchmark to evaluate LLMs on nested sequences of API calls, i.e. sequences where the output of one API call is passed as input to a subsequent call. |
| Outcome: | The proposed model achieves a full sequence match accuracy of 28% and a win-rate of 60% on nested sequences of API calls. |
CONTRASTE: Supervised Contrastive Pre-training With Aspect-based Prompts For Aspect Sentiment Triplet Extraction (2023.findings-emnlp)
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| Challenge: | Existing studies on Aspect Sentiment Triplet Extraction focus on developing more efficient techniques for the task, but our proposed approach can improve the downstream performance of multiple ABSA tasks simultaneously. |
| Approach: | They propose a novel approach that uses contrastive learning to enhance the ASTE performance by masked sentiments. |
| Outcome: | The proposed approach improves the performance of multiple ABSA tasks simultaneously. |
InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis (2024.naacl-short)
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| Challenge: | Experimental results on the Sem Eval 2014, 15, and 16 datasets demonstrate that InstructABSA outperforms the previous state-of-the-art (SOTA) approaches on Term Extraction (ATE), Sentiment Classification(ATSC) and Sentimence Pair Extraction(ASPE) subtasks. |
| Approach: | They introduce positive, negative, and neutral examples to each training sample, and instruction tune the model (Tk-Instruct) for ABSA subtasks. |
| Outcome: | The proposed model outperforms the state-of-the-art (SOTA) on Term Extraction (ATE), Sentiment Classification (ATSC) and Sentimence Pair Extractions (ASPE) subtasks. |